A resource allocation method, device, and system for the integration of energy resources and computing power resources

Abstract

The present disclosure relates to a resource allocation method, apparatus and system for the integration of energy resources and computing power resources. The method includes: splitting the computing power tasks of the first park to obtain a plurality of subtasks; obtaining the computing power information and energy information of the first park and the second park respectively, inputting the plurality of subtasks, computing power information and energy information into a preset resource allocation model, and solving the resource allocation model according to the computing power information and energy information of the first park and the second park respectively according to preset constraint conditions to obtain a resource allocation result under the condition of minimizing the total cost, allocating corresponding computing power resources according to the computing power allocation method corresponding to the subtasks, and allocating corresponding energy resources according to the energy allocation method corresponding to the subtasks. This solution can achieve reasonable allocation of new energy while ensuring the computing power requirements of industrial parks, and avoid waste of new energy.

Description

Technical Field

[0001] The present disclosure relates to the field of cloud computing technology, and in particular, to a resource allocation method, device, and system for integrating energy resources and computing power resources. Background Art

[0002] In the related art, more and more industrial parks use new energy for power supply to reduce dependence on traditional energy. When there is surplus power, due to reasons such as low surplus power and high energy storage costs, power is usually discarded, and traditional energy is used for power supply when the new energy power supply cannot meet the power demand, resulting in waste of resources. In addition, the demand for computing power in industrial parks is also increasing. In order to solve the problem of insufficient computing power, the method of scheduling the computing power resources of other parks is adopted to obtain sufficient computing power. However, the traditional scheduling method only considers the principle of computing power priority and does not consider the change in power demand caused by the change in computing power allocation, resulting in the inability to balance the relationship between power consumption cost and computing power demand and causing unnecessary waste of resources. Summary of the Invention

[0003] To overcome the problems existing in the related art, the present disclosure provides a resource allocation method, device, and system for integrating energy resources and computing power resources.

[0004] According to a first aspect of an embodiment of the present disclosure, a resource allocation method for integrating energy resources and computing power resources is provided, including:

[0005] In response to receiving a computing power task to be executed in a first park, splitting the computing power task to obtain a plurality of subtasks;

[0006] Obtain the computing power information and energy information of the first park and the second park respectively; the second park is a park other than the first park among the plurality of parks sharing resources;

[0007] Input the plurality of subtasks, the computing power information, and the energy information into a preset resource allocation model; the resource allocation model includes a computing power sub-model, an energy consumption sub-model, and a cost sub-model; the computing power sub-model is used to determine the correlation between computing power and energy consumption; the energy consumption sub-model is used to determine the supply-demand relationship of energy; the cost sub-model is used to determine the correlation between cost and energy consumption;

[0008] According to the computing power information and energy information of the first park and the second park respectively, solve the resource allocation model according to preset constraint conditions to obtain a resource allocation result under the condition of minimizing the total cost; the resource allocation result includes the computing power allocation method and energy allocation method corresponding to each subtask in the plurality of subtasks;

[0009] For each subtask, allocate the corresponding computing power resources according to the computing power allocation method corresponding to the subtask, and allocate the corresponding energy resources according to the energy allocation method corresponding to the subtask.

[0010] In some embodiments of the present application, the computing power sub-model is constructed by the following formula:

[0011] In the case where the first Boolean variable ζ = 0, the computing power allocation method is to use the computing power node server in the first park to execute the subtask, and the energy consumption of the computing power node server in the first park for executing the subtask is calculated by the following formula:

[0012] E i,local = P local * T i,local

[0013] where i is the identifier of the subtask, E i,local is the energy consumption of the computing power node server in the first park for executing the subtask, T i,local is the time consumed by the server in the first park for executing the subtask, and P local is the computing power of the server in the first park;

[0014] In the case where the first Boolean variable ζ = 1, the computing power allocation method is to use the second park to execute the subtask, and the energy consumption of the computing power node server in the second park for executing the subtask is calculated by the following formula:

[0015] E i,remote = P i,remote * T i,remote + P i,wait * T i,line

[0016] where E i,remote is the energy consumption of the computing power node server in the second park for executing the subtask, P i,remote is the computing power of the computing power node server for executing the subtask, T i,remote is the time consumed by the computing power node server for executing the subtask, and T i,line is the delay for the first park to transmit the subtask to the second industrial park through the wireless link. ]>

[0017] In some embodiments of the present application, the energy consumption sub-model is constructed by the following formula:

[0018] E i,sum = E i,local; + E i

[0019] where E i,sum is the total energy consumption of the first park, and E iThe daily energy consumption of the first park for calculating the i-th sub-task excluding the computing overhead, E i,local is the energy consumption of the computing power node server in the first park for executing the sub-task;

