CN113037877A - Optimization method for time-space data and resource scheduling under cloud edge architecture - Google Patents

Optimization method for time-space data and resource scheduling under cloud edge architecture Download PDF

Info

Publication number
CN113037877A
CN113037877A CN202110576571.XA CN202110576571A CN113037877A CN 113037877 A CN113037877 A CN 113037877A CN 202110576571 A CN202110576571 A CN 202110576571A CN 113037877 A CN113037877 A CN 113037877A
Authority
CN
China
Prior art keywords
cloud
scheduling
resource
service quality
edge
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110576571.XA
Other languages
Chinese (zh)
Other versions
CN113037877B (en
Inventor
李晓明
王伟玺
吕智涵
刘铭崴
谢林甫
罗坚
郑晔
郭仁忠
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen University
Original Assignee
Shenzhen University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen University filed Critical Shenzhen University
Priority to CN202110576571.XA priority Critical patent/CN113037877B/en
Publication of CN113037877A publication Critical patent/CN113037877A/en
Application granted granted Critical
Publication of CN113037877B publication Critical patent/CN113037877B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • H04L67/61—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources taking into account QoS or priority requirements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5003—Managing SLA; Interaction between SLA and QoS
    • H04L41/5009—Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides an optimization method for scheduling spatio-temporal data and resources under a cloud edge architecture, which comprises the following steps: under a cloud edge architecture, performing log recording on the execution process of each time-space data scheduling task and converging the log recording to a cloud center; the cloud center carries out overall and local combined quantitative evaluation on the cloud edge resource service quality to obtain cloud edge resource service quality evaluation information; performing multi-objective optimization processing on the service chain service unit combination according to the cloud edge resource service quality evaluation information; establishing a pre-scheduling task at the cloud center, the edge server and the user terminal, and scheduling data to be scheduled in the next step in advance; and the cloud center optimizes the cloud edge resource allocation according to the pre-scheduling task and the real-time scheduling task sent by the user, and performs matching optimization processing of the cloud edge tasks and the resources in a multi-to-multi application scene. The invention enables resources under a cloud edge-end hybrid architecture to realize efficient scheduling of large-scale space-time data.

