Server power consumption control method, device, equipment, medium and program product

Abstract

The present disclosure provides a server power consumption control method that can be applied to the field of artificial intelligence. The server power consumption control method includes: analyzing the operating status information of each of the multiple servers in the server cluster, determining the belief distribution corresponding to the preset power consumption influencing factors, and the belief distribution is used to characterize the trust level of the degree of influence of the preset power consumption influencing factors on power consumption; fusing the belief distributions of the multiple servers according to the respective source confidences of the multiple servers to obtain comprehensive status information, and the source confidence characterizes the degree of trust in the operating status information in the server; inputting the comprehensive status information into the action selection model, so as to use the action selection model to select the optimization control operation corresponding to the comprehensive status information from multiple power consumption control operations; controlling the server cluster to perform the optimization control operation to adjust the power consumption of multiple servers. The present disclosure also provides a server power consumption control device, equipment, storage medium and program product.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, specifically to the field of server power consumption control technology, and more specifically to a server power consumption control method, apparatus, device, medium, and program product. Background Art

[0002] Server clusters handle massive amounts of data processing, generating significant power consumption during operation. To improve energy efficiency, technologies monitor server operating status in real time. If excessive power consumption is detected, users can adjust the power consumption accordingly using a controller.

[0003] In the process of realizing the concept disclosed herein, the inventors discovered that there are at least the following problems in the related art: due to the limited accuracy of the controller and the fact that users can only perform passive adjustments through the controller during the adjustment process, the adjustment of server power consumption is not timely enough and the efficiency is relatively low. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides a server power consumption control method, apparatus, device, medium and program product.

[0005] According to the first aspect of the present disclosure, a server power consumption control method is provided, comprising: analyzing the operating status information of each of a plurality of servers in a server cluster, determining a belief distribution corresponding to a preset power consumption influencing factor, wherein the belief distribution is used to characterize the trust level of the degree of influence of the preset power consumption influencing factor on power consumption; fusing the belief distributions of the plurality of servers according to their respective source confidences to obtain comprehensive status information, wherein the source confidence characterizes the degree of trust in the operating status information of the servers; inputting the comprehensive status information into an action selection model, so as to select an optimized control operation corresponding to the comprehensive status information from a plurality of power consumption control operations using the action selection model; and controlling the server cluster to execute the optimized control operation to adjust the power consumption of the plurality of servers.

[0006] According to an embodiment of the present disclosure, the above-mentioned analysis of the operating status information of each of the multiple servers in the server cluster to determine the belief distribution corresponding to the preset power consumption influencing factors includes: using a belief distribution function to analyze the operating status information of each of the multiple servers in the above-mentioned server cluster to determine the belief distribution of each of the above-mentioned servers, wherein the above-mentioned belief distribution includes a belief value of each assumption condition corresponding to the above-mentioned belief distribution function, and the above-mentioned assumption condition is obtained by combining multiple of the above-mentioned preset power consumption influencing factors.

[0007] According to an embodiment of the present disclosure, the belief distributions of the plurality of servers are fused according to the respective source confidences of the plurality of servers to obtain comprehensive status information, including: assigning a weight value to the belief distribution of each server according to the respective source confidences of the plurality of servers, wherein the weight value is proportional to the source confidence; utilizing information combination rules, based on the weight value of each belief distribution, weighted fusion is performed on the plurality of belief distributions to obtain the comprehensive status information, wherein the comprehensive status information includes a belief value for each of the power consumption influencing factors.

[0008] According to an embodiment of the present disclosure, the above-mentioned comprehensive state information is input into the action selection model to select the optimized control operation corresponding to the above-mentioned comprehensive state information from multiple power consumption control operations using the above-mentioned action selection model, including: inputting the above-mentioned comprehensive state information into the above-mentioned action selection model as the state representation of the above-mentioned action selection model, so as to use the above-mentioned action selection model to perform the following operations: using the action value function to estimate the long-term added value of using each of the above-mentioned power consumption control operations based on the above-mentioned comprehensive state information; selecting the optimized control operation from the multiple power consumption control operations according to the above-mentioned long-term added value, wherein the above-mentioned optimized control operation is the power consumption control operation with the highest long-term added value.

[0009] According to an embodiment of the present disclosure, the above method also includes: obtaining the adjustment state information obtained when the above server cluster runs under the control of the above optimization control operation; evaluating the above adjustment state information according to the above operation state information to obtain an evaluation result; when the above evaluation result indicates that there is a difference between the above adjustment state information and the above operation state information, adjusting the parameters in the belief distribution function and the information combination rule based on the above adjustment state information, wherein the above belief distribution function is used to analyze the above operation state information, and the above information combination rule is used to fuse multiple belief distributions; verifying the rationality of the adjusted belief distribution function and information combination rule; feeding back the above adjustment state information to the above action selection model to optimize the weight and bias of the action value function in the above action selection model based on the above adjustment state information; in response to the received state analysis request, determining the updated comprehensive state information of the above server cluster using the verified adjusted belief distribution function and information combination rule; inputting the above updated comprehensive state information into the optimized action selection model, and outputting the optimization control operation corresponding to the above updated comprehensive state information.

[0010] According to an embodiment of the present disclosure, the above method also includes: preprocessing the above operating status information, wherein the above preprocessing operation includes at least one of denoising processing, standardization processing and normalization processing; wherein the above normalization processing includes mapping the operating parameters in the above operating status information to a preset range based on a reference interval, wherein, for the temperature parameters of the components in the above operating status information, the above reference interval is determined based on the heat dissipation characteristics of the above components.

