CN112118135A - Minimum resource configuration method and device for cloud edge cooperative architecture industrial internet platform - Google Patents
Minimum resource configuration method and device for cloud edge cooperative architecture industrial internet platform Download PDFInfo
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- H—ELECTRICITY
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- 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
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- H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
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- 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
- H04L41/0893—Assignment of logical groups to network elements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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Abstract
The application relates to a minimum resource configuration method and device for a cloud-edge collaborative architecture industrial internet platform. The method comprises the following steps: the method comprises the steps of obtaining equipment to be accessed and application to be deployed in a cloud edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and an edge data center to be set. According to the established model, under the condition of a preset task time delay constraint, calculating a configuration scheme which enables the total amount of computing resources of the edge data center to be minimum, and correspondingly setting the edge data center, equipment and application of the cloud edge cooperative architecture industrial internet platform. According to the method and the system, the computing resource configuration model and the task time delay model between the platform and the edge data center are established according to the characteristics of the industrial internet platform, so that the edge data center resource needing to be deployed can be minimized on the premise of ensuring the minimum processing time delay of time delay sensitive signals or data, and the deployment cost of the industrial internet platform with the cloud-side cooperative architecture is reduced.
Description
Technical Field
The application relates to the technical field of industrial internet, in particular to a minimum resource configuration method and device of a cloud-edge cooperative architecture industrial internet platform.
Background
At present, data processing of an industrial internet platform mostly adopts a centralized big data processing mode. The method utilizes the strong computing and storing capacity of the data center, places all data computing and storing processes in the data cloud center, does not need to occupy other computing resources and storing resources, can realize intensive management of resources, and is widely applied. However, with the development of industrial internet platforms, such architecture gradually exposes the following problems: 1) with the continuous improvement of the performance of the edge device and the continuous expansion of the application scene, the edge data is explosively increased, so that the increase speed of the cloud computing capability gradually cannot be matched with the edge data; 2) the data transmission requirements from the edge devices to the cloud center are increased continuously, so that the load capacity of data transmission bandwidth is increased rapidly; 3) industrial scenarios need to be faced with many signals and data that are sensitive to time delay.
For this reason, edge data processing techniques with edge computing as a core have been produced and widely popularized. The edge computing is a distributed open platform which is arranged on the edge side of a network close to a sensor or a data source and integrates the core capabilities of the network, computing, storage and application. Edge computing provides a new ecological mode, and data computing and data interaction capabilities of the system are improved by gathering five types of resources such as network, computing, storage, application, intelligence and the like on the edge side of the network.
At present, the resource allocation method for the industrial internet platform mainly aims at a cloud computing architecture and cannot be directly applied to a cloud-side cooperative architecture. Existing research for edge-oriented computing resource allocation is directed to mobile terminals in mobile communication, and one of the most important optimization targets is energy consumption of the mobile terminals. For the industrial internet, because the terminal and the edge server are both supplied with power by wire, the energy consumption of the terminal is not a main factor influencing the resource allocation. Therefore, the existing resource allocation methods are not suitable for resource allocation of the cloud edge collaborative architecture industrial internet platform.
Disclosure of Invention
Based on this, it is necessary to provide a method and an apparatus for minimum resource configuration of a cloud-edge collaborative architecture industrial internet platform in order to solve the above technical problems.
A minimum resource configuration method of a cloud-edge collaborative architecture industrial Internet platform comprises the following steps:
the method comprises the steps of obtaining equipment to be accessed and application to be deployed in a cloud edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and an edge data center to be set.
And calculating a minimum resource allocation scheme which enables the total amount of the computing resources of the edge data center to be minimum under the preset task time delay constraint condition according to the established computing resource allocation model and the task time delay model.
And according to the minimum resource configuration scheme, an edge data center is arranged in the cloud edge collaborative architecture industrial internet platform, and equipment to be accessed and application to be deployed which correspond to the edge data center are arranged.
