CN115981843A - Task scheduling method and device in cloud-edge cooperative power system and computer equipment - Google Patents
Task scheduling method and device in cloud-edge cooperative power system and computer equipment Download PDFInfo
- Publication number
- CN115981843A CN115981843A CN202211586389.3A CN202211586389A CN115981843A CN 115981843 A CN115981843 A CN 115981843A CN 202211586389 A CN202211586389 A CN 202211586389A CN 115981843 A CN115981843 A CN 115981843A
- Authority
- CN
- China
- Prior art keywords
- edge
- sequence
- request sequence
- request
- sub
- 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.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Power Sources (AREA)
Abstract
The application relates to a task scheduling method and device in a cloud-edge cooperative power system and computer equipment. Determining a target edge cluster from an edge cluster sequence determined by the sizes of a plurality of resource load values corresponding to a plurality of edge clusters and creating a container example through a target sub-request sequence of a target position in a request sequence corresponding to power equipment in a cloud edge cooperative power system, distributing the target sub-request sequence to the container example, if the unallocated sub-request sequence is left in the request sequence after the target sub-request sequence is removed, generating a new request sequence based on the unallocated sub-request sequence, returning to the step and redistributing until power calculation and analysis task requests to be distributed in the request sequence are all distributed. Compared with the traditional distribution based on the weighted average, the scheme distributes the calculation tasks based on the resource utilization rate of the clusters through the round-robin scheduling algorithm, and improves the task processing efficiency.
Description
Technical Field
The present application relates to the field of power technologies, and in particular, to a method and an apparatus for task scheduling in a cloud-edge collaborative power system, a computer device, a storage medium, and a computer program product.
Background
With the gradual increase of the capacity of a new energy source such as wind power and photovoltaic and the access of a large number of distributed power sources, adjustable loads and energy storage devices to a power grid for grid-connected operation, the power system scheduling control is changed from the traditional source-load-following mode to a 'source-grid-load-storage' multi-stage coordinated regulation mode, and the real-time sensing and accurate regulation and control capability of the tail end of the power grid on distributed sources, loads and energy storage resources needs to be improved. Due to the bidirectional interaction between the power grid and the distributed resources, the frequency and the number of information interaction are increased rapidly, and higher requirements are provided for the wide-area data transmission and real-time processing and analyzing capacity of the power dispatching system.
In order to meet the requirement of data analysis processing capacity required by a novel power system, a cloud and edge two-stage cooperative power dispatching system is adopted, data services are provided at the edge side of the system by fusing technologies such as network, storage and calculation, the operation efficiency of the system can be effectively improved, and the power dispatching system is a hotspot of current research and application as a solution for large-scale data and analysis and calculation requirements of the power system. The completion of the analysis and calculation tasks of the power system is the key for greatly improving the levels of automatic control, intelligent analysis, autonomous decision making and collaborative optimization application of the power grid. Under the condition of computing and analyzing tasks of a large-scale power system, in order to ensure the overall operation stability level, the loads of a large number of servers in a cloud-side system need to be ensured to be balanced as much as possible, and at present, the task scheduling mode in the cloud-side cooperative power system is generally distributed in a weighted average mode. However, the distribution by means of weighted average fails to determine the load condition of each server, which results in unbalanced load of the server cluster and reduces task processing efficiency.
Therefore, the task scheduling method in the current cloud-edge collaborative power system has the defect of low task processing efficiency.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, a computer readable storage medium, and a computer program product for task scheduling in a cloud-edge collaborative power system, which can improve task processing efficiency.
In a first aspect, the present application provides a method for scheduling tasks in a cloud-edge collaborative power system, where the method includes:
acquiring a request sequence corresponding to power equipment in a cloud-edge cooperative power system; the request sequence comprises a plurality of sub-request sequences; each sub-request sequence comprises a preset number of power calculation analysis task requests to be distributed; determining an application container mirror image according to the plurality of power calculation and analysis task requests to be distributed;
acquiring a plurality of resource load values corresponding to a plurality of edge clusters in the cloud edge collaborative power system, and determining an edge cluster sequence according to the resource load values; the edge cluster sequence comprises a plurality of candidate edge clusters; a plurality of candidate edge clusters in the sequence of edge clusters are ordered based on a magnitude of the resource load value;
determining a corresponding target edge cluster in a plurality of candidate edge clusters in the edge cluster sequence according to a target sub-request sequence of a target position in the request sequence, creating a container instance in the target edge cluster according to the application container mirror image, and distributing the target sub-request sequence to the container instance of the corresponding target edge cluster;
removing the target sub-request sequence from the request sequence, and if an unallocated sub-request sequence exists in the removed request sequence, generating a new request sequence according to the unallocated sub-request sequence; and returning to the step of obtaining a plurality of resource load values corresponding to a plurality of edge clusters in the cloud-edge coordinated power system until the power calculation and analysis task requests to be distributed in each sub-request sequence in the request sequence are distributed.
In one embodiment, the obtaining of the request sequence sent by the power device in the cloud-edge coordinated power system includes:
receiving a plurality of power calculation analysis task requests to be distributed sent by power equipment in a cloud-edge collaborative power system;
according to the request time of the plurality of power calculation and analysis task requests to be distributed, sequencing the plurality of power calculation and analysis task requests to be distributed in sequence;
determining the preset number according to the ratio of the number of the power calculation and analysis task requests to be distributed to the preset grouping number;
and grouping the plurality of power calculation analysis task requests to be distributed according to the preset quantity to obtain a plurality of sub-request sequences, and obtaining the request sequences according to the plurality of sub-request sequences.
In one embodiment, the determining an application container image according to the plurality of to-be-distributed power calculation and analysis task requests includes:
and acquiring a corresponding application container mirror image from the application container agent according to the calculation type corresponding to each power calculation analysis task request to be distributed.