[0011] According to an embodiment of the present disclosure, the above-mentioned control of the above-mentioned server cluster to perform the above-mentioned optimization control operation to adjust the power consumption of the above-mentioned multiple servers includes: determining operating parameters associated with the above-mentioned optimization control operation, wherein the above-mentioned operating parameters include at least one of fan speed, processor utilization and memory usage; controlling the above-mentioned server cluster to adjust the above-mentioned operating parameters to reduce the power consumption of the above-mentioned multiple servers.

[0012] The second aspect of the present disclosure provides a server power consumption control device, comprising: a belief determination module, used to analyze the operating status information of each of multiple servers in a server cluster, and determine a belief distribution corresponding to a preset power consumption influencing factor, wherein the above-mentioned belief distribution is used to characterize the trust level of the degree of influence of the above-mentioned preset power consumption influencing factor on power consumption; a belief fusion module, used to fuse the belief distributions of the multiple servers according to the respective source confidences of the multiple servers, and obtain comprehensive status information, wherein the above-mentioned source confidence characterizes the degree of trust in the operating status information in the above-mentioned servers; an operation selection module, used to input the above-mentioned comprehensive status information into an action selection model, so as to use the above-mentioned action selection model to select an optimization control operation corresponding to the above-mentioned comprehensive status information from multiple power consumption control operations; an operation execution module, used to control the above-mentioned server cluster to execute the above-mentioned optimization control operation to adjust the power consumption of the above-mentioned multiple servers.

[0013] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0014] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0015] The fifth aspect of the present disclosure further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0016] According to an embodiment of the present disclosure, by analyzing the operating status information of each of the multiple servers and fusing the multiple belief distributions obtained by the analysis, the fusion of multi-source data is achieved, making the comprehensive status information determined to be more accurate and comprehensive. The fused comprehensive status information is input into the action selection model, and the optimized control operation corresponding to the comprehensive status information is determined by using the action selection model, so that when faced with changing workloads and environmental conditions, the operation of adjusting the power consumption of the server can be determined quickly and accurately. The server cluster is controlled to perform the optimized control operation, which realizes the automatic adjustment of the power consumption of the server based on the operating status information detected in real time, effectively improving the efficiency of power consumption adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0018] Figure 1 Schematically illustrates an application scenario diagram of the server power consumption control method, apparatus, device, medium, and program product according to an embodiment of the present disclosure;

[0019] Figure 2 The flowchart of the method for controlling power consumption of a server according to an embodiment of the present disclosure is schematically shown;

[0020] Figure 3 Schematically shows a flow chart of determining an optimization control operation in a method for controlling power consumption of a server according to an embodiment of the present disclosure;

[0021] Figure 4 A diagram schematically illustrates a connection relationship between multiple processing modules in a method for controlling server power consumption according to an embodiment of the present disclosure;

[0022] Figure 5 The following schematically shows a flow chart of a method for controlling server power consumption according to another embodiment of the present disclosure;

[0023] Figure 6 The following schematically shows a structural block diagram of a server power consumption control device according to an embodiment of the present disclosure;

[0024] Figure 7 A block diagram of an electronic device suitable for implementing a method for controlling server power consumption according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0025] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0026] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0027] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0028] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0029] In the technical solutions disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0030] Server clusters are a core component of modern data centers, responsible for massive data processing and computing tasks. However, these clusters consume significant amounts of electricity during operation. Therefore, effectively controlling their power consumption to improve energy efficiency, reduce operating costs, and minimize carbon emissions is a critical issue. Existing power consumption control methods primarily rely on static strategies or empirically based heuristic algorithms, but these methods often struggle to achieve an optimal balance between energy efficiency and performance when faced with complex and changing workloads.

[0031] Specifically, power consumption control methods in related technologies primarily rely on hardware-level improvements, such as using more efficient power supplies and cooling systems. However, these methods often require significant upfront investment and are unable to dynamically adapt to changes in actual workloads.

[0032] Secondly, approaches based on intelligent algorithms and data-driven approaches are becoming increasingly popular. Fixed policy control methods typically employ preset thresholds and rules to manage server power consumption. For example, when processor utilization exceeds a certain threshold, fan speed is increased or additional computing resources are enabled. This approach is simple to implement and deploy; however, it lacks flexibility, cannot dynamically adapt to uncertain workload changes, and can easily lead to over- or under-provisioning of resources.

[0033] Furthermore, feedback control methods typically use classic control methods such as controllers to adjust power consumption by monitoring the server's operating status in real time. This method allows for dynamic adjustments and a fast response; however, it suffers from limited control accuracy and struggles with complex nonlinear relationships and multi-objective optimization problems.

[0034] Going a step further, machine learning-based control methods use regression or classification models to predict future power consumption requirements and then pre-adjust based on the predicted results. This approach can leverage historical data to improve prediction accuracy, but model training and updating are complex and rely on large amounts of high-quality training data. Traditional existing solutions need to address their shortcomings in dynamic adaptability and uncertainty.

[0035] An embodiment of the present disclosure provides a server power consumption control method, which analyzes the operating status information of multiple servers in a server cluster to determine a belief distribution corresponding to a preset power consumption influencing factor, wherein the belief distribution is used to characterize the trust level of the degree of influence of the preset power consumption influencing factor on power consumption; based on the respective source confidences of the multiple servers, the belief distributions of the multiple servers are fused to obtain comprehensive status information, wherein the source confidence characterizes the degree of trust in the operating status information in the server; the comprehensive status information is input into an action selection model to use the action selection model to select an optimized control operation corresponding to the comprehensive status information from multiple power consumption control operations; and the server cluster is controlled to perform the optimized control operation to adjust the power consumption of the multiple servers.

[0036] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0037] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0038] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0039] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0040] It should be noted that the server power consumption control method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the server power consumption control device provided in the embodiment of the present disclosure can generally be set in the server 105. The server power consumption control method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the server power consumption control device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0041] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0042] The following will be based on Figure 1 The scene described by Figures 2 to 5 The server power consumption control method of the disclosed embodiment is described in detail.