Description

Optimization method for time-space data and resource scheduling under cloud edge architecture
Technical Field
The invention relates to the technical field of geographic information systems, in particular to an optimization method for scheduling spatio-temporal data and resources under a cloud edge architecture.
Background
In a geographic information system, resources such as storage, calculation and drawing in a cloud environment are integrated into a huge virtual resource pool, a plurality of calculation nodes can be instantiated in the resource pool according to the requirement on the resources, and tasks such as data I/O, analysis and calculation, scene drawing and the like in the process of space-time data visualization are distributed on different calculation nodes in a service or application mode to run. Resource scheduling in a cloud environment mainly studies how to allocate tasks with different resource requirements to each computing node and dynamic expansion of the computing nodes, so that the load balancing degree is improved and the task execution efficiency is maximized on the premise of meeting the user service quality. The cloud computing has the advantage of 'logically centralized resources', but if the cloud computing is completely relied on the 'heavyweight cloud computing' which is far away from the user terminal, a bottleneck effect is caused. The edge computing equipment is deployed at a position closer to the user terminal, and a cloud edge-end hybrid architecture from cloud to edge to terminal is constructed, so that network delay can be effectively reduced, and faster response is provided, and therefore, the large-scale space-time data is inevitably developed in a cloud center storage computing environment, an edge server computing environment and an edge-end application environment applied to multiple terminals.
However, at present, for the application requirements of large-scale spatio-temporal data with high diversity, high concurrency and high real-time, the resources under the cloud edge hybrid architecture cannot realize efficient scheduling of the large-scale spatio-temporal data.
Therefore, the prior art has defects and needs to be improved and developed.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide an optimization method for scheduling spatio-temporal data and resources under a cloud edge architecture, aiming at solving the problem that in the prior art, for the application requirements of large-scale spatio-temporal data with high diversity, high concurrency and high real-time, resources under a cloud edge hybrid architecture cannot realize efficient scheduling of large-scale spatio-temporal data.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a method for optimizing spatio-temporal data and resource scheduling under a cloud edge architecture comprises the following steps:
under a cloud edge terminal architecture, carrying out log recording on the execution process of each time-space data scheduling task in a cloud center, an edge server and a user terminal, and regularly converging the log records on the edge server and the user terminal to the cloud center;
the cloud center analyzes the resource service quality index and the network service quality index in the log record, and performs overall and local combined quantitative evaluation on the cloud edge resource service quality to obtain cloud edge resource service quality evaluation information;
evaluating the service quality of each service unit of the cloud edge terminal according to the cloud edge resource service quality evaluation information, and performing multi-objective optimization processing on service chain service unit combinations;
the cloud center establishes a pre-scheduling task at the cloud center, the edge server and the user terminal according to the cloud edge resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud edge terminal in the log record, and schedules data to be scheduled next in advance;
and the cloud center optimizes the cloud edge resource allocation according to the pre-scheduling task and the real-time scheduling task sent by the user, and performs matching optimization processing of the cloud edge task and the resource in a 'many-to-many' application scene according to an optimization result and the cloud edge resource service quality evaluation information.
Further, the cloud center analyzes the resource service quality index and the network service quality index in the log record, and performs quantitative evaluation of global and local combination on the cloud edge resource service quality to obtain cloud edge resource service quality evaluation information, including:
the cloud center establishes a correlation matrix according to the relation between the resource service quality index and the network service quality index in the log record, and determines a comprehensive evaluation value of the storage and plotting service by using a principal component analysis method;
performing correlation analysis on the real-time data and the historical data corresponding to the resource service quality index and the network service quality index, and performing overall and local combined quantitative accurate evaluation on the cloud side storage and mapping resource service quality;
and obtaining the service quality evaluation information of the cloud-edge resources according to the comprehensive evaluation value and the quantitative accurate evaluation.
Further, before the cloud center establishes a correlation matrix according to a relationship between the resource service quality index and the network service quality index in the log record and determines a comprehensive evaluation value of the storage and rendering service by using a principal component analysis method, the method further includes:
establishing an index system for evaluating the cloud side storage and drawing service quality according to the log record;
the index system comprises a resource service quality index and a network service quality index.
Further, the resource service quality indicator includes: integrating service response time, service throughput, service cost, and service reliability; the network service quality indicators include: network throughput, service access success rate, task execution success rate, and network transmission delay.
Further, according to the cloud edge resource service quality evaluation information, the service quality of each service unit of the cloud edge terminal is evaluated, and multi-objective optimization processing is performed on service chain service unit combinations, and the method comprises the following steps:
dynamically evaluating the service quality of each service unit of the cloud side according to the cloud side resource service quality evaluation information to obtain dynamic evaluation information;
according to the dynamic evaluation information, performing dynamic combination and optimization adjustment on each service unit by using a self-adaptive genetic algorithm to obtain dynamic optimization information of a service chain;
and performing multi-objective optimization processing on the service chain service unit combination according to the service chain dynamic optimization information.
Further, the cloud center establishes a pre-scheduling task at the cloud center, the edge server and the user terminal according to the cloud-edge resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud edge in the log record, and schedules data to be scheduled next in advance, including:
the cloud center establishes tracking and loading of a pre-scheduling object in the cloud center, the edge server and the user terminal according to the cloud edge resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud edge terminal in the log record;
constructing pre-scheduling graph structures of different space-time calculations, analyses or interactions according to a pre-scheduling mechanism of a pre-constructed variable template to obtain pre-scheduling tasks;
and optimizing the pre-scheduling task according to the change of the real-time scheduling task, and scheduling the data to be scheduled next in advance.
Further, the cloud center optimizes the cloud edge resource allocation according to the pre-scheduling task and the real-time scheduling task sent by the user, and performs matching optimization processing of the cloud edge task and the resource in a many-to-many application scene according to an optimization result and the cloud edge resource service quality evaluation information, wherein the matching optimization processing comprises the following steps:
the cloud center optimizes the resource allocation of the cloud side end according to the pre-scheduling task and a real-time scheduling task sent by a user to obtain scheduling task optimization adjustment information;
and performing matching optimization processing of cloud side tasks and resources in a many-to-many application scene according to the scheduling task optimization adjustment information and the cloud side resource service quality evaluation information and a pre-established bilateral matching optimization model of the cloud side task resources.
Further, the scheduling the data to be scheduled next in advance includes:
in the pre-scheduling of the cloud center, scheduling the spatio-temporal data to be scheduled in the next step from a hard disk to a memory in advance;
in the pre-scheduling of the edge server, transmitting the spatio-temporal data to be scheduled next step from the cloud center to the edge server in advance;
in the pre-scheduling of the user terminal, the space-time data needed by the user terminal in the next step is scheduled from the edge server to the memory of the user terminal in advance.
The invention discloses a cloud center, comprising: the optimization program of spatiotemporal data and resource scheduling under the cloud edge architecture is stored on the memory and can run on the processor, and when being executed by the processor, the optimization program of spatiotemporal data and resource scheduling under the cloud edge architecture realizes the steps of the optimization method of spatiotemporal data and resource scheduling under the cloud edge architecture according to any one of claims 1 to 8.
The invention discloses a computer-readable storage medium, which stores a computer program, wherein the computer program can be executed to realize the steps of the optimization method of spatio-temporal data and resource scheduling under the cloud edge architecture according to any one of claims 1 to 8.
The invention provides an optimization method for scheduling spatio-temporal data and resources under a cloud edge architecture, which comprises the following steps: under a cloud edge terminal architecture, carrying out log recording on the execution process of each time-space data scheduling task in a cloud center, an edge server and a user terminal, and regularly converging the log records on the edge server and the user terminal to the cloud center; the cloud center analyzes the resource service quality index and the network service quality index in the log record, and performs overall and local combined quantitative evaluation on the cloud edge resource service quality to obtain cloud edge resource service quality evaluation information; evaluating the service quality of each service unit of the cloud edge terminal according to the cloud edge resource service quality evaluation information, and performing multi-objective optimization processing on service chain service unit combinations; the cloud center establishes a pre-scheduling task at the cloud center, the edge server and the user terminal according to the cloud edge resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud edge terminal in the log record, and schedules data to be scheduled next in advance; and the cloud center optimizes the cloud edge resource allocation according to the pre-scheduling task and the real-time scheduling task sent by the user, and performs matching optimization processing of the cloud edge task and the resource in a 'many-to-many' application scene according to an optimization result and the cloud edge resource service quality evaluation information. The method analyzes the resource service quality index and the network service quality index in the log record, performs quantitative evaluation of global and local combination on the cloud edge resource service quality, performs multi-objective optimization processing on the service chain service unit combination, optimizes the cloud edge resource allocation according to the pre-scheduling task and the real-time scheduling task sent by the user, performs matching optimization processing of the cloud edge task and the resource in a multi-to-multi application scene, realizes optimization of time-space data and resource scheduling under a cloud edge framework, and enables the resource to realize efficient scheduling of large-scale time-space data under a cloud edge mixed framework.