[0020] In the case of E i,NE_Out +E i,Storage_Out >E i,sum the second Boolean variable λ = 0, and it is determined that the energy allocation method is to supply power for executing the i-th sub-task using the energy of the first park;

[0021] In the case of E i,NE_Out +E i,Storage_Out <E i,sum , the second Boolean variable λ = 1, and the energy allocation method is to supply power for executing the i-th sub-task using the energy of the second park. The power supply amount of the energy of the second park for executing the i-th sub-task is calculated by the following formula:

[0022] E i,Trans_Out = ((E i,NE_Out +E i,Storage_Out ) - E i,sum ) * η local,j

[0023] where E i,Trans_Out is the power supply amount of the energy of the second park for executing the i-th sub-task, E i,Trans_Out is the adjustable surplus charge amount of the first park, E i,NE_Out is the power generation amount of the new energy in the first park, E i,Storage_Out is the discharge amount of the energy storage in the first park, and η local,j is the line loss rate generated from the first park to the j-th second park.

[0024] In some embodiments of the present application, the cost sub-model is constructed by the following formula:

[0025] Cost i = (1 - ζ) * E i,local + ζ * E i,remote - (1 - λ) * α out * E i,Trans_Out + λ * α in * E i,Trans_In

[0026]

[0027] where Cost i is the total energy consumption cost generated by the first park for completing the i-th sub-task, and α out is the power quantity factor output through energy allocation, and αin The power factor input through energy allocation.

[0028] In some embodiments of the present application, the preset constraint conditions include:

[0029] minCost all

[0030]

[0031] where τ is the preset maximum delay, and ζ i is the value corresponding to the computing power allocation method of the i-th sub-task, and λ i is the value corresponding to the energy allocation method of the i-th sub-task.

[0032] In some embodiments of the present application, for each sub-task, allocating the corresponding computing power resources according to the computing power allocation method corresponding to the sub-task includes:

[0033] When there are multiple second parks and the computing power allocation method is to use the second park to execute the sub-task, obtaining the server information of each park; the server information includes the number of servers, logical computing volume, matrix computing volume, and storage space information;

[0034] Determining the target second park according to the server information and the computing power allocation method;

[0035] Controlling the target second park to execute the sub-task.

[0036] According to the second aspect of the embodiments of the present disclosure, there is provided a resource allocation device for integrating energy resources and computing power resources, including:

[0037] A splitting unit, configured to, in response to receiving a computing power task to be executed in the first park, perform splitting processing on the computing power task to obtain a plurality of sub-tasks;

[0038] An obtaining unit, configured to obtain the computing power information and energy information of the first park and the second park respectively; the second park is a park other than the first park among the multiple parks sharing resources;

[0039] An input unit, configured to input the plurality of sub-tasks, the computing power information, and the energy information into a preset resource allocation model; the resource allocation model includes a computing power sub-model, an energy consumption sub-model, and a cost sub-model; the computing power sub-model is used to determine the correlation between computing power and energy consumption; the energy consumption sub-model is used to determine the supply and demand relationship of energy; the cost sub-model is used to determine the correlation between cost and energy consumption;

[0040] A solving unit, configured to solve the resource allocation model according to the computing power information and energy information of the first park and the second park respectively, under a preset constraint condition, to obtain a resource allocation result with minimized total cost; the resource allocation result includes a computing power allocation method and an energy allocation method corresponding to each of the multiple subtasks;

[0041] An allocation unit, configured to, for each subtask, allocate corresponding computing power resources according to the computing power allocation method corresponding to the subtask, and allocate corresponding energy resources according to the energy allocation method corresponding to the subtask.

[0042] According to a third aspect of the embodiments of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the computer program, the method described in any one of the first aspect is implemented.

[0043] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method described in any one of the first aspect is implemented.

[0044] According to a fifth aspect of the embodiments of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method described in any one of the first aspect is implemented.

[0045] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: By splitting the computing power tasks of the first park, multiple subtasks are obtained; the computing power information and energy information of the first park and the second park are obtained, where the second park is a park other than the first park among the multiple parks sharing resources, and the multiple subtasks, computing power information, and energy information are input into a preset resource allocation model, which includes a computing power sub-model, an energy consumption sub-model, and a cost sub-model. According to the computing power information and energy information of the first park and the second park respectively, the resource allocation model is solved under a preset constraint condition to obtain a resource allocation result with minimized total cost. For each subtask, corresponding computing power resources are allocated according to the computing power allocation method corresponding to the subtask, and corresponding energy resources are allocated according to the energy allocation method corresponding to the subtask. By integrating the computing power resources and energy resources of multiple parks, the computing power resource allocation method and energy resource allocation method with the lowest cost are obtained, thereby realizing the reasonable allocation of new energy while ensuring the computing power requirements of the park and avoiding the waste of new energy.