In one embodiment, the obtaining a plurality of resource load values corresponding to a plurality of edge clusters in the cloud edge coordinated power system, and determining an edge cluster sequence according to the resource load values includes:
acquiring at least one of a processor utilization rate, a memory utilization rate and a storage utilization rate corresponding to each edge cluster in the cloud edge collaborative power system, and a utilization rate threshold corresponding to at least one of the processor utilization rate, the memory utilization rate and the storage utilization rate;
obtaining at least one comparison result of at least one of a processor utilization rate, a memory utilization rate and a storage utilization rate corresponding to each edge cluster and a corresponding utilization rate threshold;
taking the edge cluster with the at least one comparison result smaller than the corresponding utilization rate threshold value as a candidate edge cluster;
and sequencing the plurality of candidate edge clusters in an ascending order according to at least one of the utilization rate of a processor, the utilization rate of a memory and the utilization rate of a storage to obtain an edge cluster sequence.
In one embodiment, the sorting the candidate edge clusters in ascending order according to at least one of a processor utilization rate, a memory utilization rate, and a storage utilization rate to obtain an edge cluster sequence includes:
acquiring the average value of the processor utilization rate and the memory utilization rate;
and according to the average value, sequencing the candidate edge clusters in an ascending order to obtain an edge cluster sequence.
In one embodiment, the determining, according to a target sub-request sequence of a target location in the request sequence, a corresponding target edge cluster from a plurality of candidate edge clusters in the edge cluster sequence includes:
acquiring a first sub-request sequence in the request sequence as a target sub-request sequence; the average request time of the first sub-request sequence is the smallest in the request sequence;
acquiring a first candidate edge cluster in the edge cluster sequence as a target edge cluster; the resource load value of the first candidate edge cluster is smallest in the edge cluster sequence.
In a second aspect, the present application provides a task scheduling apparatus in a cloud-edge collaborative power system, where the apparatus includes:
the first acquisition module is used for acquiring a request sequence corresponding to the power equipment in the cloud-edge cooperative power system; the request sequence comprises a plurality of sub-request sequences; each sub-request sequence comprises a preset number of power calculation analysis task requests to be distributed; determining an application container mirror image according to the plurality of power calculation and analysis task requests to be distributed;
a second obtaining module, configured to obtain multiple resource load values corresponding to multiple edge clusters in the cloud-edge coordinated power system, and determine an edge cluster sequence according to the multiple resource load values; the edge cluster sequence comprises a plurality of candidate edge clusters; a plurality of candidate edge clusters in the sequence of edge clusters are ordered based on a magnitude of the resource load value;
an allocation module, configured to determine, according to a target sub-request sequence of a target position in the request sequence, a corresponding target edge cluster among multiple candidate edge clusters in the edge cluster sequence, create a container instance in the target edge cluster according to the application container mirror image, and allocate the target sub-request sequence to the container instance of the corresponding target edge cluster;
a returning module, configured to remove the target sub-request sequence from the request sequence, and if an unallocated sub-request sequence exists in the removed request sequence, generate a new request sequence according to the unallocated sub-request sequence; and returning to the step of obtaining a plurality of resource load values corresponding to a plurality of edge clusters in the cloud-edge coordinated power system until the power calculation and analysis task requests to be distributed in each sub-request sequence in the request sequence are distributed.
In a third aspect, the present application provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method described above.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the method described above.
According to the task scheduling method, the task scheduling device, the computer equipment, the storage medium and the computer program product in the cloud-edge collaborative power system, the request sequence corresponding to the power equipment in the cloud-edge collaborative power system is obtained, the edge cluster sequence is determined according to the sizes of the resource load values corresponding to the edge clusters, the target edge cluster is determined from the edge cluster sequence according to the target sub-request sequence of the target position in the request sequence, the container instance is created in the target edge cluster sequence, the target sub-request sequence is distributed to the container instance, if the unallocated sub-request sequence is left in the request sequence after the target sub-request sequence is removed, a new request sequence is generated based on the unallocated sub-request sequence, and the steps of obtaining the resource load values corresponding to the edge clusters in the cloud-edge collaborative power system are returned until the power calculation analysis task requests to be distributed in the request sequence are all distributed. Compared with the traditional distribution based on the weighted average, the scheme distributes the calculation tasks based on the resource utilization rate of the clusters through the round-robin scheduling algorithm, and improves the task processing efficiency.
Drawings
Fig. 1 is an application environment diagram of a task scheduling method in a cloud-edge collaborative power system in an embodiment;
FIG. 2 is a schematic flow chart illustrating a task scheduling method in the cloud-edge collaborative power system according to an embodiment;
fig. 3 is a schematic flow chart of a task scheduling method in the cloud-edge collaborative power system according to another embodiment;
fig. 4 is a block diagram illustrating a structure of a task scheduling device in the cloud-edge coordinated power system according to an embodiment;
FIG. 5 is a diagram of the internal structure of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
The task scheduling method in the cloud-edge collaborative power system provided by the embodiment of the application can be applied to the application environment shown in fig. 1. The edge cluster 102 communicates with the cloud server 104 through a network, and the cloud server 104 may also be connected to an electrical device. The data storage system may store data that cloud server 104 needs to process. The data storage system may be integrated on the cloud server 104 or may be placed on another network server. The cloud server 104, the edge cluster 102 and the power equipment form a cloud-edge cooperative power system, the cloud server 104 may obtain a request sequence corresponding to the power equipment, determine an application container mirror image based on a plurality of power calculation analysis tasks to be allocated, determine an edge cluster sequence according to a plurality of resource load values corresponding to the plurality of edge clusters 102, and perform cyclic allocation on each sub-request sequence in the request sequence according to a target sub-request sequence at a target position in the request sequence and based on a resource load value of each edge cluster in the edge cluster sequence and a position of each sub-request sequence in the request sequence until all tasks to be allocated are evenly allocated. The edge cluster 102 and the cloud server 104 may be implemented by independent servers or a server cluster composed of a plurality of servers.