[0043] like Figure 2 As shown, the server power consumption control method of this embodiment includes operations S210 to S240.

[0044] In operation S210 , operating status information of each of the plurality of servers in the server cluster is analyzed to determine a belief distribution corresponding to a preset power consumption influencing factor, wherein the belief distribution is used to represent a confidence level of the degree of influence of the preset power consumption influencing factor on power consumption.

[0045] In operation S220 , the belief distributions of the multiple servers are fused according to their respective source confidences to obtain comprehensive status information, wherein the source confidence represents the degree of trustworthiness of the running status information in the servers.

[0046] In operation S230 , the comprehensive state information is input into an action selection model, so as to select an optimized control operation corresponding to the comprehensive state information from a plurality of power consumption control operations using the action selection model.

[0047] In operation S240 , the server cluster is controlled to perform an optimization control operation to adjust power consumption of the plurality of servers.

[0048] According to an embodiment of the present disclosure, the operating status information of each server in a server cluster is collected and analyzed to control the power consumption generated during cluster operation. The server operating status information is provided by the server's baseboard management controller (BMC), and different BMCs can be considered different information sources. Multiple sensors in the BMC are used to acquire operating status information, including processor utilization, memory usage, temperature, fan speed, and other information.

[0049] According to an embodiment of the present disclosure, the operating status information of each server is analyzed to convert the operating status information into a belief distribution corresponding to preset power consumption influencing factors. The preset power consumption factors include temperature factors, load factors, and resource occupancy factors. In the process of analyzing the operating status information, information related to each preset power consumption influencing factor is extracted and analyzed one by one to obtain a corresponding belief distribution. The belief distribution is used to quantify the degree of trust in the information under certain assumptions, that is, the level of trust in the degree to which the preset power consumption influencing factors affect power consumption.

[0050] According to an embodiment of the present disclosure, after determining the belief distributions for each server, these multiple belief distributions are fused to obtain comprehensive status information for the server cluster. Because each server has different source confidence levels, the weight assigned to each server's belief distribution during the fusion process also varies. Source confidence refers to an assessment of the reliability and authenticity of the information source. By fusing belief distributions based on source confidence, the resulting comprehensive status information is more accurate.

[0051] According to an embodiment of the present disclosure, comprehensive state information is input into an action selection model, which is then used to determine the optimal control operation corresponding to the comprehensive state information. The action selection model can be an algorithm that combines deep learning and reinforcement learning, such as the DQN (Deep Q-Network) model. The core concept of the action selection model is to use a deep neural network to approximate the action-value function, thereby making decisions among multiple power control operations. These power control operations are multiple means of operating the server learned during model training.

[0052] According to an embodiment of the present disclosure, after determining that an optimization control operation corresponding to the comprehensive status information is obtained, the server cluster is controlled to execute the operation to adjust the power consumption of multiple servers and reduce the overall power consumption of the server cluster. The optimization control operation can maximize the power consumption reduction effect.

[0053] According to an embodiment of the present disclosure, by analyzing the operating status information of each of the multiple servers and fusing the multiple belief distributions obtained by the analysis, the fusion of multi-source data is achieved, making the comprehensive status information determined to be more accurate and comprehensive. The fused comprehensive status information is input into the action selection model, and the optimized control operation corresponding to the comprehensive status information is determined by using the action selection model, so that when faced with changing workloads and environmental conditions, the operation of adjusting the power consumption of the server can be determined quickly and accurately. The server cluster is controlled to perform the optimized control operation, which realizes the automatic adjustment of the power consumption of the server based on the operating status information detected in real time, effectively improving the efficiency of power consumption adjustment.

[0054] According to an embodiment of the present disclosure, the operating status information of each of the multiple servers in the server cluster is analyzed to determine the belief distribution corresponding to the preset power consumption influencing factors, including: using a belief distribution function to analyze the operating status information of each of the multiple servers in the server cluster to determine the belief distribution of each server, wherein the belief distribution includes a belief value for each assumption condition corresponding to the belief distribution function, and the assumption condition is obtained by combining multiple preset power consumption influencing factors.

[0055] According to an embodiment of the present disclosure, a belief distribution function is used to analyze the operating status information of each of the multiple servers in the server cluster. The belief distribution function is used to define the probability mapping of an event in the interval [0,1], which represents the credibility of the occurrence of the event under a certain assumption. Each assumption corresponds to a belief distribution function, and the belief distribution function is used to calculate the belief value under each assumption. Specifically, the assumption is obtained by combining multiple preset power consumption influencing factors. In the process of determining the assumption, the identification framework is analyzed, and then all possible situations in the identification framework are listed, thereby obtaining multiple assumptions. The identification framework is defined based on the preset power consumption influencing factors.

[0056] For example, based on the preset identification framework of power consumption influencing factors defined as {temperature factor, load factor, resource utilization factor}, multiple hypotheses are determined as {temperature factor}, {temperature factor, load factor}, {temperature factor, resource utilization factor}, {load factor}, {load factor, resource utilization factor}, and {resource utilization factor}. The belief distribution function is used to calculate the belief value corresponding to each hypothesis. If the operating status information contains strong supporting evidence for the "temperature factor," the trust allocation function may assign a higher belief value to the "temperature factor."

[0057] According to the embodiment of the present disclosure, the belief distribution of the server is constructed according to the belief value of each hypothesis condition, and the belief distribution is used to represent the trust level of different hypothesis conditions of the health status of the server. Assuming that the identification framework is {temperature factor, load factor, resource occupancy factor}, then using m i ({A}) represents each belief value to obtain the belief distribution, where i represents different servers and A represents different assumptions.