In one embodiment, the steps of acquiring a device to be accessed and an application to be deployed in a cloud-edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task delay model among the device to be accessed, the application to be deployed and an edge data center to be set include:
the method comprises the steps of obtaining transmission capability parameters and computing resource parameters of equipment to be accessed in a cloud edge collaborative architecture industrial internet platform and computing resource occupation parameters of applications to be deployed, and obtaining transmission capability parameters and computing resource parameters of an edge data center to be set.
And establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the acquired parameter values.
In one embodiment, the step of establishing a computing resource configuration model and a task delay model among the device to be accessed, the application to be deployed, and the edge data center to be set according to the obtained parameter values includes:
and establishing a computing resource configuration model among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the acquired parameter values.
And calculating task calculation time delay, task transmission time delay and task queuing time delay among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the established calculation resource configuration model to obtain a corresponding task time delay model.
In one embodiment, the step of calculating, according to the established calculation resource allocation model and the task delay model, a minimum resource allocation scheme that minimizes the total amount of calculation resources of the edge data center under a preset task delay constraint condition includes:
and generating a decision vector according to the variable parameters in the established calculation resource configuration model and the task delay model. The variable parameters include: the number of the edge data centers, the equipment to be accessed to the edge data centers, and the applications to be deployed which are deployed in the edge data centers.
And under the preset task time delay constraint condition, calculating the value of the decision vector which enables the total amount of the calculation resources of the edge data center to be minimum to obtain the minimum resource decision vector.
And obtaining a corresponding minimum resource configuration scheme according to the minimum resource decision vector.
In one embodiment, the variable parameters further include task tuples of the application to be deployed, which are deployed in the edge data center.
In one embodiment, under a preset task delay constraint condition, calculating a value of a decision vector that minimizes the total amount of computing resources of the edge data center, and obtaining a minimum resource decision vector includes:
under the condition of a preset task time delay constraint, the decision vector is taken as an individual, the individual fitness is obtained according to the total amount of the computing resources of the edge data center, and the individual with the minimum total amount of the computing resources of the edge data center is obtained by using a genetic algorithm.
In one embodiment, under a preset task time delay constraint condition, taking the decision vector as an individual, obtaining the fitness of the individual according to the total amount of the computing resources of the edge data center, and obtaining the individual with the minimum total amount of the computing resources of the edge data center by using a genetic algorithm comprises the following steps:
under the condition of a preset task time delay constraint, the decision vector is taken as an individual, a population consisting of the individual is generated and updated based on a genetic algorithm, the total amount of computing resources of the edge data center corresponding to the individual in the population is calculated, and the fitness of the individual is obtained.
And obtaining the individual with the highest fitness to obtain the individual with the minimum total amount of computing resources of the edge data center.
A minimum resource configuration device of a cloud-edge collaborative architecture industrial Internet platform, the device comprising:
the minimum resource configuration model establishing module is used for acquiring equipment to be accessed and application to be deployed in the cloud edge collaborative architecture industrial internet platform and establishing a computing resource configuration model and a task delay model among the equipment to be accessed, the application to be deployed and the edge data center to be set.
And the minimum resource allocation scheme generation module is used for calculating a minimum resource allocation scheme which enables the total amount of the computing resources of the edge data center to be minimum under a preset task time delay constraint condition according to the established computing resource allocation model and the task time delay model.
And the minimum resource configuration scheme implementation module is used for setting an edge data center in the cloud edge collaborative architecture industrial internet platform according to the minimum resource configuration scheme, and setting equipment to be accessed and application to be deployed corresponding to the edge data center.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
the method comprises the steps of obtaining equipment to be accessed and application to be deployed in a cloud edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and an edge data center to be set.
And calculating a minimum resource allocation scheme which enables the total amount of the computing resources of the edge data center to be minimum under the preset task time delay constraint condition according to the established computing resource allocation model and the task time delay model.
And according to the minimum resource configuration scheme, an edge data center is arranged in the cloud edge collaborative architecture industrial internet platform, and equipment to be accessed and application to be deployed which correspond to the edge data center are arranged.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
the method comprises the steps of obtaining equipment to be accessed and application to be deployed in a cloud edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and an edge data center to be set.
And calculating a minimum resource allocation scheme which enables the total amount of the computing resources of the edge data center to be minimum under the preset task time delay constraint condition according to the established computing resource allocation model and the task time delay model.