Drawings
FIG. 1 is a flowchart illustrating an optimization method for spatio-temporal data and resource scheduling in cloud edge architecture according to a preferred embodiment of the present invention.
Fig. 2 is a flowchart illustrating the step S200 of the method for optimizing spatio-temporal data and resource scheduling in the cloud edge architecture according to the present invention.
Fig. 3 is a flowchart illustrating the step S300 of the method for optimizing spatio-temporal data and resource scheduling in the cloud edge architecture according to the present invention.
Fig. 4 is a flowchart illustrating the step S400 of the method for optimizing spatio-temporal data and resource scheduling in the cloud edge architecture according to the present invention.
Fig. 5 is a flowchart illustrating the step S500 of the method for optimizing spatio-temporal data and resource scheduling in the cloud edge architecture according to the present invention.
FIG. 6 is a functional block diagram of a preferred embodiment of the cloud center of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the application requirements of large-scale space-time data on diversity, high concurrency and high real-time, how to optimize the cooperative scheduling algorithm of resources under the cloud edge-end mixed architecture is very important to realize the efficient scheduling of the large-scale space-time data through the cooperative scheduling of stored and plotted resources.
The invention provides a cloud edge resource adaptive scheduling optimization method based on service quality evaluation, aiming at the characteristics of randomness, dynamics, diversity, uncertainty and the like of scheduling tasks under a cloud edge hybrid architecture. Firstly, researching a storage and computation plotting resource service quality evaluation model based on real-time and historical data association, and carrying out overall and local combined quantitative accurate evaluation on the cloud side storage and computation plotting resource service quality by performing association analysis on the real-time data and the historical data; then, performing scheduling service chain dynamic optimization based on a service quality evaluation model, performing dynamic evaluation on service chains and service unit service quality based on the service quality evaluation model, and realizing self-adaptive optimization selection of optimal service combinations of cloud edge scheduling service chains based on a service chain service unit combination multi-objective optimization method of an improved self-adaptive genetic algorithm; then, a cloud edge scheduling task and resource allocation adaptive optimization mechanism facing to high-concurrency diversified tasks is established, adaptive optimization is carried out on the scheduling task through an integrated pre-scheduling mechanism, and the adaptive optimization mechanism for matching the cloud edge scheduling task and the resources is established based on a task-resource matching decision optimization method of cloud edge cooperation.
Referring to fig. 1, fig. 1 is a flowchart illustrating an optimization method for spatio-temporal data and resource scheduling under a cloud edge architecture according to the present invention. As shown in fig. 1, the method for optimizing spatio-temporal data and resource scheduling under a cloud edge architecture according to the embodiment of the present invention includes the following steps:
s100, under a cloud edge terminal architecture, performing log recording on the execution process of each time-space data scheduling task in a cloud center, an edge server and a user terminal, and regularly converging the log records on the edge server and the user terminal to the cloud center.
Specifically, the cloud side end refers to a cloud center, an edge server and a user terminal. The invention carries out comprehensive evaluation on the service quality of the storage and drawing resources of the cloud edge, mainly utilizes the cloud center to collect data related to the service quality of the cloud center and data related to the service quality on an edge server and a terminal, and adopts a unified cloud edge quality evaluation index system to carry out comprehensive evaluation. A cloud side storage and computation resource service quality evaluation model, a scheduling service chain adaptive optimization mechanism, a scheduling task adaptive optimization mechanism and a cloud side task and resource bilateral matching optimization model are established by the cloud center, and then the cloud center, the edge server and the terminal are efficiently scheduled by the models and the mechanisms.
The step S100 is followed by: s200, the cloud center analyzes the resource service quality index and the network service quality index in the log record, and performs overall and local combined quantitative evaluation on the cloud edge resource service quality to obtain cloud edge resource service quality evaluation information.
In an implementation manner, referring to fig. 2, the step S200 specifically includes:
s220, the cloud center establishes a correlation matrix according to the relation between the resource service quality index and the network service quality index in the log record, and determines a comprehensive evaluation value of the storage and plotting service by using a principal component analysis method;
s230, performing correlation analysis on the real-time data and the historical data corresponding to the resource service quality index and the network service quality index, and performing overall and local combined quantitative accurate evaluation on the cloud side storage and mapping resource service quality;
and S240, obtaining the service quality evaluation information of the cloud edge resource according to the comprehensive evaluation value and the quantitative accurate evaluation.
Specifically, quantitative evaluation is carried out on the cloud side deposit and drawing service quality, a correlation matrix is established according to the relation between indexes in an index system, then a principal component analysis method is used for determining the comprehensive evaluation value of the deposit and drawing service, the complexity of the comprehensive service quality evaluation of the cloud side deposit and drawing resource is reduced, and the quantitative index calculation of the service quality of each service unit in a service chain is realized.
Then, the real-time data and the historical data related to the scheduling task are subjected to correlation analysis, the cloud side deposit and draw resource service quality is subjected to overall and local combined quantitative accurate evaluation by combining with an index system for cloud side deposit and draw service quality evaluation, and overall reliability of the service quality evaluation result is also ensured while reliability of the local service quality evaluation result is ensured by associating the real-time data with the historical data for overall consideration. The global quantitative evaluation is the analysis and evaluation of the total data by combining all data of historical data and real-time data to obtain a global index; the local quantitative evaluation mainly only carries out relevant analysis and evaluation on the real-time data to obtain local evaluation; the global and local evaluation results are combined, mainly a weighted quantitative comprehensive value is carried out, and meanwhile, the global evaluation index and the local evaluation index are considered.
Specifically, the real-time data refers to data obtained in real time, and the historical data refers to vertically accumulated data. The data are index data related to an index system of quality evaluation, the value of the latest acquired index can be regarded as real-time data, and the data of all previous indexes can be called historical data. The correlation analysis of the real-time data and the historical data mainly comprises the steps of mining the rules and the trends of the historical data, further correcting the rules and the trends according to the real-time data, and continuously correcting the real-time data.
In a further implementation manner, the step S220 further includes, before the step S: and S210, establishing an index system for evaluating the cloud side storage and computation service quality according to the log records. The index system comprises a resource service quality index and a network service quality index.
The establishment of a Quality of Service (QoS) evaluation model of the storage and drawing resources can quantitatively evaluate the QoS conditions of the storage, calculation and drawing resources of the cloud side end, thereby providing a quantitative reference basis for the dynamic optimization of the scheduling Service chain.
That is, firstly, an index system for evaluating the service quality stored and calculated by the cloud side is established, and resource service quality indexes such as service response time, service throughput, service cost and service reliability, and network service quality indexes such as network throughput, service access success rate, task execution success rate and network transmission delay are integrated. The resource service quality index comprises: integrating service response time, service throughput, service cost, and service reliability; the network service quality indicators include: network throughput, service access success rate, task execution success rate, and network transmission delay. The resource service quality index is respectively obtained at the cloud center, the edge server and the user terminal; the network service quality index mainly comprises the throughput of a network from a cloud center to an edge server, a network from the edge server to a terminal and the like.
The step S200 is followed by: s300, evaluating the service quality of each service unit of the cloud edge terminal according to the cloud edge resource service quality evaluation information, and performing multi-objective optimization processing on service chain service unit combination. That is to say, according to indexes such as performance, load capacity and stability of each service unit, the cloud center evaluates the service quality of each service unit of the cloud edge based on service quality evaluation, performs multi-objective optimization on service chain service unit combinations, and realizes optimal selection of the optimal service combination of the cloud edge scheduling service chain.
In a further implementation manner, referring to fig. 3, the step S300 specifically includes:
s310, dynamically evaluating the service quality of each service unit of the cloud side terminal according to the cloud side resource service quality evaluation information to obtain dynamic evaluation information;
s320, according to the dynamic evaluation information, performing dynamic combination and optimization adjustment on each service unit by using a self-adaptive genetic algorithm to obtain dynamic optimization information of a service chain;
and S330, performing multi-objective optimization processing on the service chain service unit combination according to the service chain dynamic optimization information.
That is to say, the scheduling service chain is dynamically optimized in real time, and the service unit combination of the scheduling service chain is adaptively optimized and selected, so that guarantee is provided for efficient execution of the scheduling task. Each service unit is used for serving storage, calculation and drawing resources of the user terminal and the edge server, and the minimum unit of serving is the service unit.
Specifically, firstly, according to indexes (namely indexes in a service quality evaluation model) such as performance, loading capacity and stability of each service unit stored by the cloud center and the edge server, the service quality of each service unit in the service chain is dynamically evaluated based on the service quality evaluation model, so that the operation condition of each service unit in the service chain can be accurately grasped.
Then, based on the service quality evaluation of the service units of the scheduling service chain, the service units of the scheduling chain are dynamically combined and optimally adjusted through a service chain service unit combination method based on an improved adaptive genetic algorithm, so that scheduling tasks such as space-time data scheduling, computational analysis, interactive computation, drawing and the like can all allocate the optimal service combination, and the adaptive dynamic allocation of the scheduling tasks of the cloud side memory-drawing resources is realized. The adaptive genetic algorithm is a process of immediately producing a group of next generation seeds according to sampling data, substituting the next generation seeds into an optimization objective function, and automatically adjusting parameters according to an optimization function result.