In an embodiment, as shown in fig. 2, a task scheduling method in a cloud-edge collaborative power system is provided, which is described by taking an example that the method is applied to a cloud server in fig. 1, and includes the following steps:
step S202, acquiring a request sequence corresponding to the power equipment in the cloud-edge cooperative power system; the request sequence comprises a plurality of sub-request sequences; each sub-request sequence comprises a preset number of power calculation analysis task requests to be distributed; and determining the application container mirror image according to the plurality of power calculation and analysis task requests to be distributed.
The cloud-edge collaborative power system may include power devices, an edge cluster, and a cloud server, where the power devices in the system may generate corresponding analysis and computation tasks, and the cloud server needs to schedule and allocate the analysis and computation tasks. The cloud-edge collaborative power system may include a plurality of power devices, each power device may generate one or more analysis and computation tasks, and when a computation and analysis task needs to be performed on a power device, a power computation and analysis task request to be allocated may be sent to the cloud server. The cloud server can obtain a plurality of to-be-distributed power computing and analyzing task requests sent by a plurality of power devices in the cloud-edge collaborative power system, combine the to-be-distributed power computing and analyzing task requests into a request sequence, and group the to-be-distributed power computing and analyzing task requests in the request sequence, so that the to-be-distributed power computing and analyzing task requests in the request sequence are divided into a plurality of sub-request sequences. After the cloud server generates the corresponding request sequence, the corresponding application container mirror image, such as a Docker container mirror image, may be determined based on the computation types of the plurality of power computation analysis tasks to be allocated.
Step S204, acquiring a plurality of resource load values corresponding to a plurality of edge clusters in the cloud-edge collaborative power system, and determining an edge cluster sequence according to the resource load values; the edge cluster sequence comprises a plurality of candidate edge clusters; the plurality of candidate edge clusters in the sequence of edge clusters are ordered based on a magnitude of the resource load value.
The cloud edge collaborative power system comprises a plurality of edge clusters, each edge cluster has a corresponding resource load value, the resource load value of each edge cluster can be determined based on the hardware utilization rate of the edge cluster, and the resource load value is larger when the number of computational analysis tasks processed by the edge clusters is larger. The cloud server can obtain a plurality of resource load values corresponding to the plurality of edge clusters, and determine an edge cluster sequence according to the magnitude of the resource load values. For example, the cloud server determines a plurality of candidate edge clusters from the plurality of edge clusters based on the resource load value, and forms an edge cluster sequence based on the candidate edge clusters. In addition, the cloud server may also rank the plurality of candidate edge clusters in the edge cluster sequence, for example, based on the magnitude of the resource load value of each candidate edge cluster. Therefore, when the power calculation analysis task is distributed, the distribution mode of each task can be determined based on the positions of the candidate edge clusters in the edge cluster sequence.
Step S206, according to the target sub-request sequence of the target position in the request sequence, determining a corresponding target edge cluster in the plurality of candidate edge clusters in the edge cluster sequence, creating a container instance in the target edge cluster according to the application container mirror image, and distributing the target sub-request sequence to the container instance of the corresponding target edge cluster.
Wherein, the request sequence comprises a plurality of sub-request sequences. The cloud server can select a target sub-request sequence from the target sub-request sequences, and allocates corresponding target edge clusters to the power calculation analysis task requests to be allocated in the target sub-request sequence. The cloud server may determine a target edge cluster of the plurality of candidate edge clusters at a corresponding position in the edge cluster sequence according to a target position of the target sub-request sequence in the request sequence. After determining the target edge cluster, the cloud server may create a container instance in the target edge cluster based on the application container mirror image, and allocate a target sub-request sequence to the container instance of the target edge cluster, so that the power calculation analysis task corresponding to each request in the target sub-request sequence may be calculated in the container instance of the target edge cluster. The power system computational analysis tasks are computed, for example, by a Docker container in the target edge cluster.
Step S208, removing the target sub-request sequence from the request sequence, and if the removed request sequence has an unallocated sub-request sequence, generating a new request sequence according to the unallocated sub-request sequence; and returning to the step of obtaining a plurality of resource load values corresponding to a plurality of edge clusters in the cloud-edge coordinated power system until the power calculation and analysis task requests to be distributed in each sub-request sequence in the request sequence are distributed.
The cloud server may detect whether a sub-request sequence which is not allocated exists in the removed request sequence, and if so, the cloud server may generate a new request sequence according to the sub-request sequence which is not allocated, for example, a plurality of power calculation and analysis task requests to be allocated in the sub-request sequence which is not allocated form a new request sequence. And because the target edge cluster is allocated with the computing task, the load resource value of the target edge cluster also changes, so that the cloud server can return to the step of acquiring a plurality of resource load values corresponding to a plurality of edge clusters in the cloud edge coordinated power system based on the new request sequence, recalculate the resource loads of the plurality of edge clusters, determine a new edge cluster sequence, perform next round of allocation based on the new request sequence and the new edge cluster sequence, and end the allocation process until all power computing and analyzing task requests to be allocated are allocated completely. Namely, the cloud server can perform waiting scheduling allocation on a plurality of power computing analysis task requests to be allocated.