And according to the minimum resource configuration scheme, an edge data center is arranged in the cloud edge collaborative architecture industrial internet platform, and equipment to be accessed and application to be deployed which correspond to the edge data center are arranged.
According to the minimum resource configuration method, the minimum resource configuration device, the computer equipment and the storage medium of the cloud-edge collaborative framework industrial internet platform, the equipment to be accessed and the application to be deployed in the cloud-edge collaborative framework industrial internet platform are obtained, and a computing resource configuration model and a task delay model among the equipment to be accessed, the application to be deployed and the edge data center to be set are established. According to the established model, under the condition of a preset task time delay constraint, a minimum resource allocation scheme for minimizing the total amount of computing resources of the edge data center is calculated, the edge data center of the cloud edge collaborative architecture industrial internet platform, equipment accessed to each edge data center and applications deployed in each edge data center are set according to the scheme. According to the method and the device, the equipment needing to be accessed to the edge data center in the platform, the computing resource configuration model and the task time delay model needing to be deployed between the applications of the edge data center are established according to the characteristics of the industrial internet platform, the method and the device are suitable for resource configuration of various cloud edge cooperative architecture industrial internet platforms, the edge data center resource needing to be deployed can be minimized on the premise of ensuring the minimum processing time delay of time delay sensitive signals or data, and therefore the deployment cost of the industrial internet platform of the cloud edge cooperative architecture is reduced.
Drawings
Fig. 1 is an application scenario diagram of a minimum resource allocation method of a cloud-edge collaborative architecture industrial internet platform in an embodiment;
FIG. 2 is a flowchart illustrating a method for configuring minimum resources of a cloud-edge collaborative architecture industrial Internet platform according to an embodiment;
FIG. 3 is a schematic diagram of a calculation flow of a genetic algorithm in one embodiment;
FIG. 4 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The minimum resource configuration method for the cloud-edge collaborative architecture industrial internet platform can be applied to the application environment shown in fig. 1. The industrial internet platform resource configuration device 102 communicates with the industrial internet platform resource management device 104, and obtains parameter data of a device to be accessed and an application to be deployed of the cloud-edge collaborative architecture industrial internet platform and parameter data of an edge data center to be set from the industrial internet platform resource management device 104. The industrial internet platform resource configuration device 102 may be, but is not limited to, a device that can meet the computing capability requirement of the method provided in the present application, such as various servers, personal computers, and notebook computers, and the industrial internet platform resource management device 104 may be a platform management device in a cloud-side collaborative architecture industrial internet platform, or may be another device that stores parameter data of a device to be accessed and an application to be deployed.
In an embodiment, as shown in fig. 2, a method for minimum resource configuration of a cloud-edge collaborative architecture industrial internet platform is provided, which is described by taking the application of the method to the industrial internet platform resource configuration device 102 in fig. 1 as an example, and includes the following steps:
step 202, acquiring a device to be accessed and an application to be deployed in the cloud-edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task delay model among the device to be accessed, the application to be deployed and an edge data center to be set.
Specifically, when a cloud-edge collaborative architecture industrial internet platform is configured, it is necessary to first know the devices that need to be accessed in the platform and the applications that need to be deployed corresponding to the data processing requirements of the devices. According to the parameters of the equipment to be accessed and the application to be deployed and the equipment parameters of the selectable edge data center, the computing resource providing capacity and the computing resource using requirement of each equipment in the cloud edge collaborative architecture industrial internet platform and the balance relation between the computing resource providing capacity and the computing resource using requirement of each equipment in the cloud edge collaborative architecture industrial internet platform can be obtained, the time delay generated by running the application of each equipment and the transmission delay caused by data transmission between the equipment can be obtained, and therefore a computing resource configuration model and a task time delay model of the platform to be configured are obtained.
And 204, calculating a minimum resource allocation scheme for minimizing the total amount of the computing resources of the edge data center under a preset task time delay constraint condition according to the established computing resource allocation model and the task time delay model.