[0058] According to an embodiment of the present disclosure, the Dempster-Shafer evidence theory (DS evidence theory) can be used to determine the belief distribution. By converting the operating status information into a belief distribution, the current operating status of the server can be accurately determined.

[0059] According to an embodiment of the present disclosure, the belief distributions of multiple servers are fused according to their respective source confidences to obtain comprehensive status information, including: assigning a weight value to the belief distribution of each server according to their respective source confidences, wherein the weight value is proportional to the source confidence; utilizing information combination rules, based on the weight value of each belief distribution, weighted fusion is performed on multiple belief distributions to obtain comprehensive status information, wherein the comprehensive status information includes belief values ​​for each power consumption influencing factor.

[0060] According to an embodiment of the present disclosure, multiple belief distributions are fused, wherein the fusion process involves two dimensions: one dimension is to comprehensively consider the evidence provided by multiple belief distributions regarding preset power consumption influencing factors, thereby deriving a comprehensive estimate of the server cluster status; the other dimension is to consider the information provided by different servers, derive the confidence of different sources, and fuse the confidence of information from different servers. Specifically, during the fusion process, a corresponding weight value is assigned to each server's belief distribution based on the source confidence, thereby fusing multiple belief distributions based on the weight values.

[0061] According to an embodiment of the present disclosure, a weighted fusion of multiple belief distributions is performed using an information combination rule to obtain comprehensive state information. The information combination rule may be the Dempster combination rule, also known as the Dempster-Shafer synthesis rule, which is a method for combining different pieces of evidence. In this embodiment, each belief distribution corresponds to a belief confidence level. A weighted summation of the multiple belief distributions is performed to obtain a comprehensive belief distribution corresponding to each hypothesis condition, and comprehensive state information is then generated based on the comprehensive belief distribution.

[0062] According to an embodiment of the present disclosure, in the process of fusing belief distributions, when there is a conflict between multiple belief distributions, the conflict needs to be eliminated to ensure the smooth progress of the fusion process. When calculating the conflict, the conflict factor is calculated based on the belief distribution. The conflict factor can be expressed as K. When K=0, it means that the two belief distributions do not conflict at all. When K=1, it means that the two belief distributions conflict completely. In this case, using the Dempster combination rule will result in the belief distributions being unable to fuse. Therefore, in the process of fusing using the Dempster combination rule, the conflict information K is used as part of the normalization factor and is "eliminated" from the final fusion result, that is, the information of the conflicting part is proportionally distributed to the non-conflicting part.

[0063] According to the embodiments of the present disclosure, if the belief value in the final fused belief distribution is greater than 1, the multiple integrated belief distributions need to be normalized again. By fusing the belief distributions of different servers, the final integrated state information is more accurate and comprehensive. Conflicts are eliminated during the fusion process, ensuring that the final fused result is more reliable.

[0064] According to an embodiment of the present disclosure, the comprehensive state information is input into the action selection model to select the optimized control operation corresponding to the comprehensive state information from multiple power consumption control operations using the action selection model, including: inputting the comprehensive state information into the action selection model as a state representation of the action selection model to perform the following operations using the action selection model: using the action value function to estimate the long-term added value of using each power consumption control operation based on the comprehensive state information; selecting the optimized control operation from multiple power consumption control operations based on the long-term added value, wherein the optimized control operation is the power consumption control operation with the highest long-term added value.

[0065] According to an embodiment of the present disclosure, the comprehensive state information obtained after fusion is used as the state representation of the action selection model and input into the action selection model. The action selection model will perform action value function approximation based on this new state representation input. The action value function can be the Q function in the action selection model. The approximation of the Q function is usually implemented by a deep neural network. The basic principle is to approximate the Q function Q(s,a) through a neural network, where s is the state representation and a is the action representation. The neural network estimates the optimal Q value through continuous trial and error learning.

[0066] According to an embodiment of the present disclosure, the action selection model estimates the long-term added value, that is, the long-term reward, of taking a certain action a in a certain state s by learning a Q-value function. In this problem, the reward function is usually based on the following aspects: temperature control effect, energy efficiency, and task performance. With respect to the temperature control effect, if the adjusted temperature remains within a safe range, its long-term added value is high. With respect to energy efficiency, if the heat dissipation strategy adopted can reduce energy consumption while maintaining a good temperature control effect, its long-term added value is high. With respect to task performance, if the heat dissipation strategy adopted can maintain the efficient working state of the server, its long-term added value is high.

[0067] According to an embodiment of the present disclosure, based on the Q-value estimation of the current state, the optimal action is selected, which generally refers to a power consumption adjustment strategy or a resource allocation scheme. Specifically, by comparing the long-term added value of different power consumption control operations, the power consumption control operation with the highest long-term added value is selected as the optimized control operation. Among them, the optimized control operation can be adjusting the fan speed or limiting the processor utilization to optimize the heat dissipation performance of the server. By using the action selection model to analyze the current and predicted states, and combining the comprehensive state information fused by DS evidence, the power consumption of the server cluster can be accurately adjusted.

[0068] like Figure 3 As shown, the process of determining the optimal control operation includes four steps. In step S310, the operating status information of each server in the server cluster is obtained. In step S320, the operating status information is analyzed using DS evidence theory to construct a belief distribution for each server. In step S330, the belief distribution is input into the action selection model to approximate the action selection function. In step S340, the optimal control operation is selected based on the function approximation result.

[0069] According to the embodiments of the present disclosure, by analyzing the current and predicted states through an action selection model and integrating multi-source data with DS evidence theory, the power consumption of a server cluster can be precisely adjusted. This dynamic adjustment maximizes energy efficiency while ensuring service quality, based on real-time load conditions and expected demand.

