Task execution method, electronic device, storage medium and program product

Abstract

The present invention provides a task execution method that can be applied to the technical field of server energy efficiency improvement. The method includes: processing the current operating parameters of the server to obtain current operating data; predicting the energy consumption and response time of the server in a predetermined time period based on the current operating data and the first historical operating data of the server to obtain a prediction result; adjusting the initial task allocation plan based on the prediction result until the evaluation index based on the adjusted task allocation plan meets the predetermined index condition, thereby obtaining a target task allocation plan; controlling the operation of the cooling equipment associated with the data center based on the expected heat load obtained based on the prediction result and the environmental data of the data center where the server is located, until the environmental data of the data center meets the predetermined environmental condition, so that the server executes the target task allocation plan under the predetermined environmental condition. The present invention also provides an electronic device, a storage medium, and a program product.

Description

Technical Field

[0001] The present invention relates to the technical field of server energy efficiency improvement, and in particular to a task execution method, electronic equipment, storage medium and program product. Background Art

[0002] With the development of data centers, the problem of high energy consumption in data centers has continued to intensify. By improving the energy efficiency of servers deployed in data centers, the energy utilization rate of data centers can be improved and the high energy consumption of data centers can be reduced.

[0003] Current server energy efficiency improvement systems struggle to provide reliable and accurate predictions of future energy consumption. Furthermore, data center cooling equipment struggles to adjust its parameters based on server energy consumption and the data center's environment. This results in poor environmental control, shortened server lifespans, and reduced energy utilization. These low energy consumption prediction accuracy and low energy utilization reduce the effectiveness of server energy efficiency improvement systems. Summary of the Invention

[0004] In view of the above problems, the present invention provides a task execution method, an electronic device, a storage medium and a program product.

[0005] One aspect of the present invention provides a task execution method, the method comprising: in response to an energy consumption prediction request for an initial task allocation plan, processing the current operating parameters of the server to obtain current operating data; predicting the energy consumption and response time of the server in a predetermined time period based on the current operating data and the first historical operating data of the server to obtain a prediction result; adjusting the initial task allocation plan based on the prediction result until the evaluation index based on the adjusted task allocation plan meets the predetermined index condition, thereby obtaining a target task allocation plan; controlling the operation of the cooling equipment associated with the data center based on the expected heat load obtained based on the prediction result and the environmental data of the data center where the server is located, until the environmental data of the data center meets the predetermined environmental condition, so that the server executes the target task allocation plan under the predetermined environmental condition.

[0006] Another aspect of the present invention provides an electronic device, comprising: one or more processors; 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.

[0007] Another aspect of the present invention 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.

[0008] Another aspect of the present invention provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0009] According to an embodiment of the present invention, in response to an energy consumption prediction request, a server's energy consumption and response time for a predetermined time period are predicted based on the server's current operating data and first historical operating data to obtain a prediction result; an initial task allocation plan is adjusted based on the prediction result until predetermined indicators are met; and the operation of the cooling equipment is controlled based on the predicted thermal load and data center environmental data so that the server executes the target task allocation plan under predetermined environmental conditions. Since energy consumption is predicted based on the current operating data and first historical operating data during task execution, energy consumption can be predicted based on the distribution characteristics of the operating data, improving the accuracy of energy consumption prediction and providing a basis for adjusting the task allocation plan under low energy consumption conditions. When adjusting the task allocation plan, the predetermined indicator condition can be set to minimum energy consumption, thereby obtaining a target task allocation plan with minimum energy consumption. When controlling the operation of the cooling equipment, control can be performed based on the server's thermal load and the data center environment, achieving precise adjustment, reducing energy waste, and improving energy utilization. The task execution method of the present invention can reduce energy consumption, improve energy utilization, and enhance server energy efficiency and system execution efficiency by predicting energy consumption, optimizing the task allocation plan based on minimum energy consumption, and implementing closed-loop control of coordinated cooling control. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings.