According to the task scheduling method in the cloud-edge collaborative power system, a request sequence corresponding to power equipment in the cloud-edge collaborative power system is obtained, an edge cluster sequence is determined according to the sizes of a plurality of resource load values corresponding to a plurality of edge clusters, a target edge cluster is determined from the edge cluster sequence according to a target sub-request sequence of a target position in the request sequence, a container instance is created in the container instance, the target sub-request sequence is distributed to the container instance, if a non-distributed sub-request sequence remains in the request sequence after the target sub-request sequence is removed, a new request sequence is generated based on the non-distributed sub-request sequence, and the step of obtaining the plurality of resource load values corresponding to the plurality of edge clusters in the cloud-edge collaborative power system is returned until power calculation analysis task requests to be distributed in the request sequence are distributed. Compared with the traditional distribution based on the weighted average, the scheme distributes the calculation tasks based on the resource utilization rate of the clusters through the waiting scheduling algorithm, and the task processing efficiency is improved.
In one embodiment, acquiring a request sequence sent by a power device in a cloud-edge coordinated power system includes: receiving a plurality of power calculation analysis task requests to be distributed sent by power equipment in a cloud-edge cooperative power system; sequencing the plurality of power calculation and analysis task requests to be distributed in sequence according to the request time of the plurality of power calculation and analysis task requests to be distributed; calculating the ratio of the number of the analysis task requests to the preset grouping number according to the plurality of power to be distributed, and determining the preset number; the method comprises the steps of grouping a plurality of power calculation analysis task requests to be distributed according to a preset number to obtain a plurality of sub-request sequences, and obtaining request sequences according to the plurality of sub-request sequences.
In this embodiment, the cloud server may classify and combine a plurality of requests sent by the power device into a request sequence. The cloud-edge coordinated power system may send a plurality of power calculation and analysis task requests to be allocated to the cloud server, where request times of the power calculation and analysis task requests may be different, and the cloud server may sort the power calculation and analysis task requests to be allocated according to the request times of the power calculation and analysis task requests to be allocated, for example, sort the power calculation and analysis task requests from morning to evening according to the request times, place the request with the earliest request time first, and place the request with the latest request time last. Therefore, the cloud server can obtain an original request sequence formed by combining a plurality of sequenced power calculation analysis task requests to be distributed. The cloud server can also group a plurality of power computing analysis task requests to be distributed. For example, the cloud server may determine the preset number of each group according to a ratio of the number of the multiple power calculation analysis task requests to be allocated to the preset group number, the cloud server may group the multiple power calculation analysis task requests to be allocated according to the preset number to obtain multiple sub-request sequences, the number of the power calculation analysis task requests to be allocated in each sub-request sequence may be the preset number, and the cloud server may obtain the request sequence according to the multiple sub-request sequences.
In particular toThe power equipment may be power equipment in a scheduling and control system, and the cloud server may sequence, after receiving a plurality of power calculation and analysis task requests to be allocated, initiated by the power equipment, the plurality of power calculation and analysis task requests to be allocated, which are currently generated, in order of request time, so as to obtain an original request sequence: t = { T = { (T) 1 ,T 2 ,…,T Nt In which T is Nt And representing the Nt power calculation and analysis task request to be distributed. The cloud server can select the preset number N from the earliest required calculation 0 I.e. the sub-request sequence described above. Wherein, N 0 The calculation formula of (a) is as follows: n is a radical of 0 ＝[N t /N]. Wherein N is t And calculating the total number of the analysis task requests for the power to be distributed. Namely, the above-mentioned N 0 And calculating the number of the analysis task requests for the power to be distributed in each sub-request sequence. The cloud server can convert the original request sequence into a request sequence composed of task sets, and the form of the request sequence is as follows:
Through the embodiment, the cloud server can divide the plurality of power calculation analysis task requests to be distributed into the plurality of sub-request sequences based on the request time, and combine the plurality of sub-request sequences into the request sequence, so that the cloud server can distribute the power calculation analysis task requests to be distributed in each sub-request sequence based on the request sequence, and the task scheduling efficiency is improved.
In one embodiment, determining an application container image according to a plurality of power calculation analysis task requests to be distributed comprises: and acquiring a corresponding application container mirror image from the application container agent according to the calculation type corresponding to each power calculation analysis task request to be distributed.
In this embodiment, the to-be-distributed power calculation analysis task request has a corresponding calculation type. The cloud server can obtain the computing type corresponding to each power computing and analyzing task request to be distributed, and obtain the corresponding application container mirror image from the application container agent according to the computing type of each power computing and analyzing task request to be distributed. Specifically, the application container mirror image may be a Docker container mirror image. The analysis and calculation tasks in the cloud-side collaborative power scheduling system can be completed through a Docker container, the mirror image of the container can be packaged through a cloud server, both the cloud side and the edge system can operate the container to perform calculation and analysis, the calculation and analysis requests related to power scheduling of the cloud side or the edge are firstly sent to a Docker agent, the Docker agent selects the corresponding Docker container mirror image according to the calculation type of the power calculation and analysis task request, container examples are dynamically created on the distributed server cluster, calculation results are returned after calculation is completed in the container examples of the server cluster, and resources occupied by the container are released.
By the embodiment, the cloud server can determine the corresponding application container mirror image based on the calculation type of the power calculation analysis task request to be distributed, and calculate the power calculation analysis task based on the container instance corresponding to the application container mirror image, so that the scheduling efficiency of the power calculation analysis task is improved.
In one embodiment, acquiring a plurality of resource load values corresponding to a plurality of edge clusters in a cloud-edge coordinated power system, and determining an edge cluster sequence according to the resource load values includes: acquiring at least one of processor utilization rate, memory utilization rate and storage utilization rate corresponding to each edge cluster in the cloud edge collaborative power system, and acquiring a utilization rate threshold corresponding to at least one of processor utilization rate, memory utilization rate and storage utilization rate; obtaining at least one comparison result of at least one of a processor utilization rate, a memory utilization rate and a storage utilization rate corresponding to each edge cluster and a corresponding utilization rate threshold; taking at least one edge cluster with a comparison result smaller than the corresponding utilization rate threshold value as a candidate edge cluster; and sequencing the plurality of candidate edge clusters in an ascending order according to at least one of the utilization rate of a processor, the utilization rate of a memory and the utilization rate of a storage to obtain an edge cluster sequence.
