Multi-data center load coordinated regulation and control method and device based on information gap decision theory and computer equipment

Abstract

The invention relates to a multi-data center load coordinated regulation and control method and device based on an information gap decision theory and computer equipment. The method comprises the following steps: constructing a data center load optimization model; initializing a data center group of a quasi-collaborative game, and constructing a multi-data center load collaborative optimization model; establishing a multi-data center cost allocation model based on the improved Sharpley value; and according to the data center load optimization model, the multi-data center load collaborative optimization model and the improved Shapley value multi-data center cost allocation model, establishing a multi-data center collaborative regulation and control model based on an information gap decision theory. By adopting the method, efficient collaboration of computing resources can be realized, reasonable benefit distribution is ensured, and meanwhile, the method has the capability of coping with sudden computing requirements.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device and computer equipment for coordinated load control of multiple data centers based on information gap decision theory. Background Art

[0002] With the implementation of the "East Data West Computing" strategy, investment in infrastructure such as data centers has been increased, aiming to adjust the computing power layout, guide the computing demand in the east to the western region, and make full use of the abundant renewable energy in the west, such as wind, hydro and solar energy. This strategy is not only beneficial to western power generation companies, but also reduces the dependence of eastern data centers on fossil energy. At the same time, by synergistically transferring computing demand and electricity demand from the east to the west, it can effectively avoid the need to invest in expensive ultra-high voltage transmission lines in the east due to the large amount of electricity demand brought by high-density data centers, thereby saving construction and operation and maintenance costs. In addition, this computing-electricity synergy model can also help improve the overall energy efficiency of data centers. By transferring computing demand in space, it can maximize the use of various high-efficiency computing resources and further reduce energy consumption.

[0003] However, in actual applications, it is difficult to achieve efficient collaboration because data centers belong to different operators and computing resources are not shared. Although data center operators can promote resource sharing through collaboration, they still face problems such as profit distribution, and the existence of sudden computing needs brings uncertainty to resource sharing. Summary of the invention

[0004] Based on this, it is necessary to provide a multi-data center load collaborative control method, device and computer equipment based on information gap decision theory to address the above technical problems.

[0005] In a first aspect, the present application provides a multi-data center load collaborative control method based on information gap decision theory, the method comprising:

[0006] Construct a data center load optimization model; the data center load optimization model is used to characterize the energy consumption of various equipment in each data center;

[0007] Initialize the data center group for collaborative game and build a multi-data center load collaborative optimization model; the data center group for collaborative game is a cluster of data centers of each operator that participate in resource allocation and energy consumption; the multi-data center load collaborative optimization model represents a model for optimizing resource allocation and energy consumption when multiple data centers work together;

[0008] Establish a multi-data center cost sharing model based on improved Shapley value;

[0009] According to the data center load optimization model, the multi-data center load collaborative optimization model and the improved Shapley value multi-data center cost sharing model, a multi-data center collaborative control model based on information gap decision theory is established; the multi-data center collaborative control model based on information gap decision theory is used to optimize resource allocation and energy consumption management when multiple data centers work together.

[0010] In one embodiment, building a data center load optimization model includes:

[0011] Extracting key parameters of data center load; key parameters of data center load include: regional data center set, idle power of servers in the data center, peak power of servers in the data center, set of all servers in the data center, time interval and time set of a day;

[0012] Based on the key parameters of data center load, a data center load pressure drop model is constructed;

[0013] Based on the key parameters of data center load and the data center load pressure drop model, a data center load optimization model is constructed.

[0014] In one embodiment, a data center load pressure drop model is constructed based on key data center load parameters, including:

[0015] Based on the key parameters of data center load, build server energy consumption optimization model, temperature control equipment energy consumption optimization model and power distribution system energy consumption model; the key parameters of data center load include: regional data center collection , Server Idle Power in Data Centers , Peak power of servers in data centers , Data Center A collection of all servers , time interval and time of day collection ;

[0016] Among them, the server energy consumption optimization model is:

[0017]

[0018] in, For Data Center In the period Energy consumption generated by all servers Indicates data center No. Servers in the period Whether it is in the on state, if it is on, it is 1, otherwise it is 0; and is an auxiliary variable; Indicates data center No. Servers in the period The CPU utilization of the virtual machine; CPU utilization thresholds set for data center servers; It is the maximum CPU utilization rate that the data center server is allowed to overclock to reduce the overall energy consumption; is a maximum value; Indicates data center A collection of servers;

[0019] The energy consumption optimization model of temperature control equipment is:

[0020]

[0021] in, For Data Center In the period Energy consumption of temperature control equipment; is the cooling efficiency function of the data center; Indicates data center Refrigeration equipment in the period Supply air temperature; is the heat distribution coefficient of the data center room; It is a data center Total number of servers;

[0022] The energy consumption model of the power distribution system is:

[0023]

[0024] in, is a coefficient, representing the ratio between the energy consumption of the power distribution system and the energy consumption of the server;

[0025] Construct a data center load drop model based on the server energy consumption optimization model, temperature control equipment energy consumption optimization model, and power distribution system energy consumption model;

[0026] The data center load drop model is:

[0027]

[0028] in, Indicates data center In the period The energy consumption generated.