[0011] Figure 1 An application scenario diagram of a task execution method according to an embodiment of the present invention is shown.

[0012] Figure 2 A flowchart of a task execution method according to an embodiment of the present invention is shown.

[0013] Figure 3 A schematic diagram of a task allocation adjustment scheme according to the present invention is shown.

[0014] Figure 4 A flowchart of a task execution method according to another embodiment of the present invention is shown.

[0015] Figure 5 A structural block diagram of a task execution device according to an embodiment of the present invention is shown.

[0016] Figure 6 A block diagram of an electronic device suitable for implementing a task execution method according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0017] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. 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 known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention.

[0018] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. 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.

[0019] 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.

[0020] 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.).

[0021] Improving server energy efficiency refers to improving the overall energy efficiency of servers and their data centers. With data centers experiencing increasing energy consumption, improving the intelligence and security of server energy efficiency has become a pressing issue.

[0022] When it comes to improving server energy efficiency, existing server energy efficiency improvement systems struggle to effectively capture the time series characteristics of server operating parameters, resulting in low energy consumption forecasting accuracy and, consequently, difficulty providing reliable future energy consumption forecasts. Furthermore, existing cooling management systems within data centers struggle to dynamically adjust to real-time workloads and environmental conditions, leading to energy waste or poor temperature control, impacting equipment lifespan. Furthermore, existing server energy efficiency improvement systems lack effective feedback mechanisms for energy consumption prediction models, task allocation strategies, and cooling management policies, making them difficult to adapt to changing workloads and environmental conditions.

[0023] In view of this, an embodiment of the present invention provides a task execution method, electronic device, storage medium and program product for improving energy consumption prediction accuracy and energy utilization, as well as the execution efficiency of a server energy efficiency improvement system. The task execution method includes: in response to an energy consumption prediction request for an initial task allocation plan, processing the current operating parameters of the server to obtain current operating data; based on the current operating data and the first historical operating data of the server, predicting the energy consumption and response time of the server in a predetermined time period to obtain a prediction result; based on the prediction result, adjusting the initial task allocation plan until the evaluation index based on the adjusted task allocation plan meets the predetermined index condition, thereby obtaining a target task allocation plan; based on the expected heat load obtained based on the prediction result and the environmental data of the data center where the server is located, controlling the operation of the cooling equipment associated with the data center until the environmental data of the data center meets the predetermined environmental condition, so that the server executes the target task allocation plan under the predetermined environmental condition.

[0024] Figure 1 An application scenario diagram of a task execution method according to an embodiment of the present invention is shown.

[0025] 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.

[0026] A user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, such as sending an energy consumption prediction request or receiving task execution results. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as task assignment applications, energy consumption prediction applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only). In some embodiments, the energy consumption prediction request does not need to be triggered by the user through the terminal device. The energy consumption prediction request can be automatically triggered when the initial task assignment plan is generated, and the user can simply browse the task execution process of the embodiment of the present invention through the terminal device.

[0027] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices with display screens, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0028] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports requests sent 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 requests and other data, and provide feedback to the terminal device regarding the processing results (e.g., task execution results, web pages, information, or data obtained or generated based on the requests).

[0029] It should be noted that the task execution method provided in the embodiment of the present invention can generally be executed by the server 105. Accordingly, the task execution device provided in the embodiment of the present invention can generally be set in the server 105. The task execution method provided in the embodiment of the present invention 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 task execution device provided in the embodiment of the present invention 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.

[0030] It should be understood that Figure 1 The 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.

[0031] The following will be based on Figure 1 The scene described by Figure 2~Figure 3 The task execution method according to the embodiment of the present invention is described in detail.

[0032] Figure 2 A flowchart of a task execution method according to an embodiment of the present invention is shown.

[0033] like Figure 2 As shown, the task execution method of this embodiment includes operations S210 to S240.