[0046] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments in accordance with the present invention, and are used together with the specification to explain the principles of the present invention.

[0048] Figure 1 It is a flowchart of a resource allocation method for the integration of energy resources and computing power resources shown according to an exemplary embodiment.

[0049] Figure 2 It is a block diagram of a resource allocation device for the integration of energy resources and computing power resources shown according to an exemplary embodiment.

[0050] Figure 3 It is a block diagram of a device for a resource allocation method for the integration of energy resources and computing power resources shown according to an exemplary embodiment. Detailed implementation manners

[0051] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0052] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present disclosure. The singular forms "a" and "the" used in the embodiments of the present disclosure and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0053] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the embodiments of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "when" as used herein can be interpreted as "when" or "while" or "in response to a determination".

[0054] In addition, various forms of processes shown in the embodiments of the present disclosure can be used, reordering, adding or deleting steps. For example, the steps described in the present application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and no limitation is imposed herein.

[0055] In related technologies, more and more industrial parks use new energy for power supply to reduce dependence on traditional energy. When there is surplus electricity, due to reasons such as low surplus electricity and high energy storage costs, the surplus electricity is usually discarded, and traditional energy is used for power supply when the new energy power supply cannot meet the electricity demand, resulting in waste of resources. In addition, the demand for computing power in industrial parks is also increasing. In order to solve the problem of insufficient computing power, the method of scheduling the computing power resources of other parks is adopted to obtain sufficient computing power. However, the traditional scheduling method only considers the principle of computing power priority and does not consider the change in computing power allocation resulting in the change in electricity demand, leading to the inability to balance the relationship between electricity cost and computing power demand and causing unnecessary waste of resources.

[0056] To solve the above problems, the present disclosure provides a resource allocation method, device, and system for integrating energy resources and computing power resources. By splitting the computing power tasks of the first park, multiple subtasks are obtained; the computing power information and energy information of the first park and the second park are obtained, where the second park is the park other than the first park among the multiple parks sharing resources. The multiple subtasks, computing power information, and energy information are input into a preset resource allocation model. The resource allocation model includes a computing power sub-model, an energy consumption sub-model, and a cost sub-model. According to the computing power information and energy information of the first park and the second park respectively, the resource allocation model is solved according to the preset constraint conditions to obtain the resource allocation result under the condition of minimizing the total cost. For each subtask, the corresponding computing power resources are allocated according to the computing power allocation method corresponding to the subtask, and the corresponding energy resources are allocated according to the energy allocation method corresponding to the subtask. By integrating the computing power resources and energy resources of multiple parks and obtaining the computing power resource allocation method and energy resource allocation method with the lowest cost, the reasonable allocation of new energy is realized while ensuring the computing power demand of the park, and the waste of new energy is avoided.

[0057] Figure 1 is a flowchart of a resource allocation method for integrating energy resources and computing power resources shown according to an exemplary embodiment. As Figure 1 shown, it should be noted that the resource allocation method for integrating energy resources and computing power resources in the embodiments of the present application can be applied to a new energy computing power network, and the resource allocation method for integrating energy resources and computing power resources in the embodiments of the present application is applied to a resource allocation device for integrating energy resources and computing power resources. As Figure 1 shown, the method may include the following steps:

[0058] Step 101, in response to receiving the computing power task to be executed by the first park, split the computing power task to obtain multiple subtasks.

[0059] In the embodiments of the present application, the terminal device in the first park can be used to send the computing tasks to be executed to the new energy computing power network. After receiving the computing tasks, the new energy computing power network performs splitting processing on the computing tasks to obtain multiple subtasks.

[0060] For example, the computing task A can be split into {A1, A2, A3,..., A n}, to obtain n subtasks.

[0061] In some embodiments of the present application, the computing task can be to train a deep learning model. There may be a large training dataset in the computing task. The training dataset can be divided into multiple batches, and each batch can be processed in parallel on different servers, that is, multiple subtasks are processed in parallel to accelerate the training process.

[0062] In other embodiments of the present application, the computing task can be to train a neural network model. The neural network model can be divided into multiple layers or modules, and each layer or module is a subtask, and different subtasks can be executed by different servers.

[0063] Step 102, obtain the computing power information and energy information of the first park and the second park respectively.

[0064] Among them, the second park is the park other than the first park among the multiple parks sharing resources.