[0029] In one embodiment, a data center group to be used for collaborative gaming is initialized to construct a multi-data center load collaborative optimization model, including:

[0030] by represents the data center cluster of the proposed collaborative game, where , For collection Including data centers all subsets of ;

[0031] Construct computing demand statistical models, virtual machine resource allocation models, virtual machine bandwidth demand constraint models, and data center energy cost models;

[0032] Among them, the calculation demand statistical model is:

[0033]

[0034] in, For Data Center IT users In the period To time period The calculation delay sequence of Represents IT users In the period The minimum delay of calculation; For time period Statistical intervals about IT users within; Represents the set of classified user groups; is the number of user groups after classification, ( ) indicates the Level user, also refers to user group ; Indicates The upper limit of computing delay for users of level 1 The computing delay of the user level shall not exceed ; The interval used to calculate latency ratings for IT users; Indicates data center A collection of IT users; Indicates user group In the period The CPU utilization of the virtual machine; Indicates user group In the period The CPU utilization of virtual machines with uncertain computing needs; Indicates time period Data Center IT users Whether to include in the user group ; Indicates data center Users In the period The CPU utilization of the virtual machine; Indicates data center Users In the period The CPU utilization of virtual machines due to uncertain computing demand;

[0035] The virtual machine resource allocation model is:

[0036]

[0037] in, It is the time period Virtual machine resources that are temporarily occupied due to random computing needs of IT users; Indicates data center In the period Virtual machine resources that are temporarily allocated to meet random computing needs of IT users; Data Center In the period The number of servers enabled, It is a data center Total number of servers;

[0038] The virtual machine resource bandwidth demand constraint model is:

[0039]

[0040] in, For Data Center IT users From the period To time period The bandwidth requirement sequence of Represents IT users In the period Maximum bandwidth requirement; Indicates data center Server The maximum bandwidth that can be provided; Indicates user group bandwidth requirements; Indicates user group In the period Bandwidth requirements for random computing needs;

[0041] The energy cost model of a data center is:

[0042]

[0043] in, Indicates data center In the period The energy cost, Indicates data center In the period The electricity price, Indicates data center In the period The unit incentives that can be obtained by participating in demand response to reduce load, Indicates data center In the period Baseline load; Represents a collection of data centers All data centers in the period Total energy cost;

[0044] Based on the computing demand statistical model, virtual machine resource allocation model, virtual machine bandwidth demand constraint model and data center energy cost model, a multi-data center load collaborative optimization model is constructed.

[0045] In one embodiment, a multi-data center cost allocation model based on an improved Shapley value includes: an optimal cost function of a data center, a multi-data center Shapley value model, an improved Shapley value model considering uncertainty in computing demand, a data center energy cost model based on Shapley value allocation, a modified data center energy cost model, an optimal energy cost model for a single data center, and an adaptive cost correction model;

[0046] Among them, the optimal cost function of the data center is:

[0047]

[0048] in, is the optimal energy cost function, and the input is , the objective function is to minimize the set Data centers within the period Total cost;

[0049] The multi-data center Shapley value model is:

[0050]

[0051] in, For Data Center In the period The Shapley value of is the auxiliary variable introduced; Representing a collection The number of data centers, Representing a collection Number of data centers;

[0052] The improved Shapley value model considering the uncertainty of computing demand is:

[0053]

[0054] in, For Data Center In the period The modified Shapley value of is an auxiliary variable;

[0055] The data center energy cost model based on Shapley value allocation is:

[0056]

[0057] in, Indicates data center In the period Energy cost after allocation based on Shapley value;

[0058] The revised data center energy cost model is:

[0059]

[0060] in, For Data Center In the period Energy cost based on modified Shapley values;

[0061] The optimal energy cost model for a single data center is:

[0062]

[0063] in, Data Center In the period The optimal energy cost without participating in the collaborative game is ;

[0064] The adaptive cost correction model is:

[0065]

[0066] Determine the data center set Each data center in Corresponding Is it established? If it is established, each data center In the period The energy cost is , if not, each data center In the period The energy cost is .

[0067] In one embodiment, according to the data center load optimization model, the multi-data center load collaborative optimization model and the improved Shapley value multi-data center cost sharing model, a multi-data center collaborative control model based on information gap decision theory is established, including:

[0068] Sure When it is 0 ;

[0069] based on Solving for Data Center Sets All data centers in the period The total energy cost is used to construct a cost constraint model based on the information gap decision theory. The cost constraint model is:

[0070]

[0071] in, is the information gap decision theory coefficient, the initialization value of t is 0, and t is assigned according to t=t+1;

[0072] make As variables, the optimization objective of the data center under the information gap decision theory is constructed; the optimization objective of the data center is:

[0073]

[0074] The optimal solution for the data center optimization objective is solved to determine the parameters of the following models and functions corresponding to the optimal solution:

[0075] Server energy consumption optimization model, temperature control equipment energy consumption optimization model, power distribution system energy consumption model, computing demand statistical model, virtual machine resource allocation model, virtual machine bandwidth demand constraint model, data center energy cost model, data center optimal cost function, multi-data center Shapley value model, improved Shapley value model considering computing demand uncertainty, data center energy cost model based on Shapley value allocation, corrected data center energy cost model, single data center optimal energy cost model, adaptive cost correction model;

[0076] Output the coordinated control strategy of each data center based on the parameters of the model and function. The strategy includes , , , , and .