[0070] According to the embodiments of the present disclosure, the action selection model selects the optimal control strategy and adjustment parameters based on the belief distribution, thereby optimizing the system's power consumption and performance. This intelligent decision-making can quickly respond to changing workloads and environmental conditions, improving the efficiency and stability of the entire system.

[0071] According to an embodiment of the present disclosure, a server cluster is controlled to perform an optimization control operation to adjust the power consumption of multiple servers, including: determining operating parameters associated with the optimization control operation, wherein the operating parameters include at least one of fan speed, processor utilization, and memory usage; and controlling the server cluster to adjust the operating parameters to reduce the power consumption of multiple servers.

[0072] According to an embodiment of the present disclosure, after determining the optimization control operation, since the optimization control operation determined according to the action selection model is in the form of a vector, it is not possible to directly control the server based on the vector. Determine the operating parameters associated with the optimization control operation. Specifically, the operating parameters are the parameters actually involved in the operation of the server cluster and are adjustable. Control the server cluster to adjust the operating parameters to reduce the power consumption of multiple servers. For example, the speed of the cooling fan in the server can be increased to reduce the temperature of the server during operation. By determining the operating parameters, it is convenient to automatically adjust the power consumption of the server.

[0073] According to an embodiment of the present disclosure, after performing the optimization control operation, the execution effect can also be evaluated, heat dissipation performance indicators such as temperature stability, fan speed control accuracy, etc. can be calculated, and room for improvement can be determined to provide further strategies for reducing power consumption.

[0074] According to an embodiment of the present disclosure, the server power consumption control method also includes: obtaining adjustment status information obtained when the server cluster runs under the control of the optimization control operation; evaluating the adjustment status information according to the operation status information to obtain an evaluation result; when the evaluation result indicates that there is a difference between the adjustment status information and the operation status information, adjusting the parameters in the belief distribution function and the information combination rule based on the adjustment status information, wherein the belief distribution function is used to analyze the operation status information, and the information combination rule is used to fuse multiple belief distributions; verifying the rationality of the adjusted belief distribution function and information combination rule; feeding back the adjustment status information to the action selection model to optimize the weight and bias of the action value function in the action selection model based on the adjustment status information; in response to the received status analysis request, determining the updated comprehensive status information of the server cluster using the verified adjusted belief distribution function and information combination rule; inputting the updated comprehensive status information into the optimized action selection model, and outputting the optimization control operation corresponding to the updated comprehensive status information.

[0075] According to an embodiment of the present disclosure, after executing an optimization control operation, adjustment state information resulting from the operation of the server cluster under the optimization control operation is obtained. The adjustment state information is evaluated based on the operating state information. Specifically, the evaluation method may be to determine whether there is a difference between the two. If there is a difference, it indicates that the optimization control operation has been effective. Alternatively, the evaluation method may be to re-evaluate the power consumption of the current server based on the adjustment state information to determine whether the current power consumption is within a stable operating range, thereby generating an evaluation result.

[0076] According to an embodiment of the present disclosure, when the evaluation result indicates that there is no difference between the adjustment state information and the running state information, it means that the optimization control operation has not produced any effect. The optimization control operation is marked and fed back to the action selection model. The weight of the optimization control operation is reduced so that a lower long-term added value is assigned to it when approximating the action value function. In addition, if in a new server operating environment, it is determined for multiple consecutive times that there is no difference between the adjustment state information and the running state information, it means that the parameters of the current function and model are relatively inaccurate. In this case, the step size of the preset length is increased during the parameter adjustment process, thereby increasing the span of the parameter adjustment and enabling it to learn quickly to adapt to the new operating environment.

[0077] According to embodiments of the present disclosure, when the evaluation results indicate a discrepancy between the adjustment state information and the operational state information, it is determined that the optimized control operation has been effective. Feedback learning is performed based on the adjustment state information, prompting the system to continuously learn and optimize. Feedback learning is divided into two parts: one for DS evidence theory and the other for the action selection model.

[0078] According to an embodiment of the present disclosure, for the DS evidence theory, the parameters in the belief distribution function and the information combination rule are adjusted based on the adjustment state information, so that the belief distribution generated by the belief distribution function is more accurate, and the information combination rule is more comprehensive and accurate when fusing multiple belief distributions. After adjusting the belief distribution function and the information combination rule, it is also necessary to verify their rationality to reduce the occurrence of conflicts. Specifically, simulation data can be used to test the adjusted belief distribution function and combination rules, generate different input belief distributions, combine them, and observe whether the results are reasonable.

[0079] According to embodiments of the present disclosure, the weights and biases of the action-value function in the action selection model are optimized based on the adjusted state information. Specifically, the model's loss function can be used to adjust the model's parameters. By adjusting the parameters of the action selection model, the action-value function's predictions are made closer to the actual action values, improving the performance of the action-value function.

[0080] According to an embodiment of the present disclosure, after both parts of feedback learning are processed, if a state analysis request is received, the verified adjusted belief distribution function and information combination rule can be used to determine updated comprehensive state information for the server cluster. This updated comprehensive state information is then input into the optimized action selection model, and an optimized control action corresponding to the updated comprehensive state information is output. After the optimized control action is obtained, the server cluster is controlled to execute the optimized control action to reduce power consumption of multiple servers.

[0081] According to the embodiments of the present disclosure, feedback learning is performed based on the adjustment status information, so that each processing module can continuously learn and optimize, continuously improve the decision-making ability and prediction accuracy, and cope with the increasingly complex server cluster management challenges.

[0082] like Figure 4 As shown, the server power consumption control method mainly includes a state monitoring module 410 , a belief distribution fusion module 420 , an action selection module 430 , an execution control module 440 , and a feedback learning module 450 .