In this embodiment, the cloud server may determine the resource load value of each edge cluster by detecting the hardware utilization rate of the edge cluster. For example, the cloud server may obtain at least one of a processor utilization rate, a memory utilization rate, and a storage utilization rate corresponding to each edge cluster in the cloud edge coordinated power system, and obtain a utilization rate threshold corresponding to the at least one hardware utilization rate. The hardware utilization rates represent resource load values, and the utilization rate threshold corresponding to each hardware utilization rate can be set according to actual conditions. The cloud server may compare the obtained hardware usage rate with a corresponding usage rate threshold value to obtain at least one comparison result. For example, the cloud server compares the processor usage rate with a processor usage rate threshold, compares the memory usage rate with a memory usage rate threshold, and compares the storage usage rate with a storage usage rate threshold, thereby obtaining at least one comparison result. The cloud server may take the edge cluster whose comparison result is less than the corresponding usage threshold as the candidate edge cluster. When a plurality of comparison results exist, the cloud server may determine the edge cluster corresponding to each comparison result as a candidate edge cluster only when it is detected that each comparison result is smaller than the corresponding usage threshold.
Specifically, when the power calculation and analysis task performs calculation, the main used resources are a Central Processing Unit (CPU), a memory and a storage device, so the cloud server may obtain the resource utilization rate of each edge cluster. The cloud server may set a preset utilization threshold for each computing resource, and thus the cloud server may select a candidate edge cluster meeting requirements, that is, the cloud server may regard an edge cluster of which the utilization of each resource is lower than the set threshold as a low-load state, may perform task allocation, and the cloud server may obtain an edge cluster sequence based on the above determination, and represent the obtained edge cluster sequence as D. The judgment formula of each candidate edge cluster can be as follows: a. The i ＝(C i,1 <θ i,1 )∩(C i,2 <θ i,2 )∩(C i,3 <θ i,3 ). Wherein A is i A decision rule for the ith edge cluster; c i,1 The CPU utilization rate of the ith edge cluster; c i,2 The memory usage rate of the ith edge cluster; c i,3 Storage usage for the ith edge cluster; theta i,1 A CPU usage threshold for the ith edge cluster; theta.theta. i,2 A memory usage threshold for the ith edge cluster; theta i,1 A storage usage threshold for the ith edge cluster. That is, the cloud server may use the edge cluster that meets the above-mentioned determination formula as a candidate edge cluster.
After the cloud server obtains the plurality of candidate edge clusters, the plurality of candidate edge clusters may be sorted in an ascending order according to at least one of a processor utilization rate, a memory utilization rate, and a storage utilization rate, so as to obtain an edge cluster sequence. The cloud server ranks the plurality of candidate edge clusters according to the hardware utilization rate selected as the basis for judging the candidate edge clusters. For example, in some embodiments, the hardware usage rate selected as the determination condition may be a processor usage rate and a memory usage rate. The cloud server may obtain an average value of the processor utilization rate and the memory utilization rate, and perform ascending sorting on the plurality of candidate edge clusters according to the size of the average value, for example, sort from small to large according to the size of the average value, to obtain an edge cluster sequence.
Specifically, the determining that a plurality of candidate edge clusters may form a sequence D, the cloud server may count the number of edge clusters included in the sequence D, and set the number to be N, and the cloud server may sort the sequence D. The specific cloud servers can be sorted from small to large according to the average utilization rate of the CPU and the memory. The calculation formula of the average utilization rate is as follows: c i,avr ＝(C i,1 +C i,2 )/2. Wherein C is i,avr The average utilization rate of the CPU and the memory of the ith edge cluster. Therefore, the cloud server can sort the candidate edge clusters according to the average utilization rate to obtain a sequence D' = { S = } 1 ,S 2 ,…,S N I.e. the edge cluster sequence described above. Wherein S 1 ,S 2 ,…,S N For each candidate edge cluster after sorting, and the resource load value of each candidate edge cluster is increased in sequence, namely S 1 Has a minimum resource load value of S N The resource load value of (2) is maximum.
Through the embodiment, the cloud server can determine the candidate edge clusters meeting the requirements based on the hardware utilization rate of each edge cluster, and sequence the candidate edge clusters to obtain the edge cluster sequence. Therefore, the cloud server can distribute the power computing and analyzing task requests to be distributed based on the edge cluster sequence, and the distribution efficiency of the power computing and analyzing tasks is improved.
In one embodiment, determining a corresponding target edge cluster from a plurality of candidate edge clusters in an edge cluster sequence according to a target sub-request sequence of a target location in the request sequence includes: acquiring a first sub-request sequence in the request sequence as a target sub-request sequence; the average request time of the first sub-request sequence is the smallest in the request sequence; acquiring a first candidate edge cluster in the edge cluster sequence as a target edge cluster; the resource load value of the first candidate edge cluster is smallest in the edge cluster sequence.
In this embodiment, the cloud server may perform allocation of the power calculation analysis task request to be allocated based on the request sequence and the edge cluster sequence. The request sequence may include a plurality of sub-request sequences, and the cloud server may obtain a first sub-request sequence in the request sequence as a target sub-request sequence. Since the requests of the power calculation and analysis tasks to be distributed in the request sequence are ordered according to the request time sequence, the average request time in the first sub-request sequence is the minimum in the request sequence. The cloud server may further obtain a first candidate edge cluster in the edge clusters as a target edge cluster. Since each candidate edge cluster in the edge cluster sequence is sorted in ascending order according to the resource load value, the resource load value of the first candidate edge cluster is the smallest. The cloud server can allocate the plurality of power computing analysis task requests to be allocated in the first sub-request sequence to the target edge cluster.