[0077] In a second aspect, the present application also provides a multi-data center load collaborative control device based on information gap decision theory, the device comprising:

[0078] A data center load optimization model building module is used to build a data center load optimization model; the data center load optimization model is used to characterize the energy consumption of various devices in each data center;

[0079] The multi-data center load collaborative optimization model construction module is used to initialize the data center group to be collaboratively played and to construct the multi-data center load collaborative optimization model; the data center group to be collaboratively played is a cluster of data centers of each operator that participate in resource allocation and energy consumption; the multi-data center load collaborative optimization model represents a model used by multiple data centers to optimize resource allocation and energy consumption when working collaboratively;

[0080] A multi-data center cost sharing model building module is used to build a multi-data center cost sharing model based on improved Shapley value;

[0081] The multi-data center collaborative control model building module is used to establish a multi-data center collaborative control model based on the information gap decision theory according to the data center load optimization model, the multi-data center load collaborative optimization model and the improved Shapley value multi-data center cost sharing model; the multi-data center collaborative control model based on the information gap decision theory is used to optimize resource allocation and energy consumption management when multiple data centers work together.

[0082] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0083] In a fourth aspect, the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0084] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the method in the above embodiment when the computer program is executed by a processor.

[0085] The above-mentioned multi-data center load coordinated control method, device and computer equipment based on information gap decision theory have at least the following beneficial effects:

[0086] By building a data center load optimization model, we can determine the energy consumption of each data center. On this basis, we can build a multi-data center load collaborative optimization model based on computing needs and energy consumption to determine the allocation ratio of computing resources in each data center. We can further build a multi-data center cost sharing model based on the improved Shapley value to determine the cost of each data center corresponding to the computing resource allocation ratio, and ensure reasonable distribution of benefits. Combining the above three models and based on the information gap decision theory, we can build a multi-data center collaborative control model to cope with sudden computing needs, thereby achieving efficient coordination of computing resources and ensuring reasonable distribution of benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0088] Figure 1 This is an application environment diagram of a multi-data center load collaborative control method based on information gap decision theory in one embodiment;

[0089] Figure 2 It is a flowchart of a multi-data center load collaborative control method based on information gap decision theory in one embodiment;

[0090] Figure 3 A schematic diagram of a flow chart of steps for constructing a data center load optimization model in one embodiment;

[0091] Figure 4 A diagram of a multi-data center load collaborative control architecture in one embodiment;

[0092] Figure 5 It is a structural block diagram of a multi-data center load collaborative control device based on information gap decision theory in one embodiment;

[0093] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0095] The multi-data center 102 load coordination control method based on information gap decision theory provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, each data center 102 is connected to the data center 102 load collaborative control system 104, and the data center 102 load collaborative control system 104 is used to build a data center 102 load optimization model, and the data center 102 load optimization model is used to characterize the energy consumption of various devices inside each data center 102; it is also used to initialize the data center 102 group to be coordinated and build a multi-data center 102 load collaborative optimization model. The data center 102 group to be coordinated and is a cluster composed of data centers 102 participating in resource allocation and energy consumption in each operator. The multi-data center 102 load collaborative optimization model characterizes multiple A model for optimizing resource allocation and energy consumption when data centers 102 work collaboratively; in addition, it is also used to establish a multi-data center 102 cost sharing model based on improved Shapley values; the data center 102 load collaborative control system 104 establishes a multi-data center 102 collaborative regulation model based on information gap decision theory according to the data center 102 load optimization model, the multi-data center 102 load collaborative optimization model and the improved Shapley value multi-data center 102 cost sharing model; the multi-data center 102 collaborative regulation model based on information gap decision theory is used to optimize resource allocation and energy consumption management when multiple data centers 102 work collaboratively.

[0096] In an exemplary embodiment, Figure 2 As shown in the figure, a multi-data center load coordination control method based on information gap decision theory is provided. Figure 1 The data center load collaborative control system in the embodiment of the present invention is described as an example, and includes the following steps S202 to S206. Among them:

[0097] S202, constructing a data center load optimization model; the data center load optimization model is used to characterize the energy consumption of various devices in each data center.

[0098] Exemplarily, the operating status data of the internal equipment of each data center (such as servers, temperature control equipment, power distribution system, etc.) are collected and analyzed, including but not limited to indicators such as CPU utilization, memory usage, network traffic, and power consumption. Based on these data, a data center load optimization model is constructed to determine the energy consumption of each data center. The model can refer to a mathematical model, such as a linear programming model or a mixed integer programming model, which takes the energy consumption of the data center as the objective function and considers factors such as equipment load and task scheduling as constraints.

[0099] S204, initialize the data center group for collaborative game and construct a multi-data center load collaborative optimization model; the data center group for collaborative game is a cluster composed of data centers participating in resource allocation and energy consumption among various operators; the multi-data center load collaborative optimization model represents a model for optimizing resource allocation and energy consumption when multiple data centers work collaboratively.