Finally, after the dynamic optimization of the scheduling service chain is completed, the service quality of the service unit can be further improved through the dynamic optimization of the scheduling service chain, the self-adaptive matching of the service quality evaluation model and the dynamic optimization of the service chain is realized, and a scheduling service chain self-adaptive optimization mechanism based on the service quality evaluation model is established, so that the quick response of multi-scheduling tasks of time-space data scheduling, computational analysis, drawing and the like is more effectively supported.
The step S300 is followed by: s400, the cloud center establishes a pre-scheduling task at the cloud center, the edge server and the user terminal according to the cloud side resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud side end in the log record, and schedules data to be scheduled next in advance.
In an implementation manner, referring to fig. 4, the step S400 specifically includes:
s410, the cloud center establishes tracking and loading of a pre-scheduling object in the cloud center, the edge server and the user terminal according to the cloud side resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud side end in the log record;
s420, constructing different pre-scheduling graph structures for space-time calculation, analysis or interaction according to a pre-scheduling mechanism of a pre-constructed variable template to obtain a pre-scheduling task;
s430, optimizing the pre-scheduling task according to the change of the real-time scheduling task, and scheduling data to be scheduled in the next step in advance.
Specifically, the cloud center establishes a pre-scheduling task in the cloud center, the edge server and the user terminal according to the cloud edge resource service quality evaluation condition and in combination with various resource utilization and idle conditions of the cloud edge, and schedules data which may need to be scheduled in the next step in advance.
Due to the fact that space sensing data (such as geographic phenomena and geographic processes) have the characteristics of burstiness, unstructured performance and non-homogeneity, space-time data have the characteristics of uneven distribution, different densities and strong randomness, the problem of large granularity difference exists in the aspects of data expression, data storage, special application and the like, and efficient scheduling is difficult to guarantee. The scheduling of massive space-time data is realized by adopting a strategy of organically combining scheduling and pre-scheduling, and the difficulty is mainly how to select objects which can be scheduled in advance from a massive real-time GIS database.
The scheduling object is calculated according to the user requirement and needs to be scheduled; the pre-scheduling object is obtained by pre-judging the scheduling object required by the user to obtain the object which is possibly required to be scheduled by the future user, so that the scheduling can be performed in advance. The scheduling is carried out at the cloud side end according to the hierarchy of the far and near parts from the near part to the edge part and the cloud part from the near part to the far part, and each scheduling can be further divided into memory scheduling and disk scheduling.
And scheduling a task priority strategy, wherein a priority function is used for comprehensively considering influence factors, and the transaction priority factors comprise: deadlines, cumulative service volumes, required service volumes, age, slack, and the like; and simultaneously, carrying out correlation analysis on the priority function influence factors and influence factors (including instantaneity, access frequency, permanence and criticality) of the spatio-temporal data memory and external memory placement strategy by utilizing spatio-temporal semantic relations. And finally, giving specific weights to different influence factors by using a statistical method, and calculating to obtain the correlation between the scheduling priority transaction and the memory data/disk data by using a weighted least square method.
The pre-scheduling mechanism may construct some variable templates, such as pre-judging according to a visible range, pre-judging according to a relationship between objects, or combining them, where some parameters may be customized, such as a pre-scheduling range, a threshold of a pre-scheduling object, etc.
Aiming at the characteristics of the cloud edge resource cooperative scheduling service such as dynamic property, uncertainty and large scale, and facing to the application requirements of multi-terminal diversity and high concurrency, the invention needs to establish a self-adaptive optimization mechanism of cloud edge scheduling tasks and resource allocation, and mainly comprises two aspects of self-adaptive optimization of the cloud edge scheduling tasks and self-adaptive optimization of the cloud edge resource allocation.
Firstly, a cloud edge scheduling task optimization mechanism combining real-time scheduling and pre-scheduling is established, tracking and loading of pre-scheduled objects are achieved by establishing an associated sequence model of the scheduled objects, a pre-scheduling mechanism of a variable template is established, pre-scheduling graph structures of different space-time calculations, analyses or interactions are established based on template rules, the pre-scheduled tasks are optimized in time according to changes of the real-time scheduling tasks, and self-adaptive optimization of the scheduling tasks integrating real-time scheduling and pre-scheduling is achieved.
The template rule is that some rules are defined for the pre-scheduling template, and the pre-scheduling can be set, so that a user can set the pre-scheduling. The construction of the pre-scheduling graph structure adopts a pre-scheduling technology based on an object relationship graph, the pre-scheduling technology adopts an object-oriented idea, an object relationship graph is established for a geographic process calculation object with inheritance, derivation, combination and aggregation relationships, and the tracking and loading of the pre-scheduling object are realized along the graph. However, different geographic phenomena and processes have inherent characteristics (structural information, parameter information, relationship information, evolution information and the like), so that templates need to be constructed, and pre-dispatching graph structures of different geographic processes are constructed based on template rules, so as to meet the requirement of real-time geographic process calculation and analysis.
In one implementation manner, the "scheduling data to be scheduled next in advance" in the step S400 specifically includes the following three points: in the pre-scheduling of the cloud center, scheduling the spatio-temporal data to be scheduled in the next step from a hard disk to a memory in advance; in the pre-scheduling of the edge server, transmitting the spatio-temporal data to be scheduled next step from the cloud center to the edge server in advance; in the pre-scheduling of the user terminal, the space-time data needed by the user terminal in the next step is scheduled from the edge server to the memory of the user terminal in advance. That is to say, the pre-scheduling in the cloud center mainly schedules the spatio-temporal data which can be scheduled in the future from the hard disk to the memory in advance; the scheduling of the edge server mainly comprises the steps of transmitting possible future scheduled space-time data from the cloud center to the edge server in advance; the pre-scheduling of the user terminal is mainly to schedule the time-space data which can be used by the terminal in the next step from the edge server to the memory of the user terminal in advance.
The step S400 is followed by: s500, the cloud center optimizes the cloud edge resource allocation according to the pre-scheduling task and the real-time scheduling task sent by the user, and performs matching optimization processing of the cloud edge task and the resource in a many-to-many application scene according to an optimization result and the cloud edge resource service quality evaluation information.
That is to say, the cloud center optimizes the cloud edge resource allocation according to the scheduling task optimization, performs resource quality service evaluation according to the optimization adjustment of the scheduling task and the cloud edge storage calculation, performs matching optimization of the cloud edge task and the resource in a many-to-many application scene, and realizes real-time optimization and dynamic allocation of the cloud edge resource allocation.
In an implementation manner, referring to fig. 5, the step S500 specifically includes:
s510, the cloud center optimizes the cloud side resource allocation according to the pre-scheduling task and a real-time scheduling task sent by a user to obtain scheduling task optimization adjustment information;
s520, according to the scheduling task optimization adjustment information and the cloud side resource service quality evaluation information, and according to a pre-established bilateral matching optimization model of the cloud side task resources, matching optimization processing of the cloud side tasks and the resources in a many-to-many application scene is carried out.
Specifically, self-adaptive optimization is carried out on cloud side resource allocation according to scheduling task optimization, a resource quality service evaluation model is drawn according to optimization adjustment of scheduling tasks and cloud side storage and calculation, a cloud side task-resource bilateral matching optimization model is established, matching optimization of cloud side tasks and resources in a multi-to-multi application scene is carried out, real-time optimization and dynamic allocation of cloud side resource allocation are achieved, a high-expandability and high-adaptability cloud side scheduling task integrated allocation and optimization mechanism is established, and the multi-terminal diversified multi-level visual high-concurrency application requirements are effectively met.
In the cloud edge-side fusion mode, the tasks comprise a data drawing task, a data scheduling task, a calculation analysis task and an interactive calculation task. The resources are divided into storage resources, computing resources and drawing resources of the cloud side. A task request, called a task for short, issued by a resource demander; cloud resource services, which are issued by a resource provider, are called resources for short.
The purpose of establishing the cloud side task-resource bilateral matching optimization model is to match the m subtask attribute index information with the n resource attribute index information, so that the satisfaction degrees of a task main body and a resource main body are optimal at the same time, and the optimal task-resource matching is sought. Firstly, clustering similar resources is completed; secondly, determining an optimal task set corresponding to each type of resource; thirdly, designing a main body satisfaction solving algorithm; fourthly, based on the knowledge of the satisfaction degree of the main body, an optimization model of bilateral matching decision of the cloud side end fusion platform task and the resource is constructed; and fifthly, solving the model and determining the optimal matching result of the task and the resource.
In other words, the cloud edge resource adaptive scheduling optimization method based on the service quality evaluation is researched according to the characteristics of randomness, dynamics, diversity, uncertainty and the like of the space-time data scheduling task under the cloud edge hybrid architecture, and a cloud edge scheduling task adaptive optimization mechanism is established.
Specifically, a cloud side storage and computation resource service quality evaluation model associated with the historical data in real time is established. Through the correlation analysis of the real-time data and the historical data, the service quality of the cloud side deposit and drawing resources is subjected to quantitative and accurate evaluation of global and local combination, and the overall reliability of the service quality evaluation result is ensured.
And secondly, dynamically optimizing the scheduling service chain based on the service quality evaluation model. According to indexes such as performance, load capacity and stability of each service unit, the service quality of each service unit is evaluated based on a service quality evaluation model, and based on a service chain service unit combination multi-objective optimization method of an improved adaptive genetic algorithm, the adaptive optimization selection of the optimal service combination of the cloud edge scheduling service chain is realized, and the service quality is improved through the dynamic optimization of the scheduling service chain.