[0065] In some embodiments of the present application, the computing power information may include the total computing power, the current remaining computing power, and the computing power of the park, and the energy information may include the adjustable charge amount of new energy in the park, the discharge amount of energy storage, and the daily energy consumption.

[0066] It can be understood that in the case where multiple parks participate in resource sharing, the other parks among the multiple parks except the first park that initiates the task execution application are the second parks.

[0067] Step 103, input the multiple subtasks, computing power information, and energy information into a preset resource allocation model.

[0068] Among them, the resource allocation model includes a computing power sub-model, an energy consumption sub-model, and a cost sub-model.

[0069] Among them, the computing power sub-model is used to determine the correlation between computing power and energy consumption; the energy consumption sub-model is used to determine the supply and demand relationship of energy; the cost sub-model is used to determine the correlation between cost and energy consumption.

[0070] It can be understood that by comprehensively considering the computing power factor, energy consumption factor, and cost factor, a resource allocation plan with the lowest cost that meets the computing power demand and energy consumption demand can be obtained.

[0071] In some embodiments of the present application, the computing power sub-model is constructed by the following formula:

[0072] When the first Boolean variable ζ = 0, the computing power allocation method is to use the computing power node server in the first park to execute the subtask, that is, to execute the subtask in this park. The energy consumption of the computing power node server in the first park for executing the subtask is calculated by the following formula:

[0073] E i,local = P local * T i,local

[0074] where i is the identifier of the subtask, E i,local is the energy consumption of the computing power node server in the first park for executing the subtask, T i,local is the time consumed by the server in the first park for executing the subtask, and P local is the computing power of the server in the first park;

[0075] When the first Boolean variable ζ = 1, the computing power allocation method is to use the second park to execute the subtask, that is, to retrieve the computing power of the second park to execute the subtask. The energy consumption of the computing power node server in the second park for executing the subtask can be calculated by the following formula:

[0076] E i,remote = P i,remote * T i,remote + P i,wait * T i,line

[0077] where E i,remote is the energy consumption of the computing power node server in the second park for executing the subtask, P i,remote is the computing power of the computing power node server for executing the subtask, T i,remote is the time consumed by the computing power node server for executing the subtask, and T i,line is the delay for the first park to transmit the subtask to the second industrial park through the wireless link.

[0078] In some embodiments of the present application, the energy consumption sub-model is constructed by the following formula:

[0079] E i,sum = E i,kocal + E i

[0080] where E i,sum is the total energy consumption of the first park, E i is the daily energy consumption of the first park except for the computing overhead when calculating the i-th subtask, and E i,local is the energy consumption of the computing power node server in the first park for executing the subtask.

[0081] In the case of E i,NE_Out +E i,Storage_Out >E i,sum the second Boolean variable λ = 0, and it is determined that the energy allocation method is to use the energy of the first park to supply power for the execution of the i-th sub-task.

[0082] It can be understood that if the new energy power generation in the first park plus the energy storage discharge power can exceed the total load of the park, the excess power can be allocated through the new energy computing power network and transmitted to other parks.

[0083] In the case of E i,NE_Out +E i,Storage_Out <E i,sum , that is, when the power generation within the first park plus the energy storage discharge of the first park cannot meet the total load of the park, the energy allocation method is to use the energy of the second park to supply power for the execution of the i-th sub-task. The power supply for the execution of the i-th sub-task by the energy of the second park is calculated by the following formula:

[0084] E i,Trans_Out = ((E i,NE_Out +E i,Storage_Out ) - E i,sum ) * η local,j

[0085] where E i,Trans_Out is the power supply for the execution of the i-th sub-task by the energy of the second park, E i,Trans_Out is the excess charge that can be allocated in the first park, E i,NE_Out is the power generation of new energy in the first park, E[[ID=4I]] i,Storage_Out is the energy storage discharge of the first park, and η local,j is the line loss rate generated from the first park to the j-th second park.

[0086] In some embodiments of the present application, the cost sub-model is constructed by the following formula:

[0087] Cost i = (1 - ζ) * E i,local + ζ * E i,remote - (1 - λ) * α out * E i,Trans_Out + λ * α in * E i,Trans_In

[0088]

[0089] where Cost i is the total energy consumption cost generated by the first park for completing the i-th sub-task, and α outα is the electricity quantity factor output through energy allocation. in is the electricity quantity factor input through energy allocation.

[0090] Step 104: According to the computing power information and energy information of the first park and the second park respectively, solve the resource allocation model according to the preset constraints to obtain the resource allocation result under the condition of minimizing the total cost.

[0091] Among them, the resource allocation result includes the computing power allocation method and energy allocation method corresponding to each subtask in multiple subtasks.