[0100] Exemplarily, the data centers participating in computing collaboration among various operators are identified, and these data centers are regarded as a data center cluster. The operation data of these data centers are collected, including but not limited to the current load level, power consumption, efficiency of the cooling system, network latency, and any other key indicators related to data center performance of each data center, as the basis of the multi-data center load collaborative optimization model. Based on the collected data and actual computing needs, a multi-data center load collaborative optimization model is constructed to determine virtual machine resource allocation, virtual machine bandwidth requirements, and data center energy costs.

[0101] S206, establishing a multi-data center cost sharing model based on improved Shapley value.

[0102] Exemplarily, Shapley value is a method for fairly distributing benefits or costs in a cooperative game. In this step, an improved Shapley value algorithm is designed to fairly distribute the cost savings or increased costs of each data center due to participating in collaborative optimization. Specifically, a multi-data center cost sharing model based on improved Shapley value can be constructed based on models and parameters such as the optimal cost function of the data center, the Shapley value model of multiple data centers, the improved Shapley value model considering the uncertainty of computing demand, the data center energy cost model based on Shapley value allocation, the modified data center energy cost model, the optimal energy cost model of a single data center, and the adaptive cost correction model.

[0103] S208, according to the data center load optimization model, the multi-data center load collaborative optimization model and the improved Shapley value multi-data center cost sharing model, a multi-data center collaborative control model based on the information gap decision theory is established; the multi-data center collaborative control model based on the information gap decision theory is used to optimize resource allocation and energy consumption management when multiple data centers work together.

[0104] Exemplarily, based on the data center load optimization model, multi-data center load collaborative optimization model and improved Shapley value multi-data center cost sharing model determined in the above steps, the three are combined to establish a multi-data center collaborative control model based on information gap decision theory, so as to achieve efficient collaboration of computing resources of different operators.

[0105] The above-mentioned multi-data center load collaborative control method based on information gap decision theory determines the energy consumption of each data center by constructing a data center load optimization model, and on this basis, according to computing requirements and combined with energy consumption, constructs a multi-data center load collaborative optimization model to determine the allocation ratio of computing resources in each data, and further establishes a multi-data center cost sharing model based on improved Shapley value to determine the cost corresponding to the allocation ratio of computing resources for each data center, so as to ensure reasonable distribution of benefits. The above three types of models are combined, and based on the information gap decision theory, a multi-data center collaborative control model is constructed to cope with sudden computing needs, thereby achieving efficient coordination of various computing resources and ensuring reasonable distribution of benefits.

[0106] In an exemplary embodiment, Figure 3 As shown, a data center load optimization model is constructed, including:

[0107] S302, extracting key parameters of data center load; key parameters of data center load include: regional data center set, idle power of servers in the data center, peak power of servers in the data center, set of all servers in the data center, time interval and time set of a day.

[0108] S304: construct a data center load pressure drop model based on key data center load parameters.

[0109] S306: Construct a data center load optimization model based on the data center load key parameters and the data center load pressure drop model.

[0110] Exemplarily, key parameters of data center load are extracted, including a set of regional data centers, idle power of servers in the data center, peak power of servers, a set of all servers, time intervals, and a set of times of the day; and based on these key parameters, a data center load drop model is constructed to simulate the load changes of the data center at different time points to help predict and control the energy consumption of the data center; further, based on the previously extracted key parameters and the constructed load drop model, a data center load optimization model is further constructed to achieve effective management of internal resources of the data center and optimization of energy consumption by comprehensively considering the energy consumption of the data center, the working status of the server, and time factors.

[0111] In this embodiment, the purpose of energy conservation and emission reduction is achieved by finely managing the load of the data center, reducing unnecessary energy consumption, and improving resource utilization. In addition, by optimizing the load of the data center, the service quality and operating efficiency of the data center can also be improved, ensuring that while meeting business needs, costs are saved to the greatest extent possible and the reliability and stability of the system are improved.

[0112] In an exemplary embodiment, a data center load pressure drop model is constructed based on key data center load parameters, including:

[0113] Based on the key parameters of data center load, build server energy consumption optimization model, temperature control equipment energy consumption optimization model and power distribution system energy consumption model; the key parameters of data center load include: regional data center collection , Server Idle Power in Data Centers , Peak power of servers in data centers , Data Center A collection of all servers , time interval and time of day collection ;

[0114] Among them, the server energy consumption optimization model is:

[0115]

[0116] in, For Data Center In the period Energy consumption generated by all servers Indicates data center No. Servers in the period Whether it is in the on state, if it is on, it is 1, otherwise it is 0; and is an auxiliary variable; Indicates data center No. Servers in the period The CPU utilization of the virtual machine; CPU utilization thresholds set for data center servers; It is the maximum CPU utilization rate that the data center server is allowed to overclock to reduce the overall energy consumption; is a maximum value; Indicates data center A collection of servers;

[0117] The energy consumption optimization model of temperature control equipment is:

[0118]

[0119] in, For Data Center In the period Energy consumption of temperature control equipment; is the cooling efficiency function of the data center; Indicates data center Refrigeration equipment in the period Supply air temperature; is the heat distribution coefficient of the data center room; It is a data center Total number of servers;

[0120] The energy consumption model of the power distribution system is:

[0121]

[0122] in, is a coefficient, representing the ratio between the energy consumption of the power distribution system and the energy consumption of the server;

[0123] Construct a data center load drop model based on the server energy consumption optimization model, temperature control equipment energy consumption optimization model, and power distribution system energy consumption model;

[0124] The data center load drop model is:

[0125]

[0126] in, Indicates data center In the period The energy consumption generated.