[0083] The state monitoring module 410 is responsible for collecting real-time operational status information of the server cluster, such as processor utilization, memory usage, temperature, and fan speed. The belief distribution fusion module 420 uses DS evidence theory to fuse multi-source data from different sensors to improve the accuracy of state estimation. The action selection module 430 learns and makes decisions based on the input comprehensive state information, determining the optimal control operation. The execution control module 440 adjusts the operating parameters of the server cluster based on the optimal control operation selected by the action selection module 430 to achieve power consumption control. The feedback learning module 450 evaluates the execution results and uses the feedback for continuous learning and optimization of the model.

[0084] like Figure 4 As shown, the state monitoring module 410 is connected to the belief distribution fusion module 420. The various operating status information collected in real time by the state monitoring module 410 is transmitted to the belief distribution fusion module 420. The belief distribution fusion module 420 is connected to the action selection module 430. The belief distribution fusion module 420 fuses the collected multi-source data and transmits the fused, highly reliable comprehensive state information to the action selection module 430. The action selection module 430 is connected to the execution control module 440. Based on the fused state information, the action selection module 430 determines the optimal control operation through the deep Q network and transmits the optimized control operation to the execution control module 440. The execution control module 440 is connected to the feedback learning module 450. The execution control module 440 adjusts the operating parameters of the server cluster based on the received optimal control operation.

[0085] According to an embodiment of the present disclosure, after executing an optimization control operation, the state monitoring module 410 collects the execution results and new state information and transmits this information to the feedback learning module 450. The feedback learning module 450 connects the belief distribution fusion module 420 and the action selection module 430. The feedback learning module 450 transmits the feedback state information to the belief distribution fusion module 420 to adjust the evidence fusion rules. The execution results are used for continuous learning and optimization in the action selection module 430, which updates the action selection module 430 to improve the accuracy of the next round of decision-making.

[0086] According to the embodiments of the present disclosure, by designing a feedback learning module and utilizing it to interact with the belief distribution fusion module and the action selection module, it is possible to learn more optimal strategies and adjust parameters to improve system efficiency and power consumption control. This allows the system to learn and optimize based on actual execution results to adapt to dynamically changing workloads and environmental requirements.

[0087] According to an embodiment of the present disclosure, the server power consumption control method also includes: preprocessing the operating status information, wherein the preprocessing operation includes at least one of denoising processing, standardization processing and normalization processing; wherein the normalization processing includes mapping the operating parameters in the operating status information to a preset range based on a reference interval, wherein, for the temperature parameters of the components in the operating status information, the reference interval is determined based on the heat dissipation characteristics of the components.

[0088] According to an embodiment of the present disclosure, the collected operating status information is preliminarily cleaned and preprocessed, such as denoising, standardization, and normalization. For temperature data, normalization can be performed based on the heat dissipation characteristics of the server. Specifically, the temperature range of each component is determined based on the heat dissipation characteristics of the server. For example, the temperature range of the processor may be between 40°C and 80°C, and the temperature range of the hard disk may be between 30°C and 50°C. These temperature ranges are used as reference intervals for normalization of temperature parameters so that subsequent modules can better understand and use these data.

[0089] like Figure 5 As shown, the server power consumption control method executed in this embodiment includes an operation status information collection unit 501, a data preprocessing unit 502, a belief distribution generation unit 503, a belief distribution fusion unit 504, a fusion conflict resolution unit 505, a fusion result input unit 506, an action value function approximation unit 507, an optimization control operation selection unit 508, an optimization control operation execution unit 509, an operation feedback acquisition unit 510, an operation feedback evaluation unit 511 and a feedback learning unit 512.

[0090] like Figure 5As shown, after the feedback learning unit 512 performs feedback learning based on the evaluation results, the feedback learning results are transmitted to the data preprocessing unit 502, the belief distribution generation unit 503, the belief distribution fusion unit 504, and the action-value function approximation unit 507. The feedback learning results are transmitted to the data preprocessing unit 502 to update the reference interval in the data preprocessing, making the normalized results more reliable. The feedback learning results are transmitted to the belief distribution generation unit 503 to update the parameters in the belief distribution function used when generating the belief distribution, making the generated belief distribution more accurate. The feedback learning results are transmitted to the belief distribution generation unit 503 to update the parameters in the information combination rule used when fusing the belief distribution, making the fused comprehensive state information more comprehensive and reliable. The feedback learning results are transmitted to the action-value function approximation unit 507 to optimize the weights and biases of the action-value function, so that the optimized control strategy selected when approximating the action-value function is more accurate.

[0091] According to the embodiments of the present disclosure, the action selection model in deep reinforcement learning is used to approximate the action value function through a neural network to achieve optimal decision-making on the power consumption control strategy of the server cluster. The DS evidence theory is used to fuse data from multiple monitoring points, which improves the accuracy and comprehensiveness of the system status. The power consumption of the server is dynamically adjusted, and dynamic power consumption adjustment based on real-time monitoring data and prediction models is realized, so that the server cluster can achieve the best energy efficiency and performance under different load and environmental conditions. Utilizing the feedback and learning mechanism, the system can be learned and optimized according to the actual execution results to adapt to dynamically changing workloads and environmental requirements.

[0092] Based on the above server power consumption control method, the present disclosure also provides a server power consumption control device. Figure 6 The device is described in detail.

[0093] like Figure 6 As shown, the server power consumption control device 600 of this embodiment includes a belief determination module 610 , a belief fusion module 620 , an operation selection module 630 and an operation execution module 640 .

[0094] Belief determination module 610 is configured to analyze the operating status information of each of the multiple servers in the server cluster and determine a belief distribution corresponding to a preset power consumption influencing factor, where the belief distribution represents a confidence level regarding the degree of impact of the preset power consumption influencing factor on power consumption. In one embodiment, belief determination module 610 may be configured to perform operation S210 described above and will not be further described here.