[0034] In operation S210 , in response to an energy consumption prediction request for an initial task allocation plan, current operating parameters of the server are processed to obtain current operating data.

[0035] In operation S220 , energy consumption and response time of the server in a predetermined time period are predicted based on the current operation data and the first historical operation data of the server to obtain a prediction result.

[0036] In operation S230 , the initial task allocation plan is adjusted according to the prediction result until the evaluation index based on the adjusted task allocation plan meets the predetermined index condition, thereby obtaining a target task allocation plan.

[0037] In operation S240, according to the expected heat load obtained based on the prediction result and the environmental data of the data center where the server is located, the cooling equipment associated with the data center is controlled to operate until the environmental data of the data center meets the predetermined environmental conditions, so that the server executes the target task allocation plan under the predetermined environmental conditions.

[0038] In some embodiments, the initial task allocation scheme can be obtained by randomly assigning tasks to servers based on the relationship between tasks and services. In one example, a data center may have m1 servers, and these m1 servers need to perform a total of m2 tasks, where m1 and m2 are both positive integers. When m1 and m2 are equal, the initial task allocation scheme can be that one server can perform one task; when m1 and m2 are not equal, the initial task allocation scheme can be to assign the m2 tasks to the m1 servers in a round-robin manner.

[0039] In some embodiments, a server may be a device used to perform a task.

[0040] In some embodiments, the current operating parameters may be server operating parameters acquired via a sensor network at the time the energy consumption prediction request is processed. The server operating parameters may include central processing unit (CPU) utilization, memory usage, traffic flow, temperature, humidity, and power consumption, among other parameters. The current operating data may be obtained by processing abnormal parameters in the operating parameters and then normalizing the processed abnormal parameters.

[0041] In some embodiments, the first historical operation data may be historical operation data randomly extracted from a database storing historical operation data of the server when predicting the energy consumption of the initial task allocation plan, such as historical CPU utilization, historical memory utilization, historical traffic, historical temperature, historical humidity, and historical power consumption.

[0042] In some embodiments, the predetermined time period may be a future time period, such as any time period after the moment when the energy consumption prediction request is processed. Based on the data change trend and periodic characteristics reflected by the current operating data of the server and the first historical operating data, the energy consumption and response time of the server in the future predetermined time period may be predicted, and the obtained prediction results may include energy consumption prediction values and response time prediction values. In one embodiment, predicting the energy consumption and response time of the server in the future predetermined time period may be achieved with the aid of an energy consumption prediction model. The energy consumption prediction model may be constructed based on a deep learning network, such as a long short-term memory neural network for processing and predicting long-term dependencies in time series data.

[0043] In some embodiments, based on the aforementioned predicted energy consumption and response time values, the initial task allocation plan may be adjusted continuously until the evaluation metric of the adjusted task allocation plan satisfies a predetermined metric condition or a predetermined number of adjustments are made. The predetermined metric condition may be to minimize energy consumption while ensuring that the response time does not exceed a threshold, thereby achieving a balance between energy consumption and response time.

[0044] Figure 3 A schematic diagram of a task allocation adjustment scheme according to the present invention is shown.

[0045] like Figure 3As shown, in one example, a task center may have three servers S1, S2, and S3. These three servers need to execute a total of five tasks T1 to T5. The initial task allocation plan may be S1 executing T1 and T2, S2 executing T3, and S3 executing T4 and T5. In this case, the prediction results obtained based on the server's current operating data and the server's first historical operating data may be an energy consumption prediction value of 1 and a response time prediction value of 1. The evaluation index value obtained based on the energy consumption prediction value 1 and the response time prediction value 1 is index value 1. The initial task allocation plan is randomly adjusted, for example, adjusted to S1 executing T1 and T3, S2 executing T2 and T4, and S3 executing T5. In this case, the prediction results obtained based on the server's current operating data and the second historical operating data randomly extracted from the database may be an energy consumption prediction value of 2 and a response time prediction value of 2. The evaluation index value obtained based on the energy consumption prediction value 2 and the response time prediction value 2 is index value 2. The process of adjusting the task allocation scheme and calculating the evaluation index value can be repeated repeatedly until the number of adjustments reaches a predetermined number of times; or the number of allocation schemes with non-overlapping tasks reaches a predetermined number of permutations and combinations of servers and tasks. At this point, the process of adjusting the task allocation scheme and calculating the evaluation index ends, and the index value z is obtained. z is a positive integer that can be equal to the predetermined number of times or the number of permutations and combinations of servers and tasks, depending on the termination condition. If the termination condition is reaching the predetermined number of adjustments, then z is the predetermined number. The target task allocation scheme can be the task allocation scheme corresponding to the minimum evaluation index value among these evaluation index values.