[0127] In this embodiment, by constructing a server energy consumption optimization model, a temperature control equipment energy consumption optimization model and a power distribution system energy consumption model based on the key parameters of the data center load, and further integrating these models to construct a data center load drop model, it is possible to achieve refined management and optimization of the energy consumption of the data center in different time periods, which not only helps to accurately control the energy consumption of servers inside the data center, ensuring that energy consumption is minimized while meeting computing needs, but also can further reduce overall energy consumption and improve the energy efficiency ratio of the data center by optimizing the working efficiency of the temperature control equipment and the energy consumption ratio of the power distribution system, thereby achieving the purpose of energy conservation and emission reduction, and through the dynamic adjustment mechanism of the model, it can better cope with changes in computing needs, ensure the efficient operation and service quality of the data center in different time periods, and ultimately achieve significant optimization of the data center in terms of resource utilization and energy consumption management.

[0128] In an exemplary embodiment, a data center group to be used for collaborative gaming is initialized to construct a multi-data center load collaborative optimization model, including:

[0129] by represents the data center cluster of the proposed collaborative game, where , For collection Including data centers all subsets of ;

[0130] Construct computing demand statistical models, virtual machine resource allocation models, virtual machine bandwidth demand constraint models, and data center energy cost models;

[0131] Among them, the calculation demand statistical model is:

[0132]

[0133] in, For Data Center IT users In the period To time period The calculation delay sequence of Represents IT users In the period The minimum delay of calculation; For time period Statistical intervals about IT users within; Represents the set of classified user groups; is the number of user groups after classification, ( ) indicates the Level user, also refers to user group ; Indicates The upper limit of computing delay for users of level 1 The computing delay of the user level shall not exceed ; The interval used to calculate latency ratings for IT users; Indicates data center A collection of IT users; Indicates user group In the period The CPU utilization of the virtual machine; Indicates user group In the period The CPU utilization of virtual machines with uncertain computing needs; Indicates time period Data Center IT users Whether to include in the user group ; Indicates data center Users In the period The CPU utilization of the virtual machine; Indicates data center Users In the period The CPU utilization of virtual machines due to uncertain computing demand;

[0134] The virtual machine resource allocation model is:

[0135]

[0136] in, It is the time period Virtual machine resources that are temporarily occupied due to random computing needs of IT users; Indicates data center In the period Virtual machine resources that are temporarily allocated to meet random computing needs of IT users; Data Center In the period The number of servers enabled, It is a data center Total number of servers;

[0137] The virtual machine resource bandwidth demand constraint model is:

[0138]

[0139] in, For Data Center IT users From the period To time period The bandwidth requirement sequence of Represents IT users In the period Maximum bandwidth requirement; Indicates data center Server The maximum bandwidth that can be provided; Indicates user group bandwidth requirements; Indicates user group In the period Bandwidth requirements for random computing needs;

[0140] The energy cost model of a data center is:

[0141]

[0142] in, Indicates data center In the period The energy cost, Indicates data center In the period The electricity price, Indicates data center In the period The unit incentives that can be obtained by participating in demand response to reduce load, Indicates data center In the period Baseline load; Represents a collection of data centers All data centers in the period Total energy cost;

[0143] Based on the computing demand statistical model, virtual machine resource allocation model, virtual machine bandwidth demand constraint model and data center energy cost model, a multi-data center load collaborative optimization model is constructed.

[0144] In this embodiment, by initializing a data center group for collaborative gaming and constructing a computing demand statistical model, a virtual machine resource allocation model, a virtual machine bandwidth demand constraint model, and a data center energy cost model, a multi-data center load collaborative optimization model is formed, which can accurately count the computing needs of the data center in different time periods, and optimize according to the computing latency requirements of IT users and the allocation of virtual machine resources, while taking into account the constraints of the virtual machine bandwidth requirements to ensure effective resource allocation while meeting the service quality. In addition, by constructing a data center energy cost model, it is possible to comprehensively consider factors such as electricity prices, demand response incentives, and baseline loads to optimize the energy consumption management of the data center, thereby improving resource utilization and reducing operating costs while ensuring that the data center cluster can work efficiently and collaboratively in the face of dynamically changing computing needs, thereby achieving a dual improvement in energy conservation and emission reduction and economic benefits.