And thirdly, optimizing the scheduling task of the cloud side end by combining real-time scheduling and pre-scheduling. A cloud edge scheduling task self-adaptive optimization mechanism facing to high-concurrency diversified task requests is established, and a cloud edge scheduling task self-adaptive optimization mechanism combining real-time scheduling and pre-scheduling is established for the scheduling requirements of multi-terminal diversified high-concurrency tasks through an integrated pre-scheduling mechanism.
And fourthly, performing self-adaptive optimization on the matching of the cloud edge scheduling task and the resource. And establishing a self-adaptive optimization mechanism for matching the scheduling task of the cloud side end with the resource by combining the optimization adjustment of the scheduling task and the cloud side end memory-computation resource quality service evaluation model.
Further, as shown in fig. 6, based on the optimization method of spatio-temporal data and resource scheduling under the cloud edge architecture, the present invention also provides a cloud center, which includes a processor 10 and a memory 20. Fig. 6 shows only a portion of the components of the cloud center, but it is to be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.
The storage 20 may be an internal storage unit of the cloud center in some embodiments, for example, a hard disk or a memory of the cloud center. In other embodiments, the memory 20 may also be an external storage device of the cloud center, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the cloud center. Further, the memory 20 may also include both an internal storage unit and an external storage device of the cloud center. The memory 20 is used for storing application software installed in the cloud center and various types of data, such as program codes for installing the cloud center. The memory 20 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 20 stores thereon an optimization program 30 for scheduling spatiotemporal data and resources under a cloud-edge architecture, and the optimization program 30 for scheduling spatiotemporal data and resources under the cloud-edge architecture can be executed by the processor 10, so as to implement the optimization method for scheduling spatiotemporal data and resources under the cloud-edge architecture in the present application.
The processor 10 may be a Central Processing Unit (CPU), a microprocessor or other data Processing chip in some embodiments, and is configured to run program codes stored in the memory 20 or process data, for example, execute an optimization method of spatiotemporal data and resource scheduling in the cloud edge architecture.
In one embodiment, when the processor 10 executes the optimization program 30 for spatiotemporal data and resource scheduling under the cloud-side architecture in the memory 20, the following steps are implemented:
under a cloud edge terminal architecture, carrying out log recording on the execution process of each time-space data scheduling task in a cloud center, an edge server and a user terminal, and regularly converging the log records on the edge server and the user terminal to the cloud center;
the cloud center analyzes the resource service quality index and the network service quality index in the log record, and performs overall and local combined quantitative evaluation on the cloud edge resource service quality to obtain cloud edge resource service quality evaluation information;
evaluating the service quality of each service unit of the cloud edge terminal according to the cloud edge resource service quality evaluation information, and performing multi-objective optimization processing on service chain service unit combinations;
the cloud center establishes a pre-scheduling task at the cloud center, the edge server and the user terminal according to the cloud edge resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud edge terminal in the log record, and schedules data to be scheduled next in advance;
and the cloud center optimizes the cloud edge resource allocation according to the pre-scheduling task and the real-time scheduling task sent by the user, and performs matching optimization processing of the cloud edge task and the resource in a 'many-to-many' application scene according to an optimization result and the cloud edge resource service quality evaluation information.
The cloud center analyzes the resource service quality index and the network service quality index in the log record, performs quantitative evaluation of global and local combination on the cloud edge resource service quality, and obtains cloud edge resource service quality evaluation information, and the method comprises the following steps:
the cloud center establishes a correlation matrix according to the relation between the resource service quality index and the network service quality index in the log record, and determines a comprehensive evaluation value of the storage and plotting service by using a principal component analysis method;
performing correlation analysis on the real-time data and the historical data corresponding to the resource service quality index and the network service quality index, and performing overall and local combined quantitative accurate evaluation on the cloud side storage and mapping resource service quality;
and obtaining the service quality evaluation information of the cloud-edge resources according to the comprehensive evaluation value and the quantitative accurate evaluation.
The cloud center establishes a correlation matrix according to the relation between the resource service quality index and the network service quality index in the log record, and before determining the comprehensive evaluation value of the storage and drawing service by using a principal component analysis method, the method further comprises the following steps:
establishing an index system for evaluating the cloud side storage and drawing service quality according to the log record;
the index system comprises a resource service quality index and a network service quality index.
The resource service quality index comprises: integrating service response time, service throughput, service cost, and service reliability; the network service quality indicators include: network throughput, service access success rate, task execution success rate, and network transmission delay.
Evaluating the service quality of each service unit of the cloud edge terminal according to the cloud edge resource service quality evaluation information, and performing multi-objective optimization processing on service chain service unit combination, wherein the multi-objective optimization processing comprises the following steps:
dynamically evaluating the service quality of each service unit of the cloud side according to the cloud side resource service quality evaluation information to obtain dynamic evaluation information;
according to the dynamic evaluation information, performing dynamic combination and optimization adjustment on each service unit by using a self-adaptive genetic algorithm to obtain dynamic optimization information of a service chain;
and performing multi-objective optimization processing on the service chain service unit combination according to the service chain dynamic optimization information.
The cloud center establishes a pre-scheduling task at the cloud center, the edge server and the user terminal according to the cloud edge resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud edge terminal in the log record, and schedules data to be scheduled in the next step in advance, wherein the pre-scheduling task comprises the following steps:
the cloud center establishes tracking and loading of a pre-scheduling object in the cloud center, the edge server and the user terminal according to the cloud edge resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud edge terminal in the log record;
constructing pre-scheduling graph structures of different space-time calculations, analyses or interactions according to a pre-scheduling mechanism of a pre-constructed variable template to obtain pre-scheduling tasks;
and optimizing the pre-scheduling task according to the change of the real-time scheduling task, and scheduling the data to be scheduled next in advance.
The cloud center optimizes the cloud edge resource allocation according to the pre-scheduling task and the real-time scheduling task sent by the user, and performs matching optimization processing of the cloud edge task and the resource in a many-to-many application scene according to an optimization result and the cloud edge resource service quality evaluation information, wherein the matching optimization processing comprises the following steps:
the cloud center optimizes the resource allocation of the cloud side end according to the pre-scheduling task and a real-time scheduling task sent by a user to obtain scheduling task optimization adjustment information;
and performing matching optimization processing of cloud side tasks and resources in a many-to-many application scene according to the scheduling task optimization adjustment information and the cloud side resource service quality evaluation information and a pre-established bilateral matching optimization model of the cloud side task resources.
The scheduling of the data to be scheduled next step in advance comprises the following steps:
in the pre-scheduling of the cloud center, scheduling the spatio-temporal data to be scheduled in the next step from a hard disk to a memory in advance;
in the pre-scheduling of the edge server, transmitting the spatio-temporal data to be scheduled next step from the cloud center to the edge server in advance;
in the pre-scheduling of the user terminal, the space-time data needed by the user terminal in the next step is scheduled from the edge server to the memory of the user terminal in advance.
The present invention also provides a computer-readable storage medium storing a computer program executable to implement the steps of the method for optimizing spatio-temporal data and resource scheduling under a cloud-edge architecture as described above.
In summary, the method for optimizing spatio-temporal data and resource scheduling under the cloud edge architecture disclosed by the invention comprises the following steps: under a cloud edge terminal architecture, carrying out log recording on the execution process of each time-space data scheduling task in a cloud center, an edge server and a user terminal, and regularly converging the log records on the edge server and the user terminal to the cloud center; the cloud center analyzes the resource service quality index and the network service quality index in the log record, and performs overall and local combined quantitative evaluation on the cloud edge resource service quality to obtain cloud edge resource service quality evaluation information; evaluating the service quality of each service unit of the cloud edge terminal according to the cloud edge resource service quality evaluation information, and performing multi-objective optimization processing on service chain service unit combinations; the cloud center establishes a pre-scheduling task at the cloud center, the edge server and the user terminal according to the cloud edge resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud edge terminal in the log record, and schedules data to be scheduled next in advance; and the cloud center optimizes the cloud edge resource allocation according to the pre-scheduling task and the real-time scheduling task sent by the user, and performs matching optimization processing of the cloud edge task and the resource in a 'many-to-many' application scene according to an optimization result and the cloud edge resource service quality evaluation information. The method analyzes the resource service quality index and the network service quality index in the log record, performs quantitative evaluation of global and local combination on the cloud edge resource service quality, performs multi-objective optimization processing on the service chain service unit combination, optimizes the cloud edge resource allocation according to the pre-scheduling task and the real-time scheduling task sent by the user, performs matching optimization processing of the cloud edge task and the resource in a multi-to-multi application scene, realizes optimization of time-space data and resource scheduling under a cloud edge framework, and enables the resource to realize efficient scheduling of large-scale time-space data under a cloud edge mixed framework.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.