[0046] In one embodiment, when predicting the energy consumption and response time of the updated task allocation plan, the current operating parameters of the server can be obtained again, and the time point for obtaining the current operating parameters of the server again is later than the time point for processing the energy consumption prediction request of the initial task allocation plan.

[0047] In some embodiments, the predicted heat load of the server can be the amount of heat generated by the server during a predetermined period of time in the future due to execution of tasks. The predicted heat load can be proportional to the predicted energy consumption value, and the predicted heat load of the server can be obtained based on the prediction result.

[0048] In some embodiments, the environmental data of the data center may include temperature data and humidity data. The cooling equipment may include fans and air conditioners. The predetermined environmental condition may be, for example, that the relative humidity of the data center is within a predetermined humidity range.

[0049] In some embodiments, based on the expected heat load, temperature data, and humidity data, a Proportional Integration Differentiation (PID) controller can be used to control the opening and closing of the air conditioner and fan, as well as the wind speed, so that the humidity of the data center is within a predetermined humidity range, thereby enabling the server to execute the target task allocation plan within the predetermined humidity range, reducing energy consumption and improving energy utilization.

[0050] According to an embodiment of the present invention, in response to an energy consumption prediction request, a server's energy consumption and response time for a predetermined time period are predicted based on the server's current operating data and first historical operating data to obtain a prediction result; an initial task allocation plan is adjusted based on the prediction result until predetermined indicators are met; and the operation of the cooling equipment is controlled based on the predicted thermal load and data center environmental data so that the server executes the target task allocation plan under predetermined environmental conditions. Since energy consumption is predicted based on the current operating data and first historical operating data during task execution, energy consumption can be predicted based on the distribution characteristics of the operating data, improving the accuracy of energy consumption prediction and providing a basis for adjusting the task allocation plan under low energy consumption conditions. When adjusting the task allocation plan, the predetermined indicator condition can be set to minimum energy consumption, thereby obtaining a target task allocation plan with minimum energy consumption. When controlling the operation of the cooling equipment, control can be performed based on the server's thermal load and the data center environment, achieving precise adjustment, reducing energy waste, and improving energy utilization. The task execution method of the present invention can reduce energy consumption, improve energy utilization, and enhance server energy efficiency and system execution efficiency by predicting energy consumption, optimizing the task allocation plan based on minimum energy consumption, and implementing closed-loop control of coordinated cooling control.

[0051] In some embodiments, the process of processing the current operating parameters described in operation S210 above may include the following operations: eliminating abnormal parameters from the current operating parameters to obtain intermediate operating parameters; and standardizing the intermediate operating parameters based on the distribution characteristic values of the intermediate operating parameters to obtain current operating data.

[0052] In some embodiments, a high-precision sensor network can be deployed on each server node to collect various operating parameters, including CPU utilization, memory usage, temperature, and power consumption, to obtain current operating parameters. These parameters are then preliminarily cleaned to remove erroneous or abnormal parameters, resulting in intermediate operating parameters.

[0053] For each type of parameter in the intermediate operating parameters, historical parameters of that type can be obtained from the database, and the mean and standard deviation of the historical parameters of that type can be determined to obtain the historical mean and historical standard deviation. The distribution characteristic values of the intermediate operating parameters can be the historical mean and historical standard deviation.