[0092] In some embodiments of the present application, the preset constraints include:

[0093] minCost all

[0094]

[0095] Among them, τ is the preset maximum delay, and ζ i is the value corresponding to the computing power allocation method of the i-th subtask, and λ i is the value corresponding to the energy allocation method of the i-th subtask.

[0096] It can be understood that the result of solving the resource allocation model includes the values of ζ and λ corresponding to each subtask.

[0097] In one embodiment, a single-objective optimization particle swarm algorithm can be used to solve the resource allocation model. The method of optimizing and solving the resource allocation model by using the particle swarm algorithm is as follows:

[0098] Initialize the parameters of the particle swarm optimization algorithm; including the population number, the maximum number of iterations, the particle dimension, the inertia weight, and the learning factor;

[0099] Determine the structure of the particles, and initialize the positions and velocities of the particles in the particle swarm; use the Newton-Raphson method to perform power flow calculations, calculate the fitness values of each particle, set the current positions of each particle as the individual extreme values, and then evaluate all the particles, that is, select the individual with the smallest fitness value as the global extreme value (global optimal solution), and the particle with the smallest fitness as the particle optimal solution;

[0100] Update the velocities and positions of all particles according to the global optimal solution and the particle optimal solution. If the iteration number is not reached, repeat the above steps. If the iteration number is reached, output the updated positions and velocities of the particles.

[0101] Step 105: For each subtask, allocate the corresponding computing power resources according to the computing power allocation method corresponding to the subtask, and allocate the corresponding energy resources according to the energy allocation method corresponding to the subtask.

[0102] In some embodiments of the present application, for each subtask, corresponding computing power resources are allocated according to the computing power allocation method corresponding to the subtask, which may specifically include the following steps:

[0103] When there are multiple second parks and the computing power allocation method is to use the second park to execute the subtask, obtain the server information of each park; the server information includes the number of servers, logical computing volume, matrix computing volume, and storage space information;

[0104] Determine the target second park according to the server information and the computing power allocation method;

[0105] Control the target second park to execute the subtask.

[0106] In one embodiment, according to the solution set obtained from the solution of the resource allocation model, the most suitable computing power task execution strategy (i.e., the computing power allocation method) at this time is obtained. The solution set may include ζ corresponding to each subtask i , λ i , η local,j , T i,remote , T i,line The value of the parameter, combined with the information of the number of all computing power node servers, logical computing volume, matrix computing volume, and storage space size at this time, performs Pareto solution for each solution in the solution set to obtain the optimal solution set, selects the solution with the lowest task execution delay, and finally performs task allocation and energy allocation according to this set of solutions, that is, it can be determined from the solution set obtained by solving the resource allocation model whether it is necessary to rely on the computing power resources of other parks to execute the subtask, and whether it is necessary to rely on the energy resources of other parks to execute the subtask. On this basis, Pareto solution is used to determine which second park's computing power resources and which park's new energy resources to use to execute the subtask.

[0107] A resource allocation method for the integration of energy resources and computing power resources according to an embodiment of the present application. By splitting the computing power tasks of the first park, multiple subtasks are obtained; the computing power information and energy information of the first park and the second park are obtained respectively. The second park is the park other than the first park among the multiple parks sharing resources. The multiple subtasks, computing power information and energy information are input into a preset resource allocation model. The resource allocation model includes a computing power sub-model, an energy consumption sub-model and a cost sub-model. According to the computing power information and energy information of the first park and the second park respectively, the resource allocation model is solved according to the preset constraint conditions to obtain the resource allocation result under the condition of minimizing the total cost. For each subtask, the corresponding computing power resources are allocated according to the computing power allocation method corresponding to the subtask, and the corresponding energy resources are allocated according to the energy allocation method corresponding to the subtask. By integrating the computing power resources and energy resources of multiple parks, the computing power resource allocation method and energy resource allocation method with the lowest cost are obtained, so as to realize the reasonable allocation of new energy while ensuring the computing power demand of the park and avoid the waste of new energy.

[0108] Figure 2 is a block diagram of a resource allocation device for the integration of energy resources and computing power resources shown according to an exemplary embodiment. Refer to Figure 2 As shown in the figure, the device includes a splitting unit 201, an obtaining unit 202, an input unit 203, a solving unit 204 and a distributing unit 205.