[0145] In an exemplary embodiment, a multi-data center cost allocation model based on an improved Shapley value includes: an optimal cost function of a data center, a multi-data center Shapley value model, an improved Shapley value model considering uncertainty in computing demand, a data center energy cost model based on Shapley value allocation, a modified data center energy cost model, an optimal energy cost model for a single data center, and an adaptive cost correction model;

[0146] Among them, the optimal cost function of the data center is:

[0147]

[0148] in, is the optimal energy cost function, and the input is , the objective function is to minimize the set Data centers within the period Total cost;

[0149] The multi-data center Shapley value model is:

[0150]

[0151] in, For Data Center In the period The Shapley value of is the auxiliary variable introduced; Representing a collection The number of data centers, Representing a collection Number of data centers;

[0152] The improved Shapley value model considering the uncertainty of computing demand is:

[0153]

[0154] in, For Data Center In the period The modified Shapley value of is an auxiliary variable;

[0155] The data center energy cost model based on Shapley value allocation is:

[0156]

[0157] in, Indicates data center In the period Energy cost after allocation based on Shapley value;

[0158] The revised data center energy cost model is:

[0159]

[0160] in, For Data Center In the period Energy cost based on modified Shapley values;

[0161] The optimal energy cost model for a single data center is:

[0162]

[0163] in, Data Center In the period The optimal energy cost without participating in the collaborative game is ;

[0164] The adaptive cost correction model is:

[0165]

[0166] Determine the data center set Each data center in Corresponding Is it established? If it is established, each data center In the period The energy cost is , if not, each data center In the period The energy cost is .

[0167] In this embodiment, by constructing a multi-data center cost sharing model based on improved Shapley values, including a data center optimal cost function, a multi-data center Shapley value model, an improved Shapley value model considering computing demand uncertainty, a data center energy cost model based on Shapley value sharing, a corrected data center energy cost model, a single data center optimal energy cost model and an adaptive cost correction model, it is possible to achieve fair and reasonable sharing of the costs incurred by the data center in the collaborative optimization process, ensuring that each data center shares the energy-saving effect or bears additional costs according to the degree of its contribution to the overall optimization, thereby promoting efficient collaboration between data centers and enhancing the stability and flexibility of the system. At the same time, by introducing an improved model of computing demand uncertainty and an adaptive cost correction mechanism, the model's adaptability to actual application scenarios is further improved, ensuring that optimal resource allocation and minimization of energy consumption can be achieved in a dynamically changing environment, and ultimately achieving significant optimization of the data center cluster in terms of energy conservation, emission reduction and cost control.

[0168] In an exemplary embodiment, according to the data center load optimization model, the multi-data center load collaborative optimization model and the improved Shapley value multi-data center cost sharing model, a multi-data center collaborative control model based on information gap decision theory is established, including:

[0169] Sure When it is 0 ;

[0170] based on Solving for Data Center Sets All data centers in the period The total energy cost is used to construct a cost constraint model based on the information gap decision theory. The cost constraint model is:

[0171]

[0172] in, is the information gap decision theory coefficient, the initialization value of t is 0, and t is assigned according to t=t+1;

[0173] make As variables, the optimization objective of the data center under the information gap decision theory is constructed; the optimization objective of the data center is:

[0174]

[0175] The optimal solution for the data center optimization objective is solved to determine the parameters of the following models and functions corresponding to the optimal solution:

[0176] Server energy consumption optimization model, temperature control equipment energy consumption optimization model, power distribution system energy consumption model, computing demand statistical model, virtual machine resource allocation model, virtual machine bandwidth demand constraint model, data center energy cost model, data center optimal cost function, multi-data center Shapley value model, improved Shapley value model considering computing demand uncertainty, data center energy cost model based on Shapley value allocation, corrected data center energy cost model, single data center optimal energy cost model, adaptive cost correction model;

[0177] Output the coordinated control strategy of each data center based on the parameters of the model and function. The strategy includes , , , , and .

[0178] In this embodiment, a multi-data center collaborative control model is established based on the information gap decision theory, and the data center load optimization model, the multi-data center load collaborative optimization model and the improved Shapley value multi-data center cost sharing model are comprehensively considered to determine the information gap decision theory coefficients under the initial conditions, and the total energy cost of the data center set in different time periods is solved by constructing a cost constraint model, and then the data center optimization target is constructed based on the information gap decision theory, and the optimal solution of the optimization target is solved to determine the parameters of each model and function, and finally the collaborative control strategy of each data center is output, thereby realizing the optimization of the data center cluster in resource allocation and energy consumption management, improving the overall energy efficiency ratio, reducing operating costs, and promoting efficient collaboration and sustainable development among data centers.

[0179] In order to more intuitively illustrate the series of model construction involved in the multi-data center load coordination control method based on information gap decision theory of this application, please refer to Figure 4 shown.

[0180] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0181] Based on the same inventive concept, the embodiment of the present application also provides a multi-data center load collaborative control device based on information gap decision theory for implementing the multi-data center load collaborative control method based on information gap decision theory involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the multi-data center load collaborative control device based on information gap decision theory provided below can refer to the limitations of the multi-data center load collaborative control method based on information gap decision theory above, and will not be repeated here.

[0182] In an exemplary embodiment, Figure 5 As shown, a multi-data center load collaborative control device based on information gap decision theory is provided, including: a data center load optimization model construction module 502, a multi-data center load collaborative optimization model construction module 504, a multi-data center cost sharing model construction module 506 and a multi-data center collaborative control model construction module 508, wherein:

[0183] The data center load optimization model building module 502 is used to build a data center load optimization model; the data center load optimization model is used to characterize the energy consumption of various devices in each data center.

[0184] The multi-data center load collaborative optimization model construction module 504 is used to initialize the data center group to be collaboratively played and construct a multi-data center load collaborative optimization model; the data center group to be collaboratively played is a cluster composed of data centers participating in resource allocation and energy consumption among various operators; the multi-data center load collaborative optimization model represents a model used by multiple data centers to optimize resource allocation and energy consumption when working collaboratively.