Claims (10)

1. A method for optimizing spatio-temporal data and resource scheduling under a cloud edge architecture is characterized by comprising the following steps:
under a cloud edge terminal architecture, carrying out log recording on the execution process of each time-space data scheduling task in a cloud center, an edge server and a user terminal, and regularly converging the log records on the edge server and the user terminal to the cloud center;
the cloud center analyzes the resource service quality index and the network service quality index in the log record, and performs overall and local combined quantitative evaluation on the cloud edge resource service quality to obtain cloud edge resource service quality evaluation information;
evaluating the service quality of each service unit of the cloud edge terminal according to the cloud edge resource service quality evaluation information, and performing multi-objective optimization processing on service chain service unit combinations;
the cloud center establishes a pre-scheduling task at the cloud center, the edge server and the user terminal according to the cloud edge resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud edge terminal in the log record, and schedules data to be scheduled next in advance;
and the cloud center optimizes the cloud edge resource allocation according to the pre-scheduling task and the real-time scheduling task sent by the user, and performs matching optimization processing of the cloud edge task and the resource in a 'many-to-many' application scene according to an optimization result and the cloud edge resource service quality evaluation information.
2. The method for optimizing spatio-temporal data and resource scheduling under the cloud edge architecture according to claim 1, wherein the cloud center analyzes resource service quality indicators and network service quality indicators in the log records, performs quantitative evaluation of global and local combination on cloud edge resource service quality to obtain cloud edge resource service quality evaluation information, and comprises:
the cloud center establishes a correlation matrix according to the relation between the resource service quality index and the network service quality index in the log record, and determines a comprehensive evaluation value of the storage and plotting service by using a principal component analysis method;
performing correlation analysis on the real-time data and the historical data corresponding to the resource service quality index and the network service quality index, and performing overall and local combined quantitative accurate evaluation on the cloud side storage and mapping resource service quality;
and obtaining the service quality evaluation information of the cloud-edge resources according to the comprehensive evaluation value and the quantitative accurate evaluation.
3. The method for optimizing spatio-temporal data and resource scheduling under the cloud edge architecture according to claim 2, wherein the cloud center establishes a correlation matrix according to a relationship between a resource service quality index and a network service quality index in a log record, and further comprises, before determining a comprehensive evaluation value of a deposit-and-draw service by using a principal component analysis method:
establishing an index system for evaluating the cloud side storage and drawing service quality according to the log record;
the index system comprises a resource service quality index and a network service quality index.
4. The method for optimizing spatio-temporal data and resource scheduling under the cloud-edge architecture according to claim 2, wherein the resource quality of service indicator includes: integrating service response time, service throughput, service cost, and service reliability; the network service quality indicators include: network throughput, service access success rate, task execution success rate, and network transmission delay.
5. The method for optimizing spatio-temporal data and resource scheduling under the cloud edge architecture according to claim 1, wherein evaluating the service quality of each service unit of the cloud edge according to the cloud edge resource service quality evaluation information, and performing multi-objective optimization processing on a service chain service unit combination comprises:
dynamically evaluating the service quality of each service unit of the cloud side according to the cloud side resource service quality evaluation information to obtain dynamic evaluation information;
according to the dynamic evaluation information, performing dynamic combination and optimization adjustment on each service unit by using a self-adaptive genetic algorithm to obtain dynamic optimization information of a service chain;
and performing multi-objective optimization processing on the service chain service unit combination according to the service chain dynamic optimization information.
6. The method for optimizing spatio-temporal data and resource scheduling under the cloud edge architecture according to claim 1, wherein the cloud center establishes a pre-scheduling task at the cloud center, the edge server and the user terminal according to the cloud edge resource service quality evaluation information in combination with the cloud edge various resource utilization information and the idle condition information in the log record, and schedules data to be scheduled next in advance, including:
the cloud center establishes tracking and loading of a pre-scheduling object in the cloud center, the edge server and the user terminal according to the cloud edge resource service quality evaluation information and by combining various resource utilization information and idle condition information of the cloud edge terminal in the log record;
constructing pre-scheduling graph structures of different space-time calculations, analyses or interactions according to a pre-scheduling mechanism of a pre-constructed variable template to obtain pre-scheduling tasks;
and optimizing the pre-scheduling task according to the change of the real-time scheduling task, and scheduling the data to be scheduled next in advance.
7. The method for optimizing spatio-temporal data and resource scheduling under the cloud edge architecture according to claim 1, wherein the cloud center optimizes cloud edge resource allocation according to a pre-scheduling task and a real-time scheduling task sent by a user, and performs matching optimization processing of cloud edge tasks and resources in a many-to-many application scenario according to an optimization result and the cloud edge resource service quality evaluation information, comprising:
the cloud center optimizes the resource allocation of the cloud side end according to the pre-scheduling task and a real-time scheduling task sent by a user to obtain scheduling task optimization adjustment information;
and performing matching optimization processing of cloud side tasks and resources in a many-to-many application scene according to the scheduling task optimization adjustment information and the cloud side resource service quality evaluation information and a pre-established bilateral matching optimization model of the cloud side task resources.
8. The method for optimizing spatio-temporal data and resource scheduling under the cloud edge architecture according to claim 1, wherein the scheduling of the data to be scheduled next step in advance comprises:
in the pre-scheduling of the cloud center, scheduling the spatio-temporal data to be scheduled in the next step from a hard disk to a memory in advance;
in the pre-scheduling of the edge server, transmitting the spatio-temporal data to be scheduled next step from the cloud center to the edge server in advance;
in the pre-scheduling of the user terminal, the space-time data needed by the user terminal in the next step is scheduled from the edge server to the memory of the user terminal in advance.
9. A cloud center, comprising: the optimization program of spatiotemporal data and resource scheduling under the cloud edge architecture is stored on the memory and can run on the processor, and when being executed by the processor, the optimization program of spatiotemporal data and resource scheduling under the cloud edge architecture realizes the steps of the optimization method of spatiotemporal data and resource scheduling under the cloud edge architecture according to any one of claims 1 to 8.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executable to implement the steps of the method for optimizing spatio-temporal data and resource scheduling in a cloud-edge architecture according to any one of claims 1 to 8.
CN202110576571.XA 2021-05-26 2021-05-26 Optimization method for time-space data and resource scheduling under cloud edge architecture Active CN113037877B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110576571.XA CN113037877B (en) 2021-05-26 2021-05-26 Optimization method for time-space data and resource scheduling under cloud edge architecture