[0109] Among them, the splitting unit 201 is configured to, in response to receiving the computing power tasks to be executed in the first park, split the computing power tasks to obtain multiple subtasks;

[0110] The obtaining unit 202 is configured to obtain the computing power information and energy information of the first park and the second park respectively; the second park is the park other than the first park among the multiple parks sharing resources;

[0111] The input unit 203 is configured to input the multiple subtasks, computing power information and energy information into a preset resource allocation model; the resource allocation model includes a computing power sub-model, an energy consumption sub-model and a cost sub-model; the computing power sub-model is used to determine the correlation between computing power and energy consumption; the energy consumption sub-model is used to determine the supply and demand relationship of energy; the cost sub-model is used to determine the correlation between cost and energy consumption;

[0112] The solving unit 204 is configured to solve the resource allocation model according to the computing power information and energy information of the first park and the second park respectively according to the preset constraint conditions to obtain the resource allocation result under the condition of minimizing the total cost; the resource allocation result includes the computing power allocation method and energy allocation method corresponding to each subtask among the multiple subtasks;

[0113] The allocation unit 205 is configured to allocate corresponding computing power resources for each subtask according to the computing power allocation method corresponding to the subtask, and allocate corresponding energy resources according to the energy allocation method corresponding to the subtask.

[0114] In some embodiments, the computing power sub-model is constructed by the following formula:

[0115] When the first Boolean variable ζ = 0, the computing power allocation method is to use the computing power node server in the first park to execute the subtask. The energy consumption of the computing power node server in the first park for executing the subtask is calculated by the following formula:

[0116] E i,local =P local *T i,local

[0117] where i is the identifier of the subtask, E i,local is the energy consumption of the computing power node server in the first park for executing the subtask, T i,local is the time consumed by the server in the first park for executing the subtask, and P local is the computing power of the server in the first park;

[0118] When the first Boolean variable ζ = 1, the computing power allocation method is to use the second park to execute the subtask. The energy consumption of the computing power node server in the second park for executing the subtask is calculated by the following formula:

[0119] E i,remote =P i,remote *T i,remote +P i,wait *T i,line

[0120] where, E i,remote is the energy consumption of the computing power node server in the second park for executing the subtask, P i,remote is the computing power of the computing power node server for executing the subtask, T i,remote is the time consumed by the computing power node server for executing the subtask, and T i,line is the delay for the first park to transmit the subtask to the second industrial park via the wireless link.

[0121] In some embodiments, the energy consumption sub-model is constructed by the following formula:

[0122] E i,sum =E i,kocal +E i

[0123] where, E i,sum is the total energy consumption of the first park, and E iThe daily energy consumption of the first park for calculating the i-th sub-task other than the computing overhead, E i,local is the energy consumption of the computing power node server in the first park for executing the sub-task;

[0124] In the case of E i,NE_Out +E i,Storage_Out >E i,sum the second Boolean variable λ = 0, and it is determined that the energy allocation method is to use the energy of the first park to supply power for executing the i-th sub-task;

[0125] In the case of E i,NE_Out +E i,Storage_Out <E i,sum , the second Boolean variable λ = 1, and the energy allocation method is to use the energy of the second park to supply power for executing the i-th sub-task. The power supply amount of the energy of the second park for executing the i-th sub-task is calculated by the following formula:

[0126] E i,Trans_Out = ((E i,NE_Out +E i,Storage_Out ) - E i,sum ) * η local,j

[0127] where E i,Trans_Out is the power supply amount of the energy of the second park for executing the i-th sub-task, E i,Trans_Out is the surplus charge amount that can be allocated in the first park, E i,NE_Out is the power generation amount of the new energy in the first park, E i,Storage_Out is the discharge amount of the energy storage in the first park, η local,j is the line loss rate generated from the first park to the j-th second park.

[0128] In some embodiments of the application, the cost sub-model is constructed by the following formula:

[0129] Cost i = (1 - ζ) * E i,local + ζ * E i,remote - (1 - λ) * α out * E i,Trans_Out + λ * α in * E i,Trans_In

[0130]

[0131] where Cost i is the total energy consumption cost generated by the first park for completing the i-th sub-task, α out is the power quantity factor output through energy allocation, α in is the power quantity factor input through energy allocation.

[0132] In some embodiments of the application, the preset constraint conditions include:

[0133] minCost all

[0134]

[0135] where τ is the preset maximum delay, and ζ i is the value corresponding to the computing power allocation method of the i-th subtask, and λ i is the value corresponding to the energy allocation method of the i-th subtask.

[0136] In some embodiments of the application, the allocation unit 205 can specifically be used for:

[0137] When there are multiple second campuses and the computing power allocation method is to use the second campus to execute subtasks, obtain the server information of each campus; the server information includes the number of servers, logical computing volume, matrix computing volume, and storage space information;

[0138] Determine the target second campus according to the server information and the computing power allocation method;

[0139] Control the target second campus to execute the subtask.