[0095] Belief fusion module 620 is configured to fuse the belief distributions of multiple servers based on their respective source confidences to obtain comprehensive state information, where the source confidences represent the degree of trustworthiness of the operational state information in the servers. In one embodiment, belief fusion module 620 can be configured to perform operation S220 described above, which will not be further described here.

[0096] The operation selection module 630 is used to input the comprehensive state information into the action selection model, so as to select the optimal control operation corresponding to the comprehensive state information from multiple power consumption control operations using the action selection model. In one embodiment, the operation selection module 630 can be used to perform the operation S230 described above, which will not be repeated here.

[0097] The operation execution module 640 is used to control the server cluster to execute the optimization control operation to adjust the power consumption of multiple servers. In one embodiment, the operation selection module 630 can be used to execute the operation S240 described above, which will not be repeated here.

[0098] According to an embodiment of the present disclosure, the belief determination module 610 includes a belief determination submodule.

[0099] The belief determination submodule is used to use the belief distribution function to analyze the operating status information of multiple servers in the server cluster and determine the belief distribution of each server, wherein the belief distribution includes the belief value of each assumption corresponding to the belief distribution function, and the assumption is obtained by combining multiple preset power consumption influencing factors.

[0100] According to an embodiment of the present disclosure, the belief fusion module 620 includes a weight allocation submodule and a belief fusion submodule.

[0101] The weight allocation submodule is used to allocate a weight value to the belief distribution of each server according to the respective source confidence of multiple servers, wherein the weight value is proportional to the source confidence.

[0102] The belief fusion submodule is used to utilize information combination rules and perform weighted fusion on multiple belief distributions based on the weight value of each belief distribution to obtain comprehensive state information, wherein the comprehensive state information includes the belief value for each power consumption influencing factor.

[0103] According to an embodiment of the present disclosure, the operation selection module 630 includes an information input submodule, an additional determination submodule, and an action selection submodule.

[0104] The information input submodule is used to input the comprehensive state information into the action selection model as the state representation of the action selection model.

[0105] The additional determination submodule is used to estimate the long-term additional value of using each power consumption control operation based on the comprehensive state information by using the action value function.

[0106] The action selection submodule is configured to select an optimized control operation from a plurality of power consumption control operations according to the long-term added value, wherein the optimized control operation is the power consumption control operation with the highest long-term added value.

[0107] According to an embodiment of the present disclosure, the server power consumption control device 600 further includes an information acquisition module, an information evaluation module, a parameter adjustment module, a rationality verification module, a function optimization module, an update determination module, and an operation determination module.

[0108] The information acquisition module is used to obtain adjustment status information obtained when the server cluster runs under the control of the optimization control operation.

[0109] The information evaluation module is used to evaluate the adjustment status information according to the operation status information to obtain an evaluation result.

[0110] a parameter adjustment module for adjusting parameters in the belief distribution function and the information combination rule based on the adjustment status information when the evaluation result indicates a difference between the adjustment status information and the operating status information, wherein the belief distribution function is used to analyze the operating status information and the information combination rule is used to fuse multiple belief distributions;

[0111] The rationality verification module is used to verify the rationality of the adjusted belief distribution function and information combination rules.

[0112] The function optimization module is used to feed back the adjustment state information to the action selection model to optimize the weight and bias of the action value function in the action selection model based on the adjustment state information.

[0113] The update determination module is configured to determine the updated comprehensive status information of the server cluster in response to the received status analysis request by using the verified adjusted belief distribution function and information combination rule.

[0114] The operation determination module is used to input the updated comprehensive state information into the optimized action selection model and output the optimized control operation corresponding to the updated comprehensive state information.

[0115] According to an embodiment of the present disclosure, the server power consumption control device 600 further includes a pre-processing module.

[0116] A preprocessing module is used to preprocess the operating status information, wherein the preprocessing operation includes at least one of denoising, standardization and normalization; wherein the normalization includes mapping the operating parameters in the operating status information to a preset range based on a reference interval, wherein, for the temperature parameters of the components in the operating status information, the reference interval is determined based on the heat dissipation characteristics of the components.

[0117] According to an embodiment of the present disclosure, the operation execution module 640 includes a parameter determination submodule and a parameter control submodule.

[0118] The parameter determination submodule is configured to determine an operating parameter associated with the optimization control operation, wherein the operating parameter includes at least one of a fan speed, a processor utilization rate, and a memory usage rate.

[0119] The parameter control submodule is used to control the server cluster to adjust the operating parameters to reduce the power consumption of multiple servers.

[0120] According to an embodiment of the present disclosure, any multiple modules among the belief determination module 610, the belief fusion module 620, the operation selection module 630, and the operation execution module 640 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the belief determination module 610, the belief fusion module 620, the operation selection module 630, and the operation execution module 640 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable method of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of the belief determination module 610 , the belief fusion module 620 , the operation selection module 630 and the operation execution module 640 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0121] like Figure 7As shown, the electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate operations and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage portion 708 into a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different operations of the method flow according to the embodiment of the present disclosure.

[0122] Various programs and data required for the operation of the electronic device 700 are stored in the RAM 703. The processor 701, ROM 702, and RAM 703 are connected to each other via a bus 704. The processor 701 performs various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 702 and / or RAM 703. It should be noted that the programs may also be stored in one or more memories other than the ROM 702 and RAM 703. The processor 701 may also perform various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in one or more memories.