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110576571.XA CN113037877B (en) 2021-05-26 2021-05-26 Optimization method for time-space data and resource scheduling under cloud edge architecture

Publications (2)

Publication Number Publication Date
CN113037877A true CN113037877A (en) 2021-06-25
CN113037877B CN113037877B (en) 2021-08-24

Family

ID=76455802

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110576571.XA Active CN113037877B (en) 2021-05-26 2021-05-26 Optimization method for time-space data and resource scheduling under cloud edge architecture

Country Status (1)

Country Link
CN (1) CN113037877B (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113726846A (en) * 2021-07-14 2021-11-30 阿里巴巴新加坡控股有限公司 Edge cloud system, resource scheduling method, equipment and storage medium
CN114460892A (en) * 2021-12-20 2022-05-10 北京科技大学 Task control method based on cloud programmable logic controller
CN114584563A (en) * 2022-03-18 2022-06-03 西安超越申泰信息科技有限公司 Edge-side cooperative intelligent resource scheduling method, device, terminal and storage medium
CN115408136A (en) * 2022-11-01 2022-11-29 安徽思高智能科技有限公司 RPA flow scheduling method based on genetic algorithm
CN115442444A (en) * 2022-11-04 2022-12-06 山东紫菜云数字科技有限公司 Resource balance self-adaptive scheduling method applied to many-to-many matching platform
CN117370035A (en) * 2023-12-08 2024-01-09 国网浙江省电力有限公司宁波供电公司 Real-time simulation computing resource dividing system and method
CN117938766A (en) * 2024-03-25 2024-04-26 中国电子科技集团公司第五十四研究所 Hierarchical transmission method for data resources under limited network
CN117938766B (en) * 2024-03-25 2024-06-04 中国电子科技集团公司第五十四研究所 Hierarchical transmission method for data resources under limited network

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190140933A1 (en) * 2018-12-28 2019-05-09 Francesc Guim Bernat Dynamic quality of service in edge cloud architectures
CN111585916A (en) * 2019-12-26 2020-08-25 国网辽宁省电力有限公司电力科学研究院 LTE electric power wireless private network task unloading and resource allocation method based on cloud edge cooperation
CN111800486A (en) * 2020-06-22 2020-10-20 山东大学 Cloud edge cooperative resource scheduling method and system
CN112367354A (en) * 2020-10-09 2021-02-12 国网电力科学研究院有限公司 Intelligent scheduling system and scheduling method for cloud-edge resource graph
CN112506644A (en) * 2020-11-02 2021-03-16 合肥工业大学 Task scheduling method and system based on cloud edge-side hybrid computing mode system
CN112637263A (en) * 2020-11-23 2021-04-09 国网电力科学研究院有限公司 Multi-data center resource optimization promotion method and system and storage medium
CN112650581A (en) * 2020-12-21 2021-04-13 湘潭大学 Cloud-side cooperative task scheduling method for intelligent building

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190140933A1 (en) * 2018-12-28 2019-05-09 Francesc Guim Bernat Dynamic quality of service in edge cloud architectures
CN111585916A (en) * 2019-12-26 2020-08-25 国网辽宁省电力有限公司电力科学研究院 LTE electric power wireless private network task unloading and resource allocation method based on cloud edge cooperation
CN111800486A (en) * 2020-06-22 2020-10-20 山东大学 Cloud edge cooperative resource scheduling method and system
CN112367354A (en) * 2020-10-09 2021-02-12 国网电力科学研究院有限公司 Intelligent scheduling system and scheduling method for cloud-edge resource graph
CN112506644A (en) * 2020-11-02 2021-03-16 合肥工业大学 Task scheduling method and system based on cloud edge-side hybrid computing mode system
CN112637263A (en) * 2020-11-23 2021-04-09 国网电力科学研究院有限公司 Multi-data center resource optimization promotion method and system and storage medium
CN112650581A (en) * 2020-12-21 2021-04-13 湘潭大学 Cloud-side cooperative task scheduling method for intelligent building

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
THANDA OO: ""Application-aware Task Scheduling in"", 《 2019 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC)》 *
周冰等: "基于主成份分析的云制造服务QoS评估方法研究", 《制造业自动化》 *
孙毓聪等: "一种边缘云协同的任务调度方法研究", 《无线互联科技》 *
赵宏伟: ""面向云边协同的资源调度优化策略"", 《沈阳大学学报》 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113726846A (en) * 2021-07-14 2021-11-30 阿里巴巴新加坡控股有限公司 Edge cloud system, resource scheduling method, equipment and storage medium
CN113726846B (en) * 2021-07-14 2024-03-05 阿里巴巴新加坡控股有限公司 Edge cloud system, resource scheduling method, equipment and storage medium
CN114460892A (en) * 2021-12-20 2022-05-10 北京科技大学 Task control method based on cloud programmable logic controller
CN114584563A (en) * 2022-03-18 2022-06-03 西安超越申泰信息科技有限公司 Edge-side cooperative intelligent resource scheduling method, device, terminal and storage medium
CN115408136A (en) * 2022-11-01 2022-11-29 安徽思高智能科技有限公司 RPA flow scheduling method based on genetic algorithm
CN115442444A (en) * 2022-11-04 2022-12-06 山东紫菜云数字科技有限公司 Resource balance self-adaptive scheduling method applied to many-to-many matching platform
CN115442444B (en) * 2022-11-04 2023-03-24 山东紫菜云数字科技有限公司 Resource balance self-adaptive scheduling method applied to many-to-many matching platform
CN117370035A (en) * 2023-12-08 2024-01-09 国网浙江省电力有限公司宁波供电公司 Real-time simulation computing resource dividing system and method
CN117370035B (en) * 2023-12-08 2024-05-07 国网浙江省电力有限公司宁波供电公司 Real-time simulation computing resource dividing system and method
CN117938766A (en) * 2024-03-25 2024-04-26 中国电子科技集团公司第五十四研究所 Hierarchical transmission method for data resources under limited network
CN117938766B (en) * 2024-03-25 2024-06-04 中国电子科技集团公司第五十四研究所 Hierarchical transmission method for data resources under limited network

Also Published As

Publication number Publication date
CN113037877B (en) 2021-08-24

Similar Documents

Publication Publication Date Title
CN113037877B (en) Optimization method for time-space data and resource scheduling under cloud edge architecture
CN1956457B (en) Method and apparatus for arranging mesh work in mesh computing system
CN114721833B (en) Intelligent cloud coordination method and device based on platform service type
CN111614754B (en) Fog-calculation-oriented cost-efficiency optimized dynamic self-adaptive task scheduling method
CN111752706B (en) Resource allocation method, device and storage medium
Mostafavi et al. A stochastic approximation approach for foresighted task scheduling in cloud computing
CN113822456A (en) Service combination optimization deployment method based on deep reinforcement learning in cloud and mist mixed environment
CN104657217B (en) A kind of cloud environment method for scheduling task based on non-homogeneous grain-size classification
CN115914392A (en) Computing power network resource scheduling method and system
CN113553160A (en) Task scheduling method and system for edge computing node of artificial intelligence Internet of things
CN115033340A (en) Host selection method and related device
CN113902116A (en) Deep learning model-oriented reasoning batch processing optimization method and system
CN108415775A (en) A kind of quality-of-service based resource determining method and system
Tekiyehband et al. An efficient dynamic service provisioning mechanism in fog computing environment: A learning automata approach
CN101626305A (en) Trusted dynamic level scheduling method for improving security of network environment
CN103209102A (en) Web quality of service distributed measurement system and method
CN113778675A (en) Calculation task distribution system and method based on block chain network
CN116896591A (en) Scheduling method and device for network data analysis model and computer equipment
Chowdhary et al. QoS enhancement in cloud-IoT framework for educational institution with task allocation and scheduling with task-VM matching approach
Prado et al. On providing quality of service in grid computing through multi-objective swarm-based knowledge acquisition in fuzzy schedulers
CN115016911A (en) Task arrangement method, device, equipment and medium for large-scale federal learning
CN113762972A (en) Data storage control method and device, electronic equipment and storage medium
CN114462506A (en) Communication network aided planning method supporting preference strategy and application thereof
CN113485718B (en) Context-aware AIoT application program deployment method in edge cloud cooperative system
Na et al. An adaptive replanning mechanism for dependable service-based systems