[0140] In the embodiments of the present application, the most suitable computing power task execution strategy can be obtained according to the solution set obtained by solving the resource allocation model. The solution set includes a set of ζ i , λ i , η local,j , T i,remote , T i,line parameter values corresponding to each subtask. Combining the information on the number of all computing power node servers, logical computing volume, matrix computing volume, and storage space size at this time, perform Pareto solution on each solution to obtain an optimal solution set, select the solution that can meet the lowest delay, and finally perform task allocation and energy allocation according to this set of solutions. That is, the result of the above data Pareto solution includes the computing power scheduling object and energy scheduling object for each subtask.

[0141] Regarding the device in the above embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0142] The resource allocation device for the integration of energy resources and computing power resources proposed according to the embodiments of the present application splits the computing tasks of the first park to obtain multiple subtasks; obtains the computing power information and energy information of the first park and the second park respectively, where the second park is the park other than the first park among the multiple parks sharing resources, and inputs the multiple subtasks, computing power information and energy information into a preset resource allocation model. The resource allocation model includes a computing power sub-model, an energy consumption sub-model, and a cost sub-model. According to the computing power information and energy information of the first park and the second park respectively, the resource allocation model is solved according to the preset constraint conditions to obtain the resource allocation result under the condition of minimizing the total cost. For each subtask, the corresponding computing power resources are allocated according to the computing power allocation method corresponding to the subtask, and the corresponding energy resources are allocated according to the energy allocation method corresponding to the subtask. By integrating the computing power resources and energy resources of multiple parks, the computing power resource allocation method and energy resource allocation method with the lowest cost are obtained, thus realizing the reasonable allocation of new energy while ensuring the computing power demand of the park and avoiding the waste of new energy.

[0143] Figure 3 It is a block diagram of a device for a resource allocation method for the integration of energy resources and computing power resources shown according to an exemplary embodiment. For example, device 300 may be an electronic device, such as a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0144] Referring to Figure 3 , device 300 may include one or more of the following components: a processing component 302, a memory 304, a power component 306, a multimedia component 308, an audio component 310, an input / output (I / O) interface 312, a sensor component 314, and a communication component 316.

[0145] The processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, telephone calls, data communication, camera operations, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 302 may include one or more modules to facilitate the interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate the interaction between the multimedia component 308 and the processing component 302.

[0146] The memory 304 is configured to store various types of data to support the operation of the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, and the like. The memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk.

[0147] The power component 306 provides power to the various components of the device 300. The power component 306 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the device 300.

[0148] The multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can sense not only the boundaries of the touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 308 includes a front camera and / or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0149] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC) that is configured to receive external audio signals when the device 300 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 304 or transmitted via the communication component 316. In some embodiments, the audio component 310 further includes a speaker for outputting audio signals.

[0150] The I / O interface 312 provides an interface between the processing component 302 and a peripheral interface module, which can be a keyboard, a click wheel, buttons, etc. These buttons can include, but are not limited to: a home button, a volume button, a power button, and a lock button.

[0151] The sensor assembly 314 includes one or more sensors for providing an assessment of the status of the device 300 in various aspects. For example, the sensor assembly 314 can detect the on / off state of the device 300, the relative positioning of components, such as the display and keypad of the device 300. The sensor assembly 314 can also detect a change in the position of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration / deceleration of the device 300, and the temperature change of the device 300. The sensor assembly 314 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0152] The communication component 316 is configured to facilitate communication between the device 300 and other devices in a wired or wireless manner. The device 300 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0153] In an exemplary embodiment, the device 300 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above method.

[0154] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions, such as the memory 304 including instructions, is also provided. The above instructions can be executed by the processor 320 of the device 300 to complete the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0155] In an exemplary embodiment, a computer program product including a computer program is also provided. The computer program implements the above method when executed by the processor 320 of the device 300.

[0156] Other embodiments of the present invention will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention, which follow the general principles of the invention and include known common general knowledge or conventional technical means in the technical field not disclosed in this disclosure. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the invention are pointed out by the following claims.