According to the computing resource allocation model and the task delay model, the total amount of resources required when different numbers and types of edge data centers are set, different devices are accessed to each edge data center, different applications are operated, and the task response delay of each application can be obtained, so that a plurality of computing resource allocation schemes are obtained. According to the requirement of each application on the time sensitivity in the industrial internet platform, a computing resource allocation scheme which has the minimum required computing resource, namely the minimum resource allocation scheme and meets the time sensitivity requirement of the application in the task response delay can be selected from the obtained computing resource allocation schemes.
And step 206, setting an edge data center in the cloud edge collaborative architecture industrial internet platform according to the minimum resource configuration scheme, and setting equipment to be accessed and application to be deployed corresponding to the edge data center.
According to parameters (including computing resource providing capability and data transmission capability) and quantity of the edge data centers in the minimum resource configuration scheme, the types and quantity of equipment accessed to the edge data centers and the types and quantity of applications deployed in the edge data centers, the edge data centers of corresponding types and quantities are set and configured in the cloud side coordination architecture industrial internet platform.
According to the minimum resource configuration method of the cloud-edge cooperative architecture industrial internet platform, according to the characteristics of the industrial internet platform, equipment needing to be accessed to the edge data center in the platform, a computing resource configuration model needing to be deployed between applications of the edge data center and a task delay model are established.
In one embodiment, the steps of acquiring a device to be accessed and an application to be deployed in a cloud-edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task delay model among the device to be accessed, the application to be deployed and an edge data center to be set include:
the method comprises the steps of obtaining transmission capability parameters and computing resource parameters of equipment to be accessed in a cloud edge collaborative architecture industrial internet platform and computing resource occupation parameters of applications to be deployed, and obtaining transmission capability parameters and computing resource parameters of an edge data center to be set.
And establishing a computing resource configuration model among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the acquired parameter values.
And calculating task calculation time delay, task transmission time delay and task queuing time delay among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the established calculation resource configuration model to obtain a corresponding task time delay model.
Specifically, the computing resource parameters include: the computing power of the edge data center and the equipment to be accessed can be used as the running period number C of the CPU in unit timeiExpressing that the total amount of computing resources of the ith edge data center is expressed as
The total amount of computing resources of the ith device is expressed as
The resource occupation parameters of the application to be deployed comprise: computing task tuple of the ith of an application
Wherein
For computing the input data volume of a task, CycleiThe number of CPU cycles required for a single task to execute,
to calculate the amount of output data for a task. The transmission capability parameters include: data transmission rate R between devices/applicationsi。
When a computing resource configuration model is established, at most M edge data centers are allowed to be arranged in a platform according to the specific condition of the platform and the parameters of available edge data centers1,edge2…edgeMEach edge data center has access to NmAn apparatus u1,u2…uNmThe application to be deployed is ai(i ═ 1,2 … L). Computing resources per edge data center in a model
Should satisfy
Wherein
The computing resources allocated for the edge data center for the kth task running on it. The task executed by each terminal should be satisfied
Wherein
Is a computing resource of the terminal.
When a task delay model is established, 3 types of delay are mainly considered:
the first is to calculate the time delay
Wherein C isiThe number of running cycles of a CPU per unit time of equipment (or a data center), namely computing capacity; x is the number ofiIs the amount of tasks assigned to the device (data center).
The second type is transmission delay
Wherein DataiThe amount of data to be transferred for the task to run, RiIs the data transmission rate.
The third type is queuing delay twaitThe queuing delay depends on the specific application setting, and can be solved by using queuing models such as M/M/1, M/M/c and the like.
The embodiment provides a specific implementation mode for establishing a computing resource configuration model and a task delay model in a cloud-edge cooperative architecture industrial internet, and provides a model basis for obtaining a minimum resource configuration scheme based on model computing.
In one embodiment, the step of calculating, according to the established calculation resource allocation model and the task delay model, a minimum resource allocation scheme that minimizes the total amount of calculation resources of the edge data center under a preset task delay constraint condition includes:
and generating a decision vector according to the variable parameters in the established calculation resource configuration model and the task delay model. The variable parameters include: the method comprises the following steps of the number of edge data centers, equipment to be accessed into the edge data centers, applications to be deployed in the edge data centers and task tuples of the applications to be deployed in the edge data centers.