[0123] According to an embodiment of the present disclosure, electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to bus 704. Electronic device 700 may also include one or more of the following components connected to I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 708 including a hard disk; and a communication section 709 including a network interface card such as a LAN card or modem. Communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to I / O interface 705 as needed. Removable media 711, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 710 as needed, so that computer programs read from the removable media can be installed into storage section 708 as needed.

[0124] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0125] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 702 and / or RAM 703 described above, and / or one or more memories other than ROM 702 and RAM 703.

[0126] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is executed in a computer system, the program code is used to enable the computer system to implement the server power consumption control method provided by the embodiments of the present disclosure.

[0127] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the processor 701 executes the computer program. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0128] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 709, and / or installed from a removable medium 711. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0129] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 709, and / or installed from a removable medium 711. When the computer program is executed by the processor 701, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0130] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0131] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0132] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.

[0133] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for controlling server power consumption, characterized in that: The method comprises: Analyzing the operating status information of each of the plurality of servers in the server cluster to determine a belief distribution corresponding to a preset power consumption influencing factor, wherein the belief distribution is used to represent a confidence level of the degree of influence of the preset power consumption influencing factor on power consumption; fusing the belief distributions of the plurality of servers according to their respective source confidences to obtain comprehensive status information, wherein the source confidences represent the degree of trustworthiness of the operating status information in the servers; Inputting the comprehensive state information into an action selection model, so as to select an optimized control operation corresponding to the comprehensive state information from a plurality of power consumption control operations using the action selection model; controlling the server cluster to perform the optimization control operation to adjust the power consumption of the plurality of servers; The step of fusing the belief distributions of the multiple servers according to the respective source confidences of the multiple servers to obtain comprehensive status information includes: Assigning a weight value to the belief distribution of each of the servers according to the respective source confidences of the multiple servers, wherein the weight value is proportional to the source confidence; The information combination rule is used to perform weighted fusion on a plurality of the belief distributions based on the weight value of each belief distribution to obtain the comprehensive state information, wherein the comprehensive state information includes a belief value for each of the power consumption influencing factors.

2. The method according to claim 1, characterized in that The analyzing the operating status information of each of the plurality of servers in the server cluster to determine the belief distribution corresponding to the preset power consumption influencing factors includes: Using the belief distribution function, the operating status information of each of the multiple servers in the server cluster is analyzed to determine the belief distribution of each server, wherein the belief distribution includes the belief value of each assumption condition corresponding to the belief distribution function, and the assumption condition is obtained by combining multiple preset power consumption influencing factors.

3. The method according to claim 1, characterized in that Inputting the comprehensive state information into an action selection model, and selecting an optimized control operation corresponding to the comprehensive state information from a plurality of power consumption control operations using the action selection model, includes: The comprehensive state information is used as a state representation of the action selection model and input into the action selection model to perform the following operations using the action selection model: estimating, by using an action-value function, a long-term added value of using each of the power consumption control operations based on the integrated state information; An optimal control operation is selected from the plurality of power consumption control operations according to the long-term added value, wherein the optimal control operation is the power consumption control operation with the highest long-term added value.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Acquiring adjustment status information of the server cluster under the control of the optimization control operation; Evaluate the adjustment status information according to the operation status information to obtain an evaluation result; In a case where the evaluation result indicates that there is a difference between the adjustment state information and the operating state information, adjusting parameters in a belief distribution function and an information combination rule based on the adjustment state information, respectively, wherein the belief distribution function is used to analyze the operating state information, and the information combination rule is used to fuse multiple belief distributions; Verify the rationality of the adjusted belief distribution function and information combination rules; Feeding the adjustment state information back to the action selection model to optimize the weights and biases of the action value function in the action selection model based on the adjustment state information; In response to the received status analysis request, determining updated comprehensive status information of the server cluster using the verified adjusted belief distribution function and information combination rule; The updated comprehensive state information is input into the optimized action selection model, and an optimized control operation corresponding to the updated comprehensive state information is output.

5. The method according to claim 1, wherein The method further comprises: Preprocessing the operating status information, wherein the preprocessing includes at least one of denoising, standardization, and normalization; The normalization processing includes mapping the operating parameters in the operating status information to a preset range based on a reference interval, wherein, for the temperature parameters of the components in the operating status information, the reference interval is determined based on the heat dissipation characteristics of the components.

6. The method according to claim 1, characterized in that The controlling the server cluster to perform the optimization control operation to adjust the power consumption of the plurality of servers includes: determining an operating parameter associated with the optimization control operation, wherein the operating parameter comprises at least one of a fan speed, a processor utilization, and a memory usage; The server cluster is controlled to adjust the operating parameters to reduce power consumption of the plurality of servers.

7. A server power consumption control device, characterized in that: The device comprises: a belief determination module, configured to analyze the operating status information of each of the plurality of servers in the server cluster and determine a belief distribution corresponding to a preset power consumption influencing factor, wherein the belief distribution is used to represent a confidence level in the degree of influence of the preset power consumption influencing factor on power consumption; a belief fusion module, configured to fuse the belief distributions of the plurality of servers according to their respective source confidences to obtain comprehensive status information, wherein the source confidences represent the degree of trustworthiness of the operating status information of the servers; an operation selection module, configured to input the comprehensive state information into an action selection model, so as to select an optimized control operation corresponding to the comprehensive state information from a plurality of power consumption control operations using the action selection model; an operation execution module, configured to control the server cluster to execute the optimization control operation to adjust the power consumption of the plurality of servers; A weight assignment submodule is used to assign a weight value to the belief distribution of each server according to the respective source confidence of the multiple servers, wherein the weight value is proportional to the source confidence; The belief fusion submodule is used to utilize information combination rules and perform weighted fusion on multiple belief distributions based on the weight value of each belief distribution to obtain comprehensive state information, wherein the comprehensive state information includes the belief value for each power consumption influencing factor.

8. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction 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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