[0157] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A resource allocation method for the integration of energy resources and computing power resources, characterized in that, Including: In response to receiving a computing power task to be executed in the first park, splitting the computing power task to obtain multiple subtasks; Obtaining the computing power information and energy information of the first park and the second park respectively; the second park is a park other than the first park among multiple parks sharing resources; Inputting the multiple subtasks, the computing power information and the energy information into a preset resource allocation model; the resource allocation model includes a computing power sub-model, an energy consumption sub-model and a cost sub-model; The computing power sub-model is used to determine the correlation between computing power and energy consumption; the energy consumption sub-model is used to determine the supply-demand relationship of energy; the cost sub-model is used to determine the correlation between cost and energy consumption; According to the computing power information and energy information of the first park and the second park respectively, solving the resource allocation model according to preset constraint conditions to obtain a resource allocation result under the condition of minimizing the total cost; the resource allocation result includes the computing power allocation method and energy allocation method corresponding to each subtask in the multiple subtasks; For each subtask, allocating corresponding computing power resources according to the computing power allocation method corresponding to the subtask, and allocating corresponding energy resources according to the energy allocation method corresponding to the subtask; Among them, the computing power sub-model is constructed by the following formula: When the first Boolean variable ζ = 0, the computing power allocation method is to use the computing power node server of the first park to execute the subtask, and calculate the energy consumption of the computing power node server of the first park to execute the subtask through the following formula: where i is the identifier of the subtask, is the energy consumption of the computing power node server in the first park for executing the subtask, is the time consumed by the server in the first park for executing the subtask, is the computing power of the server in the first park; When the first Boolean variable ζ = 1, the computing power allocation method is to use the second park to execute the subtask, and calculate the energy consumption of the computing power node server of the second park to execute the subtask through the following formula: Among them, is the energy consumption of the computing power node server in the second park for executing the subtask, is the computing power of the computing power node server for executing the subtask, is the time consumed by the computing power node server for executing the subtask, is the delay for the first park to transmit the subtask to the second industrial park through the wireless link; Among them, the energy consumption sub-model is constructed by the following formula: Among them, is the total energy consumption of the first park, is the daily energy consumption other than the computing overhead when calculating the i-th sub-task in the first park, is the energy consumption of the computing power node server in the first park to execute the sub-task; At , the second Boolean variable = 0, it is determined that the energy allocation method is to use the energy of the first park to supply power for the execution of the i-th sub-task; , , the second Boolean variable = 1, and the energy allocation method is to supply power for executing the i-th sub-task by using the energy of the second park. The power supply amount of the energy of the second park for executing the i-th sub-task is calculated by the following formula: Among them, is the power supply amount for the energy of the second park to supply power for the execution of the i-th sub-task, is the adjustable surplus charge amount of the first park, is the power generation amount of new energy in the first park, is the discharge amount of energy storage in the first park, is the line loss rate generated from the transmission of the j-th second park from the first park.

2. The resource allocation method for the integration of energy resources and computing power resources according to claim 1, characterized in that The cost sub-model is constructed by the following formula: wherein, is the total energy consumption cost generated for the first park to complete the i-th sub-task, is the power factor output through energy allocation, is the power factor input through energy allocation.

3. The resource allocation method for the integration of energy resources and computing power resources according to claim 2, wherein, The preset constraint conditions include: Among them, is the preset maximum delay, is the value corresponding to the computing power allocation method of the i-th sub-task, is the value corresponding to the energy allocation method of the i-th sub-task.

4. The resource allocation method for the integration of energy resources and computing power resources according to claim 1, wherein For each subtask, allocating corresponding computing power resources according to the computing power allocation method corresponding to the subtask includes: When there are multiple second parks and the computing power allocation method is to use the second park to execute the subtask, obtaining the server information of each park; the server information includes the number of servers, logical operation volume, matrix operation volume and storage space information; Determining the target second park according to the server information and the computing power allocation method; Controlling the target second park to execute the subtask.

5. A resource allocation device for the integration of energy resources and computing power resources, characterized in that, Adopting the method according to any one of claims 1-4, including: A splitting unit, configured to split the computing power task to obtain multiple subtasks in response to receiving a computing power task to be executed in the first park; An obtaining unit, configured to obtain the computing power information and energy information of the first park and the second park respectively; the second park is a park other than the first park among multiple parks sharing resources; An input unit for inputting the multiple subtasks, the computing power information, and the energy information into a preset resource allocation model; the resource allocation model includes a computing power sub-model, an energy consumption sub-model, and a cost sub-model; the computing power sub-model is used to determine the correlation between computing power and energy consumption; the energy consumption sub-model is used to determine the supply-demand relationship of energy; the cost sub-model is used to determine the correlation between cost and energy consumption; A solving unit for solving the resource allocation model according to the computing power information and the energy information of the first park and the second park respectively according to preset constraint conditions to obtain a resource allocation result under the condition of minimizing the total cost; the resource allocation result includes the computing power allocation method and the energy allocation method corresponding to each subtask in the multiple subtasks; An allocation unit for allocating corresponding computing power resources according to the computing power allocation method corresponding to each subtask and allocating corresponding energy resources according to the energy allocation method corresponding to the subtask.

6. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the method described in any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by the processor, implements the method described in any one of claims 1 to 4.

8. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the method described in any one of claims 1 to 4.

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