Under the condition of a preset task time delay constraint, the decision vector is taken as an individual, a population consisting of the individual is generated and updated based on a genetic algorithm, the total amount of computing resources of the edge data center corresponding to the individual in the population is calculated, and the fitness of the individual is obtained.
And obtaining the individual with the highest fitness, obtaining the individual with the minimum total amount of computing resources of the edge data center, and obtaining a corresponding minimum resource configuration scheme according to the minimum resource decision vector.
In particular, for applications and tasks in the industrial internet, the minimum delay constraint is obtained according to the delay requirement of the application and the task
Namely, it is
The optimization goal of the minimum resource allocation method of the cloud edge collaborative architecture industrial internet is to minimize the total resources of the edge data center
In this embodiment, a genetic algorithm is used to calculate a minimum resource allocation scheme under the minimum delay constraint:
according to N in the established calculation resource configuration model and task time delay modelgVariable parameter generation decision vector
Will ZgViewed as an individual in the genetic algorithm, then Zi(i＝1,2…Ng) The gene is the gene of the chromosome, and the value space of the gene is determined by the value range of each variable parameter. To explain the generation mode of the decision vector in detail, the types of the variable parameters comprise the number of the edge data centers, the equipment to be accessed to the edge data centers, and the equipment to be deployed and deployed in the edge data centersUsing, as an example, a task tuple of an application to be deployed, which is deployed in an edge data center, the specific variable parameters include: the method comprises the following steps of the number of edge data centers, equipment to be accessed into each edge data center, applications to be deployed which are deployed on each equipment to be accessed, and task tuples included in each application to be deployed. The value range of the variable parameters is constrained by a computing resource configuration model and a task time delay model. Taking each specific variable parameter as a dimension in the decision vector, generating a plurality of decision vectors according to different values of the variable parameters, wherein one decision vector corresponds to one resource allocation scheme.
For each individual ZgThe total amount of computing resources of the corresponding edge data center can be used for measuring the fitness of the individual: closer to the preset optimization goal
The higher the fitness is, otherwise, the smaller the fitness is; or the smaller the total amount of computing resources of the edge data center corresponding to the individual is, the greater the fitness of the edge data center is. And (3) selecting individuals by the genetic algorithm according to the fitness, and selecting excellent individuals by 'winning or losing' of the individuals in the population to obtain a minimum resource allocation scheme of the cloud-edge cooperative architecture industrial internet. As shown in fig. 3, the main operation process is as follows:
1) initial setting: setting evolution algebra t of genetic algorithmgIs 0, and the maximum evolutionary algebra is Tg(ii) a Random generation of MgIndividuals served as the initial population P (0).
2) Individual evaluation: and calculating the fitness of each individual in the population, namely evaluating the difference degree between the individual and the optimization target.
3) Selecting and operating: and selecting excellent individuals from the population according to the preset fitness of each individual, and then transmitting the excellent individuals to the next generation.
4) And (3) cross operation: and randomly pairing the individuals in the population, and exchanging values of partial components in the decision vector corresponding to the individuals according to a preset probability (called cross probability).
5) And (3) mutation operation: for each individual in the population, the value of one or more components in the corresponding decision vector is changed according to a preset probability (called a variation probability).
The embodiment defines the decision vectors corresponding to the resource allocation schemes one to one, obtains the minimum resource allocation scheme according with the optimization target based on the genetic algorithm, can adjust the optimization target of the resource allocation scheme according to the application scene requirement, and enhances the flexibility of the minimum resource allocation method of the cloud-edge cooperative architecture industrial internet platform.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order 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 portion of the steps in fig. 2 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, a minimum resource configuration apparatus of a cloud-edge collaborative architecture industrial internet platform is provided, which includes:
the minimum resource configuration model establishing module is used for acquiring equipment to be accessed and application to be deployed in the cloud edge collaborative architecture industrial internet platform and establishing a computing resource configuration model and a task delay model among the equipment to be accessed, the application to be deployed and the edge data center to be set.