[0054] According to the historical mean and historical standard deviation, a standardization method can be used to standardize each parameter in the intermediate operating parameters to obtain standardized current operating data.

[0055] According to an embodiment of the present invention, during the stage of collecting operating parameters, the high-precision sensor network not only ensures the comprehensiveness and accuracy of parameter collection, but also can detect changes in the server's operating status in real time. By performing preliminary cleaning and standardization on the current operating parameters, noise and outliers can be effectively removed, making the data used for energy consumption prediction and training of energy consumption prediction models more reliable, thereby improving the stability and prediction accuracy of the server energy efficiency improvement system.

[0056] In some embodiments, the process of predicting the energy consumption and response time of the server in a predetermined time period described in operation S220 above may include the following operations: using an energy consumption prediction model to process current operating data and the first historical operating data of the server. The energy consumption prediction model can be trained in the following manner: obtaining a data sample set, wherein the data in the data sample set includes a time point, operating data at the time point, and an actual energy consumption value at the time point; randomly dividing the data in the data sample set into K non-overlapping data sample subsets, K being an integer greater than 1; using any data sample subset as a validation set, and the remaining K-1 data sample subsets as training sets, using the operating data of at least two time points in the training set as input data, and the actual energy consumption value of the time point after at least two time points as a label, to train the initial energy consumption prediction model to obtain a performance index of the initial energy consumption prediction model; adjusting the model parameters of the initial energy consumption prediction model according to the performance index, and returning to the operation of training the initial energy consumption prediction model until the performance index meets the predetermined index value, thereby obtaining the energy consumption prediction model.

[0057] In some embodiments, a sample data set may be obtained from a database. The data sample set may include multiple data groups. Each data group may include a time point, the operating data of the server at that time point, and the actual energy consumption value of the server at that time point.

[0058] In some embodiments, the collected data sample set may also be subjected to preliminary cleaning and standardization processing using the processing method mentioned in operation S210 to obtain a standardized data sample set.

[0059] In some embodiments, the data in the standardized data sample set can be randomly divided into K non-overlapping data sample subsets, where K is an integer greater than 1, and the initial energy consumption prediction model is trained using a cross-validation method. The initial energy consumption prediction model can be a constructed long short-term memory network.

[0060] In some embodiments, any subset of data samples can be used as a validation set, and the remaining K-1 subsets of data samples can be used as training sets to train the initial energy consumption prediction model. When using the training set to train the initial energy consumption prediction model, the operating data of at least two time points in the training set can be used as input data, and the actual energy consumption values at the time points after the at least two time points can be used as labels to train the initial energy consumption prediction model.

[0061] In some embodiments, the data in the training set can be combined into a feature vector . , is the normalized value of CPU utilization, is the normalized value of memory usage, is the normalized value of temperature, is the normalized value of power consumption.

[0062] In some embodiments, in order to enhance the learning ability of the initial energy consumption prediction model, a hysteresis term, that is, operating parameters at at least two time points, can be introduced as input data.

[0063] , t represents any time point, such as the current time point, t-1 is the time point before t, is the lag term.

[0064] In some embodiments, the average actual energy consumption value in a future time period consisting of at least two time points after time t can be used as a label ,The average actual energy consumption value can be obtained by averaging the actual energy consumption values at each ,point in the future time period.

[0065] According to an embodiment of the present invention, in the actual application of a long short-term memory network, directly inputting current operating data may not be sufficient for the energy consumption prediction model to accurately predict the server's future energy consumption. The lag term is essentially a part of the time series data, representing data from past points in time. Inputting the lag term into the energy consumption prediction model as additional information can help the energy consumption prediction model better understand the relationship between the current data and past data, learn the changing trends of energy consumption, and enable the energy consumption prediction model to make predictions based on historical data points, thereby improving the prediction capability of the energy consumption prediction model.