[0185] The multi-data center cost sharing model building module 506 is used to build a multi-data center cost sharing model based on improved Shapley value.

[0186] The multi-data center collaborative control model construction module 508 is used to establish a multi-data center collaborative control model based on the information gap decision theory according to the data center load optimization model, the multi-data center load collaborative optimization model and the improved Shapley value multi-data center cost sharing model; the multi-data center collaborative control model based on the information gap decision theory is used to optimize resource allocation and energy consumption management when multiple data centers work together.

[0187] In an exemplary embodiment, the data center load optimization model building module 502 further includes:

[0188] The parameter extraction unit is used to extract the key parameters of the data center load; the key parameters of the data center load include: regional data center set, server idle power of the data center, server peak power of the data center, all server sets of the data center, time intervals and time sets of a day.

[0189] The data center load pressure drop model building unit is used to build a data center load pressure drop model based on key data center load parameters.

[0190] The data center load optimization model building unit is used to build a data center load optimization model based on the data center load key parameters and the data center load pressure drop model.

[0191] Each module in the multi-data center load coordination control device based on information gap decision theory can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0192] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store load data and model data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-data center load collaborative control method based on information gap decision theory is implemented.

[0193] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0194] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0195] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0196] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0197] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0198] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0199] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A multi-data center load collaborative control method based on information gap decision theory, characterized in that: The method comprises: Constructing a data center load optimization model; the data center load optimization model is used to characterize the energy consumption of various devices in each data center; Initialize a data center group for collaborative game and construct a multi-data center load collaborative optimization model; the data center group for collaborative game is a cluster of data centers participating in resource allocation and energy consumption in each operator; the multi-data center load collaborative optimization model represents a model for optimizing resource allocation and energy consumption when multiple data centers work collaboratively; Establish a multi-data center cost sharing model based on improved Shapley value; According to the data center load optimization model, the multi-data center load collaborative optimization model and the improved Shapley value multi-data center cost sharing model, a multi-data center collaborative control model based on information gap decision theory is established; the multi-data center collaborative control model based on information gap decision theory is used to optimize resource allocation and energy consumption management when multiple data centers work collaboratively.

2. The method for coordinated load control of multiple data centers based on information gap decision theory according to claim 1 is characterized in that: The constructing of the data center load optimization model includes: Extracting key parameters of data center load; the key parameters of data center load include: regional data center set, idle power of servers in the data center, peak power of servers in the data center, set of all servers in the data center, time interval and time set of a day; Based on the key data center load parameters, construct a data center load pressure drop model; The data center load optimization model is constructed based on the data center load key parameters and the data center load pressure drop model.

3. The method for coordinated load control of multiple data centers based on information gap decision theory according to claim 2 is characterized in that: The step of constructing a data center load pressure drop model based on the data center load key parameters includes: Based on the key parameters of the data center load, a server energy consumption optimization model, a temperature control equipment energy consumption optimization model and a power distribution system energy consumption model are constructed; the key parameters of the data center load include: regional data center set , Server Idle Power in Data Centers , Peak power of servers in data centers , Data Center A collection of all servers , time interval and time of day collection ; Wherein, the server energy consumption optimization model is: in, For Data Center In the period Energy consumption generated by all servers Indicates data center No. Servers in the period Whether it is in the on state, if it is on, it is 1, otherwise it is 0; and is an auxiliary variable; Indicates data center No. Servers in the period The CPU utilization of the virtual machine; CPU utilization thresholds set for data center servers; It is the maximum CPU utilization rate that the data center server is allowed to overclock to reduce the overall energy consumption; is a maximum value; Indicates data center A collection of servers; The energy consumption optimization model of the temperature control equipment is: in, For Data Center In the period Energy consumption of temperature control equipment; is the cooling efficiency function of the data center; Indicates data center Refrigeration equipment in the period Supply air temperature; is the heat distribution coefficient of the data center room; It is a data center Total number of servers; The energy consumption model of the power distribution system is: in, is a coefficient, representing the ratio between the energy consumption of the power distribution system and the energy consumption of the server; Based on the server energy consumption optimization model, the temperature control equipment energy consumption optimization model and the power distribution system energy consumption model, construct the data center load voltage drop model; The data center load drop model is: in, Indicates data center In the period The energy consumption generated.