And after the target sub-request sequence is allocated, the target edge cluster can allocate computing resources such as a CPU and a memory for the requested task, the resource load value of the target edge cluster changes correspondingly, the cloud server can remove the allocated target sub-request sequence from the request sequence and return to the step of determining the edge cluster sequence, reorder the low-load edge cluster, and determine a new request sequence based on the remaining power calculation and analysis task requests to be allocated, thereby allocating the remaining power calculation and analysis tasks to be allocated until all the tasks in the request sequence are allocated.
Through the embodiment, the cloud server can allocate the target edge cluster with the lowest current resource load value to the target sub-request sequence with the earlier request time based on the position of the candidate edge cluster in the edge cluster sequence and the position of the sub-request sequence in the request sequence, so that the scheduling and allocation efficiency of the power computing and analyzing task request is improved.
In one embodiment, as shown in fig. 3, fig. 3 is a schematic flowchart of a task scheduling method in a cloud-edge coordinated power system in another embodiment. In this embodiment, the cloud server may coordinate contradictions between the computation reliability of multiple scheduling time scale requirements and the balance of server resources in the cloud-edge coordinated power scheduling system in a manner as shown in fig. 3. The cloud server can calculate the calculation analysis tasks in the power system through the Docker container, and the cloud-side collaborative system server resources are uniformly managed through the Docker container. The cloud server can use the resource utilization rate as a core and use load balancing as a target, calculate and sequence the resource utilization rates of the servers in the cloud-side cooperative system, group the calculation tasks according to the time labels, sequentially distribute the tasks in the current task group to be distributed to the servers which are sequenced according to the resource utilization rates, and further sequentially perform multi-turn distribution on the tasks until the analysis and calculation tasks are completely completed.
In particular, the cloud server may, for the computing resources mainly related to task processing: CPU, memory and storage, setting a certain threshold value, selectingAnd (3) the candidate edge clusters which meet the requirements, namely the edge clusters with the utilization rates of various resources lower than the set threshold value are considered to be in a low-load state, task allocation can be carried out, and an original edge cluster sequence is obtained. The cloud server can also count the number of edge clusters contained in the original edge cluster sequence, set as N, and rank the candidate edge clusters in the original edge cluster sequence. The cloud server can also sequence the calculation analysis tasks to be distributed generated by the scheduling and control system according to the request time sequence, and selects a fixed number N from the calculation of the earliest request 0 The task set comprises a plurality of original task requests for calculating and analyzing the electric power to be distributed, a sequence mode of converting the plurality of original task requests into the task set, a request sequence comprising a plurality of sub-request sequences is obtained, a first sub-request sequence in the request sequence is distributed to a first edge cluster in the edge cluster sequence, and the distributed task set is withdrawn from the request sequence after the first sub-request sequence in the request sequence is distributed to the first edge cluster in the edge cluster sequence. After the edge cluster allocates computing resources such as a CPU and a memory to the requested task, the cloud server may return to the step of selecting a candidate edge cluster that meets the requirement, reorder the low-load edge cluster, and allocate the remaining task sets in the request sequence until all tasks in the task sequence are allocated.
Through the embodiment, the cloud server distributes the computing tasks through the round-robin scheduling algorithm based on the resource utilization rate of the cluster, and the task processing efficiency is improved. Moreover, through a Docker container mode, the power analysis and calculation task is subjected to repeated waiting distribution by taking the resource utilization rate as the core and the load balance as the target, so that the requirement of large data analysis and calculation required by cooperative control of large-scale charging facilities is met, a better load balance effect of the cloud-side cooperative system server is obtained under the condition of less resource consumption of a distribution algorithm, meanwhile, the reliability of the power system analysis and calculation task is also ensured, and the overall safety and reliability level of the cloud-side cooperative system is improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a task scheduling device in the cloud-edge collaborative power system, which is used for realizing the task scheduling method in the cloud-edge collaborative power system. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so the specific definition provided in the embodiment of the task scheduling device in one or more cloud-edge coordinated power systems provided below may refer to the definition of the task scheduling method in the cloud-edge coordinated power system, which is not described herein again.
In one embodiment, as shown in fig. 4, there is provided a task scheduling apparatus in a cloud-edge coordinated power system, including: a first acquisition module 500, a second acquisition module 502, a distribution module 504, and a return module 506, wherein:
a first obtaining module 500, configured to obtain a request sequence corresponding to a power device in a cloud-edge coordinated power system; the request sequence comprises a plurality of sub-request sequences; each sub-request sequence comprises a preset number of power calculation analysis task requests to be distributed; and determining the application container mirror image according to the plurality of power calculation and analysis task requests to be distributed.
A second obtaining module 502, configured to obtain multiple resource load values corresponding to multiple edge clusters in the cloud-edge coordinated power system, and determine an edge cluster sequence according to the multiple resource load values; the edge cluster sequence comprises a plurality of candidate edge clusters; the plurality of candidate edge clusters in the sequence of edge clusters are ordered based on a magnitude of the resource load value.
An allocating module 504, configured to determine, according to a target sub-request sequence of a target position in the request sequence, a corresponding target edge cluster in the multiple candidate edge clusters in the edge cluster sequence, create a container instance in the target edge cluster according to the application container mirror image, and allocate the target sub-request sequence to the container instance of the corresponding target edge cluster.
A returning module 506, configured to remove the target sub-request sequence from the request sequence, and if an unallocated sub-request sequence exists in the removed request sequence, generate a new request sequence according to the unallocated sub-request sequence; and returning to the step of obtaining a plurality of resource load values corresponding to a plurality of edge clusters in the cloud-edge coordinated power system until the power calculation and analysis task requests to be distributed in each sub-request sequence in the request sequence are distributed.