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Shenzhen Zhongzhi Chelian Science and Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026134

Denomination of invention: Optimization method of spatio-temporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20221211

Application publication date: 20210625

Assignee: Shenzhen No.7 Network Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026329

Denomination of invention: Optimization method of spatio-temporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20221212

Application publication date: 20210625

Assignee: Chongqing Taihuo Xinniao Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026159

Denomination of invention: Optimization method of spatio-temporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20221211

Application publication date: 20210625

Assignee: Shenzhen Bangqi Technology Innovation Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026142

Denomination of invention: Optimization method of spatio-temporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20221211

Application publication date: 20210625

Assignee: Shenzhen Tiya Digital Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026204

Denomination of invention: Optimization method of spatio-temporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20221211

Application publication date: 20210625

Assignee: SHENZHEN MIGOU NETWORK TECHNOLOGY Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026323

Denomination of invention: Optimization method of spatio-temporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20221212

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Shenzhen City Ying min Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026689

Denomination of invention: Optimization method of spatio-temporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20221213

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Guangdong Zhongke Huiju Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026706

Denomination of invention: Optimization method of spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20230110

Application publication date: 20210625

Assignee: Jingyun Grapefruit Technology (Shenzhen) Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026707

Denomination of invention: Optimization method of spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20230110

Application publication date: 20210625

Assignee: Beijing Taiflamingo Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026674

Denomination of invention: Optimization method of spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20230111

Application publication date: 20210625

Assignee: Shenzhen Search Industry Operation Service Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026696

Denomination of invention: Optimization method of spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20230110

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Chongqing Taihuo Xinniao Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2022980026805

Denomination of invention: Optimization method of spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20230116

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Shenzhen city fine uni-data Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980033383

Denomination of invention: Optimization method for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20230308

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Shenzhen Aonuo Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980034032

Denomination of invention: Optimization method for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20230323

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Lishui Taihuo Red Bird Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980034588

Denomination of invention: Optimization method for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20230411

EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Shenzhen Yingqi Consulting Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980047348

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231116

Application publication date: 20210625

Assignee: Shenzhen Minghua Trading Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980047346

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231116

Application publication date: 20210625

Assignee: Shenzhen Dongfang Huilian Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980047336

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231116

Application publication date: 20210625

Assignee: Shenzhen Weigao Investment Development Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980047270

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231116

Application publication date: 20210625

Assignee: Yuncheng Holding (Shenzhen) Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980047231

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231116

Application publication date: 20210625

Assignee: Sankexiaocao (Shenzhen) Internet of Things Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980047154

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231115

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Shenzhen Xunming Trading Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980047343

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231116

Application publication date: 20210625

Assignee: Shenzhen Haocai Digital Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980047340

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231116

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Shenzhen Kaixin Intelligent Control Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048385

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231124

Application publication date: 20210625

Assignee: Shenzhen Jiahui Education Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048376

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231124

Application publication date: 20210625

Assignee: Shenzhen Huihong Information Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048375

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231124

Application publication date: 20210625

Assignee: Shenzhen Guangwang Bozhan Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048373

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231124

Application publication date: 20210625

Assignee: DISCOVERY TECHNOLOGY (SHENZHEN) Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048372

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231124

Application publication date: 20210625

Assignee: SHENZHEN SIYOU TECHNOLOGY CO.,LTD.

Assignor: SHENZHEN University

Contract record no.: X2023980048287

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231123

Application publication date: 20210625

Assignee: Shenzhen Zhenglian Haodong Technology Development Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048082

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231123

Application publication date: 20210625

Assignee: Foshan Point to Intelligent Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048054

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231123

Application publication date: 20210625

Assignee: Shenzhen chuangyue Precision Machinery Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048053

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231123

Application publication date: 20210625

Assignee: Aixunda Technology (Shenzhen) Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048047

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231123

Application publication date: 20210625

Assignee: Shenzhen Xinsheng interconnected technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048035

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231123

Application publication date: 20210625

Assignee: Shenzhen Zhuoqi Digital Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980047950

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231123

Application publication date: 20210625

Assignee: Shenzhen Andian Electric Power Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980047939

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231123

EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Shenzhen Weiyuan Precision Technology Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048790

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231128

Application publication date: 20210625

Assignee: Shenzhen Zhihui Computer Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048429

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231127

Application publication date: 20210625

Assignee: Shenzhen Foresea Allchips Information & Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048420

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231127

Application publication date: 20210625

Assignee: Easy to sign chain (Shenzhen) Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048402

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231127

Application publication date: 20210625

Assignee: Shenzhen Ruibotong Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048397

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231127

Application publication date: 20210625

Assignee: SHENZHEN LIHAI HONGJIN TECHNOLOGY CO.,LTD.

Assignor: SHENZHEN University

Contract record no.: X2023980048392

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231127

Application publication date: 20210625

Assignee: Shenzhen Air Traffic Control Industry Development Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048352

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231127

Application publication date: 20210625

Assignee: Shiyun Technology (Shenzhen) Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048351

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231127

Application publication date: 20210625

Assignee: Huizhou Dabaihui Modern Fisheries Application Research Institute

Assignor: SHENZHEN University

Contract record no.: X2023980048341

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231128

Application publication date: 20210625

Assignee: SHENZHEN MAGIC-RAY TECHNOLOGY Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048336

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231127

Application publication date: 20210625

Assignee: Shenzhen Lingyu Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048332

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231124

Application publication date: 20210625

Assignee: Matrix Origin (Shenzhen) Information Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980048322

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231124

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Shenzhen Zhuoxin Smart Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980050521

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231206

Application publication date: 20210625

Assignee: JIUZHOU YANGGUANG POWER SUPPLY (SHENZHEN) CO.,LTD.

Assignor: SHENZHEN University

Contract record no.: X2023980050235

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231206

Application publication date: 20210625

Assignee: Shenzhen Huike Energy Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980050230

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231206

Application publication date: 20210625

Assignee: Shenzhen Huike Storage Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980050228

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231205

Application publication date: 20210625

Assignee: Shenzhen Youyou Internet Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980049890

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231204

Application publication date: 20210625

Assignee: Shenzhen Yunpo Cultural Communication Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980049889

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231204

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: SHENZHEN HUA,ANTAI INTELLIGENT & TECHNOLOGY Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980050859

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231208

Application publication date: 20210625

Assignee: Shenzhen Pengcheng Future Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980050536

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231207

Application publication date: 20210625

Assignee: Yimaitong (Shenzhen) Intelligent Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980050485

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231208

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Yiqian Network Technology (Shenzhen) Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980052642

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231218

Application publication date: 20210625

Assignee: SHENZHEN HUIKE PRECISION INDUSTRY Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980052469

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231214

Application publication date: 20210625

Assignee: Xingang Technology (Shenzhen) Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980052129

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231213

Application publication date: 20210625

Assignee: Shenzhen Yuanlian Digital Intelligence Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2023980052126

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20231213

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: SHENZHEN GENERAL BARCODE'S TECHNOLOGY DEVELOPMENT CENTER

Assignor: SHENZHEN University

Contract record no.: X2024980000040

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20240103

Application publication date: 20210625

Assignee: Shenzhen Subangbo Intelligent Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2024980000038

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20240103

Application publication date: 20210625

Assignee: Shenzhen Deep Sea Blue Ocean Technology Service Center

Assignor: SHENZHEN University

Contract record no.: X2024980000036

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20240104

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20210625

Assignee: Shenzhen Softbank Sichuang Technology Co.,Ltd.

Assignor: SHENZHEN University

Contract record no.: X2024980000263

Denomination of invention: Optimization methods for spatiotemporal data and resource scheduling under cloud edge architecture

Granted publication date: 20210824

License type: Common License

Record date: 20240108

EE01 Entry into force of recordation of patent licensing contract