And the minimum resource allocation scheme generation module is used for calculating a minimum resource allocation scheme which enables the total amount of the computing resources of the edge data center to be minimum under a preset task time delay constraint condition according to the established computing resource allocation model and the task time delay model.
And the minimum resource configuration scheme implementation module is used for setting an edge data center in the cloud edge collaborative architecture industrial internet platform according to the minimum resource configuration scheme, and setting equipment to be accessed and application to be deployed corresponding to the edge data center.
In one embodiment, the minimum resource configuration model building module is configured to: the method comprises the steps of obtaining transmission capability parameters and computing resource parameters of equipment to be accessed in a cloud edge collaborative architecture industrial internet platform and computing resource occupation parameters of applications to be deployed, and obtaining transmission capability parameters and computing resource parameters of an edge data center to be set. And establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the acquired parameter values.
In one embodiment, the minimum resource configuration model building module is configured to: and establishing a computing resource configuration model among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the acquired parameter values. And calculating task calculation time delay, task transmission time delay and task queuing time delay among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the established calculation resource configuration model to obtain a corresponding task time delay model.
In one embodiment, the minimum resource configuration scheme generation module is configured to: and generating a decision vector according to the variable parameters in the established calculation resource configuration model and the task delay model. The variable parameters include: the number of the edge data centers, the equipment to be accessed to the edge data centers, and the applications to be deployed which are deployed in the edge data centers. And under the preset task time delay constraint condition, calculating the value of the decision vector which enables the total amount of the calculation resources of the edge data center to be minimum to obtain the minimum resource decision vector. And obtaining a corresponding minimum resource configuration scheme according to the minimum resource decision vector.
In one embodiment, the variable parameters further include task tuples of the application to be deployed, which are deployed in the edge data center.
In one embodiment, the minimum resource configuration scheme generation module is configured to: under the condition of a preset task time delay constraint, the decision vector is taken as an individual, the individual fitness is obtained according to the total amount of the computing resources of the edge data center, and the individual with the minimum total amount of the computing resources of the edge data center is obtained by using a genetic algorithm.
In one embodiment, the minimum resource configuration scheme generation module is configured to: under the condition of a preset task time delay constraint, the decision vector is taken as an individual, a population consisting of the individual is generated and updated based on a genetic algorithm, the total amount of computing resources of the edge data center corresponding to the individual in the population is calculated, and the fitness of the individual is obtained. And obtaining the individual with the highest fitness to obtain the individual with the minimum total amount of computing resources of the edge data center.
For specific limitations of the minimum resource configuration device of the cloud-edge collaborative architecture industrial internet platform, reference may be made to the above limitations of the minimum resource configuration method of the cloud-edge collaborative architecture industrial internet platform, which are not described herein again. Each module in the minimum resource configuration device of the cloud-edge collaborative architecture industrial internet platform can be wholly or partially 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 terminal, and its internal structure diagram may be as shown in fig. 4. The computer device includes a processor, a memory, a network 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 network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a minimum resource allocation method of the cloud edge collaborative architecture industrial internet platform. The display screen of the computer equipment 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. 4 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, there is provided a computer device comprising a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program:
the method comprises the steps of obtaining equipment to be accessed and application to be deployed in a cloud edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and an edge data center to be set.
And calculating a minimum resource allocation scheme which enables the total amount of the computing resources of the edge data center to be minimum under the preset task time delay constraint condition according to the established computing resource allocation model and the task time delay model.
And according to the minimum resource configuration scheme, an edge data center is arranged in the cloud edge collaborative architecture industrial internet platform, and equipment to be accessed and application to be deployed which correspond to the edge data center are arranged.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the method comprises the steps of obtaining transmission capability parameters and computing resource parameters of equipment to be accessed in a cloud edge collaborative architecture industrial internet platform and computing resource occupation parameters of applications to be deployed, and obtaining transmission capability parameters and computing resource parameters of an edge data center to be set. And establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the acquired parameter values.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and establishing a computing resource configuration model among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the acquired parameter values. And calculating task calculation time delay, task transmission time delay and task queuing time delay among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the established calculation resource configuration model to obtain a corresponding task time delay model.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and generating a decision vector according to the variable parameters in the established calculation resource configuration model and the task delay model. The variable parameters include: the number of the edge data centers, the equipment to be accessed to the edge data centers, and the applications to be deployed which are deployed in the edge data centers. And under the preset task time delay constraint condition, calculating the value of the decision vector which enables the total amount of the calculation resources of the edge data center to be minimum to obtain the minimum resource decision vector. And obtaining a corresponding minimum resource configuration scheme according to the minimum resource decision vector.