In an embodiment, the first obtaining module 500 is specifically configured to receive a plurality of to-be-allocated power calculation analysis task requests sent by power devices in a cloud-edge coordinated power system; sequencing the plurality of power calculation and analysis task requests to be distributed in sequence according to the request time of the plurality of power calculation and analysis task requests to be distributed; calculating the ratio of the number of the analysis task requests to the preset grouping number according to the plurality of power to be distributed, and determining the preset number; the method comprises the steps of grouping a plurality of power calculation analysis task requests to be distributed according to a preset number to obtain a plurality of sub-request sequences, and obtaining request sequences according to the plurality of sub-request sequences.
In an embodiment, the first obtaining module 500 is specifically configured to obtain, according to a calculation type corresponding to each power calculation analysis task request to be allocated, a corresponding application container mirror image from an application container agent.
In an embodiment, the second obtaining module 502 is specifically configured to obtain at least one of a processor utilization rate, a memory utilization rate, and a storage utilization rate corresponding to each edge cluster in the cloud-edge coordinated power system, and a utilization rate threshold corresponding to at least one of the processor utilization rate, the memory utilization rate, and the storage utilization rate; obtaining at least one comparison result of at least one of processor utilization rate, memory utilization rate and storage utilization rate corresponding to each edge cluster and a corresponding utilization rate threshold; taking at least one edge cluster with a comparison result smaller than the corresponding utilization rate threshold value as a candidate edge cluster; and sequencing the candidate edge clusters in an ascending order according to at least one of the utilization rate of the processor, the utilization rate of the memory and the utilization rate of the storage to obtain an edge cluster sequence.
In an embodiment, the second obtaining module 502 is specifically configured to obtain an average value of a processor utilization rate and a memory utilization rate; and according to the average value, sequencing the candidate edge clusters in an ascending order to obtain an edge cluster sequence.
In an embodiment, the allocating module 504 is specifically configured to obtain a first sub-request sequence in the request sequence as a target sub-request sequence; the average request time of the first sub-request sequence is the smallest in the request sequence; acquiring a first candidate edge cluster in the edge cluster sequence as a target edge cluster; the resource load value of the first candidate edge cluster is smallest in the edge cluster sequence.
All or part of each module in the task scheduling device in the cloud-edge coordinated power system can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a cloud server, and the internal structure thereof may be as shown in fig. 5. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to realize a task scheduling method in the cloud-edge collaborative power system. The display unit of the computer device is used for forming a visual picture and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, which includes a memory and a processor, where the memory stores a computer program, and the processor executes the computer program to implement the task scheduling method in the cloud-side collaborative power system.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and the computer program, when executed by a processor, implements the task scheduling method in the cloud-edge collaborative power system described above.
In one embodiment, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, the method for scheduling tasks in the cloud-edge collaborative power system is implemented.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in the embodiments provided herein can include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the various embodiments provided herein may be, without limitation, general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, or the like.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A task scheduling method in a cloud-edge collaborative power system is characterized by comprising the following steps:
acquiring a request sequence corresponding to power equipment in a cloud-edge cooperative power system; the request sequence comprises a plurality of sub-request sequences; each sub-request sequence comprises a preset number of power calculation analysis task requests to be distributed; determining an application container mirror image according to the plurality of power calculation and analysis task requests to be distributed;
acquiring a plurality of resource load values corresponding to a plurality of edge clusters in the cloud edge collaborative power system, and determining an edge cluster sequence according to the resource load values; the edge cluster sequence comprises a plurality of candidate edge clusters; a plurality of candidate edge clusters in the sequence of edge clusters are ordered based on a magnitude of the resource load value;
determining a corresponding target edge cluster in a plurality of candidate edge clusters in the edge cluster sequence according to a target sub-request sequence of a target position in the request sequence, creating a container instance in the target edge cluster according to the application container mirror image, and distributing the target sub-request sequence to the container instance of the corresponding target edge cluster;
removing the target sub-request sequence from the request sequence, and if an unallocated sub-request sequence exists in the removed request sequence, generating a new request sequence according to the unallocated sub-request sequence; and returning to the step of obtaining a plurality of resource load values corresponding to a plurality of edge clusters in the cloud-edge coordinated power system until the power calculation and analysis task requests to be distributed in each sub-request sequence in the request sequence are distributed.
2. The method according to claim 1, wherein the obtaining of the request sequence sent by the power device in the cloud-edge coordinated power system comprises:
receiving a plurality of power calculation analysis task requests to be distributed sent by power equipment in a cloud-edge collaborative power system;
according to the request time of the plurality of power calculation and analysis task requests to be distributed, sequencing the plurality of power calculation and analysis task requests to be distributed in sequence;
determining the preset number according to the ratio of the number of the plurality of power calculation analysis task requests to be distributed to the preset grouping number;
and grouping the plurality of power calculation analysis task requests to be distributed according to the preset quantity to obtain a plurality of sub-request sequences, and obtaining the request sequences according to the plurality of sub-request sequences.
3. The method of claim 1, wherein determining an application container image from the plurality of power calculation analysis task requests to be allocated comprises:
and acquiring a corresponding application container mirror image from the application container agent according to the calculation type corresponding to each power calculation analysis task request to be distributed.
4. The method according to claim 1, wherein the obtaining a plurality of resource load values corresponding to a plurality of edge clusters in the cloud-edge coordinated power system, and determining an edge cluster sequence according to magnitudes of the plurality of resource load values comprises:
acquiring at least one of processor utilization rate, memory utilization rate and storage utilization rate corresponding to each edge cluster in the cloud edge collaborative power system, and a utilization rate threshold corresponding to at least one of the processor utilization rate, the memory utilization rate and the storage utilization rate;
obtaining at least one comparison result of at least one of a processor utilization rate, a memory utilization rate and a storage utilization rate corresponding to each edge cluster and a corresponding utilization rate threshold;
taking the edge cluster with the at least one comparison result smaller than the corresponding utilization rate threshold value as a candidate edge cluster;
and sequencing the plurality of candidate edge clusters in an ascending order according to at least one of the utilization rate of a processor, the utilization rate of a memory and the utilization rate of a storage to obtain an edge cluster sequence.