4. The method for coordinated load control of multiple data centers based on information gap decision theory according to claim 3 is characterized in that: The initialization of the data center group for the quasi-cooperative game and the construction of a multi-data center load collaborative optimization model include: by represents the data center group of the proposed collaborative game, where , For collection Including data centers all subsets of ; Construct computing demand statistical models, virtual machine resource allocation models, virtual machine bandwidth demand constraint models, and data center energy cost models; Wherein, the computing demand statistical model is: in, For Data Center IT users In the period To time period The calculation delay sequence of Represents IT users In the period The minimum delay of calculation; For time period Statistical intervals about IT users within; Represents the set of classified user groups; is the number of user groups after classification, ( ) indicates the Level user, also refers to user group ; Indicates The upper limit of computing delay for users of level 1 The computing delay of the user level shall not exceed ; The interval used to calculate latency ratings for IT users; Indicates data center A collection of IT users; Indicates user group In the period The CPU utilization of the virtual machine; Indicates user group In the period The CPU utilization of virtual machines with uncertain computing needs; Indicates time period Data Center IT users Whether to include in the user group ; Indicates data center Users In the period The CPU utilization of the virtual machine; Indicates data center Users In the period The CPU utilization of virtual machines due to uncertain computing demand; The virtual machine resource allocation model is: in, It is the time period Virtual machine resources that are temporarily occupied due to random computing needs of IT users; Indicates data center In the period Virtual machine resources that are temporarily allocated to meet random computing needs of IT users; Data Center In the period The number of servers enabled, It is a data center Total number of servers; The virtual machine resource bandwidth demand constraint model is: in, For Data Center IT users From the period To time period The bandwidth requirement sequence of Represents IT users In the period Maximum bandwidth requirement; Indicates data center Server The maximum bandwidth that can be provided; Indicates user group bandwidth requirements; Indicates user group In the period Bandwidth requirements for random computing needs; The data center energy cost model is: in, Indicates data center In the period The energy cost, Indicates data center In the period The electricity price, Indicates data center In the period The unit incentives that can be obtained by participating in demand response to reduce load, Indicates data center In the period Baseline load; Represents a collection of data centers All data centers in the period Total energy cost; Based on the computing demand statistical model, the virtual machine resource allocation model, the virtual machine bandwidth demand constraint model and the data center energy cost model, the multi-data center load collaborative optimization model is constructed.

5. The method for coordinated load control of multiple data centers based on information gap decision theory according to claim 4 is characterized in that: The multi-data center cost allocation model based on improved Shapley value includes: data center optimal cost function, multi-data center Shapley value model, improved Shapley value model considering computing demand uncertainty, data center energy cost model based on Shapley value allocation, modified data center energy cost model, single data center optimal energy cost model and adaptive cost correction model; Among them, the optimal cost function of the data center is: in, is the optimal energy cost function, and the input is , the objective function is to minimize the set Data centers within the period Total cost; The multi-data center Shapley value model is: in, For Data Center In the period The Shapley value of is the auxiliary variable introduced; Representing a collection The number of data centers, Representing a collection Number of data centers; The improved Shapley value model considering the uncertainty of computing demand is: in, For Data Center In the period The modified Shapley value of is an auxiliary variable; The data center energy cost model based on Shapley value allocation is: in, Indicates data center In the period Energy cost after allocation based on Shapley value; The modified data center energy cost model is: in, For Data Center In the period Energy cost based on modified Shapley values; The optimal energy cost model for a single data center is: in, Data Center In the period The optimal energy cost without participating in the collaborative game is ; The adaptive cost correction model is: Determine the data center set Each data center in Corresponding Is it true? If it is true, each data center In the period The energy cost is , if not, each data center In the period The energy cost is .

6. The method for coordinated load control of multiple data centers based on information gap decision theory according to claim 5 is characterized in that: The multi-data center collaborative control model based on information gap decision theory is established according to the data center load optimization model, the multi-data center load collaborative optimization model and the improved Shapley value multi-data center cost sharing model, including: Sure When it is 0 ; based on Solving for Data Center Sets All data centers in the period The total energy cost is used to construct a cost constraint model based on the information gap decision theory. The cost constraint model is: in, is the information gap decision theory coefficient, the initialization value of t is 0, and t is assigned according to t=t+1; make As variables, the optimization target of the data center under the information gap decision theory is constructed; the optimization target of the data center is: The optimal solution of the data center optimization objective is solved to determine the parameters of the following models and functions corresponding to the optimal solution: The server energy consumption optimization model, temperature control equipment energy consumption optimization model, power distribution system energy consumption model, computing demand statistical model, virtual machine resource allocation model, virtual machine bandwidth demand constraint model, data center energy cost model, data center optimal cost function, multi-data center Shapley value model, improved Shapley value model considering computing demand uncertainty, data center energy cost model based on Shapley value allocation, modified data center energy cost model, single data center optimal energy cost model, adaptive cost correction model; Based on the parameters of the model and function, the coordinated control strategy of each data center is output, and the strategy includes , , , , and .

7. A multi-data center load collaborative control device based on information gap decision theory, characterized in that: The device comprises: A data center load optimization model building module is used to build a data center load optimization model; the data center load optimization model is used to characterize the energy consumption of various devices in each data center; A multi-data center load collaborative optimization model construction module is used to initialize a data center group to be subjected to collaborative game and to construct a multi-data center load collaborative optimization model; the data center group to be subjected to collaborative game is a cluster of data centers of each operator that participate in resource allocation and energy consumption; the multi-data center load collaborative optimization model represents a model for optimizing resource allocation and energy consumption when multiple data centers work in collaboration; A multi-data center cost sharing model building module is used to build a multi-data center cost sharing model based on improved Shapley value; A multi-data center collaborative control model building module is used to establish a multi-data center collaborative control model based on information gap decision theory according to the data center load optimization model, the multi-data center load collaborative optimization model and the improved Shapley value multi-data center cost sharing model; the multi-data center collaborative control model based on information gap decision theory is used to optimize resource allocation and energy consumption management when multiple data centers work collaboratively.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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