In one embodiment, the variable parameters further include task tuples in the application to be deployed.
In one embodiment, the processor, when executing the computer program, further performs the steps of: under the condition of a preset task time delay constraint, the decision vector is taken as an individual, the individual fitness is obtained according to the total amount of the computing resources of the edge data center, and the individual with the minimum total amount of the computing resources of the edge data center is obtained by using a genetic algorithm.
In one embodiment, the processor, when executing the computer program, further performs the steps of: under the condition of a preset task time delay constraint, the decision vector is taken as an individual, a population consisting of the individual is generated and updated based on a genetic algorithm, the total amount of computing resources of the edge data center corresponding to the individual in the population is calculated, and the fitness of the individual is obtained. And obtaining the individual with the highest fitness to obtain the individual with the minimum total amount of computing resources of the edge data center.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
the method comprises the steps of obtaining equipment to be accessed and application to be deployed in a cloud edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and an edge data center to be set.
And calculating a minimum resource allocation scheme which enables the total amount of the computing resources of the edge data center to be minimum under the preset task time delay constraint condition according to the established computing resource allocation model and the task time delay model.
And according to the minimum resource configuration scheme, an edge data center is arranged in the cloud edge collaborative architecture industrial internet platform, and equipment to be accessed and application to be deployed which correspond to the edge data center are arranged.
In one embodiment, the computer program when executed by the processor further performs the steps of: the method comprises the steps of obtaining transmission capability parameters and computing resource parameters of equipment to be accessed in a cloud edge collaborative architecture industrial internet platform and computing resource occupation parameters of applications to be deployed, and obtaining transmission capability parameters and computing resource parameters of an edge data center to be set. And establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the acquired parameter values.
In one embodiment, the computer program when executed by the processor further performs the steps of: and establishing a computing resource configuration model among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the acquired parameter values. And calculating task calculation time delay, task transmission time delay and task queuing time delay among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the established calculation resource configuration model to obtain a corresponding task time delay model.
In one embodiment, the computer program when executed by the processor further performs the steps of: and generating a decision vector according to the variable parameters in the established calculation resource configuration model and the task delay model. The variable parameters include: the number of the edge data centers, the equipment to be accessed to the edge data centers, and the applications to be deployed which are deployed in the edge data centers. And under the preset task time delay constraint condition, calculating the value of the decision vector which enables the total amount of the calculation resources of the edge data center to be minimum to obtain the minimum resource decision vector. And obtaining a corresponding minimum resource configuration scheme according to the minimum resource decision vector.
In one embodiment, the variable parameters further include task tuples of the application to be deployed, which are deployed in the edge data center.
In one embodiment, the computer program when executed by the processor further performs the steps of: under the condition of a preset task time delay constraint, the decision vector is taken as an individual, the individual fitness is obtained according to the total amount of the computing resources of the edge data center, and the individual with the minimum total amount of the computing resources of the edge data center is obtained by using a genetic algorithm.
In one embodiment, the computer program when executed by the processor further performs the steps of: under the condition of a preset task time delay constraint, the decision vector is taken as an individual, a population consisting of the individual is generated and updated based on a genetic algorithm, the total amount of computing resources of the edge data center corresponding to the individual in the population is calculated, and the fitness of the individual is obtained. And obtaining the individual with the highest fitness to obtain the individual with the minimum total amount of computing resources of the edge data center.
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, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification 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 invention. 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 patent shall be subject to the appended claims.