5. The method of claim 4, wherein the sorting the candidate edge clusters in ascending order according to at least one of processor utilization, memory utilization, and storage utilization to obtain an edge cluster sequence comprises:
acquiring the average value of the processor utilization rate and the memory utilization rate;
and according to the average value, sequencing the candidate edge clusters in an ascending order to obtain an edge cluster sequence.
6. The method of claim 1, wherein determining a corresponding target edge cluster from a plurality of candidate edge clusters in the edge cluster sequence according to a target sub-request sequence of a target location in the request sequence comprises:
acquiring a first sub-request sequence in the request sequence as a target sub-request sequence; the average request time of the first sub-request sequence is the smallest in the request sequence;
acquiring a first candidate edge cluster in the edge cluster sequence as a target edge cluster; the resource load value of the first candidate edge cluster is smallest in the edge cluster sequence.
7. A task scheduling device in a cloud-edge collaborative power system is characterized by comprising:
the first acquisition module is used for acquiring a request sequence corresponding to the power equipment in the cloud-edge cooperative power system; the request sequence comprises a plurality of sub-request sequences; each sub-request sequence comprises a preset number of power calculation analysis task requests to be distributed; determining an application container mirror image according to the plurality of power calculation and analysis task requests to be distributed;
a second obtaining module, configured to obtain multiple resource load values corresponding to multiple edge clusters in the cloud-edge coordinated power system, and determine an edge cluster sequence according to the multiple resource load values; the edge cluster sequence comprises a plurality of candidate edge clusters; a plurality of candidate edge clusters in the sequence of edge clusters are ordered based on a magnitude of the resource load value;
an allocation module, configured to determine, according to a target sub-request sequence of a target position in the request sequence, a corresponding target edge cluster among multiple candidate edge clusters in the edge cluster sequence, create a container instance in the target edge cluster according to the application container mirror image, and allocate the target sub-request sequence to the container instance of the corresponding target edge cluster;
a returning module, configured to remove the target sub-request sequence from the request sequence, and if an unallocated sub-request sequence exists in the removed request sequence, generate a new request sequence according to the unallocated sub-request sequence; and returning to the step of obtaining a plurality of resource load values corresponding to a plurality of edge clusters in the cloud-edge coordinated power system until the power calculation and analysis task requests to be distributed in each sub-request sequence in the request sequence are distributed.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211586389.3A CN115981843A (en) | 2022-12-09 | 2022-12-09 | Task scheduling method and device in cloud-edge cooperative power system and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211586389.3A CN115981843A (en) | 2022-12-09 | 2022-12-09 | Task scheduling method and device in cloud-edge cooperative power system and computer equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115981843A true CN115981843A (en) | 2023-04-18 |
Family
ID=85960416
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211586389.3A Pending CN115981843A (en) | 2022-12-09 | 2022-12-09 | Task scheduling method and device in cloud-edge cooperative power system and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115981843A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116302581A (en) * | 2023-05-25 | 2023-06-23 | 北京智芯微电子科技有限公司 | Novel intelligent power distribution terminal and system |
-
2022
- 2022-12-09 CN CN202211586389.3A patent/CN115981843A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116302581A (en) * | 2023-05-25 | 2023-06-23 | 北京智芯微电子科技有限公司 | Novel intelligent power distribution terminal and system |
| CN116302581B (en) * | 2023-05-25 | 2023-12-22 | 北京智芯微电子科技有限公司 | Novel intelligent power distribution terminal and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107239336B (en) | Method and device for realizing task scheduling | |
| CN107491341B (en) | A kind of virtual machine distribution method based on particle group optimizing | |
| Gan et al. | Genetic simulated annealing algorithm for task scheduling based on cloud computing environment | |
| CN104142860A (en) | Resource adjusting method and device of application service system | |
| CN103916438B (en) | Cloud testing environment scheduling method and system based on load forecast | |
| CN105471985A (en) | Load balance method, cloud platform computing method and cloud platform | |
| US11496413B2 (en) | Allocating cloud computing resources in a cloud computing environment based on user predictability | |
| CN108427602B (en) | Distributed computing task cooperative scheduling method and device | |
| CN109271232A (en) | A kind of cluster resource distribution method based on cloud computing platform | |
| CN105426241A (en) | Cloud computing data center based unified resource scheduling energy-saving method | |
| CN104881322A (en) | Method and device for dispatching cluster resource based on packing model | |
| WO2015144008A1 (en) | Method and device for allocating physical machine to virtual machine | |
| Li et al. | An effective scheduling strategy based on hypergraph partition in geographically distributed datacenters | |
| CN106202092A (en) | The method and system that data process | |
| Delavar et al. | A synthetic heuristic algorithm for independent task scheduling in cloud systems | |
| CN103677960A (en) | Game resetting method for virtual machines capable of controlling energy consumption | |
| CN115981843A (en) | Task scheduling method and device in cloud-edge cooperative power system and computer equipment | |
| CN108694083B (en) | Data processing method and device for server | |
| CN114461384A (en) | Task execution method and device, computer equipment and storage medium | |
| Guo | Ant colony optimization computing resource allocation algorithm based on cloud computing environment | |
| CN111984392A (en) | Task scheduling method and device, electronic equipment and storage medium | |
| CN116647560A (en) | Method, device, equipment and medium for coordinated optimization control of Internet of things computer clusters | |
| Vigliotti et al. | Energy-efficient virtual machines placement | |
| CN114201306B (en) | Multi-dimensional geographic space entity distribution method and system based on load balancing technology | |
| Raj et al. | Augmenting hierarchical load balancing with intelligence in grid environment |
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 |