Claims (10)
1. A minimum resource configuration method for a cloud edge collaborative architecture industrial Internet, the method comprising:
acquiring equipment to be accessed and application to be deployed in a cloud edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and an edge data center to be set;
according to the computing resource configuration model and the task time delay model, under a preset task time delay constraint condition, computing a minimum resource configuration scheme which enables the total computing resources of the edge data center to be minimum;
and setting the edge data center in the cloud edge cooperative architecture industrial internet platform according to the minimum resource configuration scheme, and setting the equipment to be accessed and the application to be deployed corresponding to the edge data center.
2. The method according to claim 1, wherein the step of obtaining the device to be accessed and the application to be deployed in the cloud-edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task delay model among the device to be accessed, the application to be deployed and the edge data center to be set comprises:
the method comprises the steps of obtaining transmission capacity parameters and computing resource parameters of equipment to be accessed in a cloud edge cooperative architecture industrial internet platform and computing resource occupation parameters of applications to be deployed, and obtaining transmission capacity parameters and computing resource parameters of an edge data center to be set;
and establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the acquired parameter values.
3. The method according to claim 2, wherein the step of establishing a computing resource configuration model and a task delay model among the device to be accessed, the application to be deployed, and the edge data center to be set according to the obtained parameter values comprises:
according to the obtained parameter values, a computing resource configuration model among the equipment to be accessed, the application to be deployed and the edge data center to be set is established;
and calculating task calculation time delay, task transmission time delay and task queuing time delay among the equipment to be accessed, the application to be deployed and the edge data center to be set according to the calculation resource configuration model to obtain a corresponding task time delay model.
4. The method according to claim 1, wherein the step of calculating, according to the computing resource allocation model and the task delay model, a minimum resource allocation scheme that minimizes the total amount of computing resources of the edge data center under a preset task delay constraint condition includes:
generating a decision vector according to the variable parameters in the computing resource configuration model and the task delay model; the variable quantities include: the number of the edge data centers, the equipment to be accessed to the edge data centers, and the applications to be deployed in the edge data centers;
under the constraint condition of preset task time delay, calculating the value of the decision vector which enables the total amount of the calculation resources of the edge data center to be minimum to obtain a minimum resource decision vector;
and obtaining a corresponding minimum resource configuration scheme according to the minimum resource decision vector.
5. The method of claim 4, wherein the variable quantities further comprise task tuples of the application to be deployed at the edge data center.
6. The method according to claim 4, wherein the step of calculating the value of the decision vector that minimizes the total amount of computing resources of the edge data center under the preset task delay constraint condition to obtain a minimum resource decision vector comprises:
and under the condition of a preset task time delay constraint, taking the decision vector as an individual, obtaining the fitness of the individual according to the total amount of the computing resources of the edge data center, and obtaining the individual with the minimum total amount of the computing resources of the edge data center by using a genetic algorithm.
7. The method according to claim 4, wherein under a preset task delay constraint condition, taking the decision vector as an individual, obtaining fitness of the individual according to the total amount of computing resources of the edge data center, and obtaining the individual with the minimum total amount of computing resources of the edge data center by using a genetic algorithm comprises:
under the condition of a preset task time delay constraint, generating and updating a population consisting of individuals by taking the decision vector as the individual based on a genetic algorithm, and calculating the total amount of computing resources of the edge data center corresponding to the individual in the population to obtain the fitness of the individual;
and obtaining the individual with the highest fitness to obtain the individual with the minimum total amount of computing resources of the edge data center.
8. A minimum resource configuration device of a cloud-edge collaborative architecture industrial Internet platform, the device comprising:
the minimum resource configuration model establishing module is used for acquiring equipment to be accessed and application to be deployed in the cloud edge collaborative architecture industrial internet platform, and establishing a computing resource configuration model and a task time delay model among the equipment to be accessed, the application to be deployed and an edge data center to be set;
a minimum resource allocation scheme generation module, configured to calculate, according to the calculation resource allocation model and the task delay model, a minimum resource allocation scheme that minimizes a total amount of calculation resources of the edge data center under a preset task delay constraint condition;
and the minimum resource configuration scheme implementation module is configured to set the edge data center in the cloud-edge collaborative architecture industrial internet platform according to the minimum resource configuration scheme, and set the device to be accessed and the application to be deployed corresponding to the edge data center.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. 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 7.
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