[0066] In some embodiments, using the input data constructed above and tags , adjust the weight parameters in the long short-term memory network through the back propagation algorithm so that the output of the initial energy consumption prediction model is close to the label .

[0067] In some embodiments, a cross-validation method can be used to evaluate the performance indicators of the initial energy consumption prediction model, such as prediction accuracy, and the hyperparameters of the initial energy consumption prediction model can be adjusted according to the verification results until the predetermined prediction accuracy is achieved and the energy consumption prediction model is obtained.

[0068] In some embodiments, the trained energy consumption prediction model can be expressed by formula (1).

[0069] (1)

[0070] in, is the function expression of the energy consumption prediction model, are the trained weight parameters, The input data can be the current operation data and the first historical operation data, It can be the average energy consumption forecast value of the server within a predetermined time period, such as the energy consumption forecast value of the server.

[0071] In some embodiments, long-short-term memory networks can process time series data and possess memory capabilities, enabling them to process correlations between different time periods. By introducing hysteresis terms to enhance the learning capabilities of the energy consumption prediction model, the model can not only make predictions based on current operating data but also incorporate historical data trends to improve prediction accuracy. This improves the accuracy of energy consumption predictions and provides a solid foundation for optimizing workload allocation strategies.

[0072] In some embodiments, the predetermined evaluation indicators described in operation S230 above can be determined as follows: based on the energy consumption prediction value of the server, determine the energy consumption prediction value of the data center; based on the energy consumption prediction value and energy consumption prediction weight of the data center, as well as the response time prediction value and response time weight, determine the evaluation indicators.

[0073] In some embodiments, the evaluation index may be as shown in formula (2).

[0074] (2)

[0075] in, is the evaluation index value, is the response duration weight, is the predicted value of response time, is the energy consumption prediction weight, is the predicted energy consumption of the data center. The predicted energy consumption of the data center, i.e., the total energy consumption of all servers in the data center, can be the sum of the predicted energy consumption values of the servers deployed in the data center. Based on formula (2), the optimization goal can be set to minimize the total energy consumption while ensuring service quality and minimizing the evaluation index value.

[0076] According to an embodiment of the present invention, by setting evaluation indicators, energy consumption can be minimized while ensuring response time, thereby effectively balancing the relationship between resource utilization and response time and improving the overall resource utilization of the data center.

[0077] In some embodiments, the process of obtaining the target task allocation scheme described in operation S230 above may include the following operations: determining an initial evaluation value for the initial task allocation scheme based on the server's energy consumption prediction value and the server's response time prediction value; randomly adjusting the initial task allocation scheme to obtain an updated task allocation scheme; determining an updated evaluation value for the updated task allocation scheme based on the updated energy consumption prediction value and the updated response time prediction value of the updated task allocation scheme; and determining the target task allocation scheme based on the relationship between the updated evaluation value and the initial evaluation value.

[0078] In some embodiments, for the initial task allocation scheme, the current operating data and the first historical operating data can be input into the energy consumption prediction model, and the energy consumption prediction value and the response time prediction value can be output. The energy consumption prediction value and the response time prediction value are input into the function shown in formula (2), and the output evaluation value is used as the initial evaluation value.

[0079] In some embodiments, tasks can be randomly adjusted among multiple servers based on a reinforcement learning algorithm to obtain an updated task allocation scheme. The reinforcement learning algorithm can be shown as formula (3).

[0080] (3)

[0081] in, Assign a plan to the task, The current server status, such as the CPU resource utilization, memory utilization, temperature, and humidity in the server operation data described above, is the decision function of the reinforcement learning algorithm, It can be the average energy consumption forecast value of the server in the predetermined time period obtained by formula (1). , adjust the task distribution on each server, and feed back the actual operation results to the reinforcement learning model as the basis for decision-making.

[0082] In one example, by passing the current server status and average energy consumption forecast Common input to the decision function By setting the current server status Convert it into a vector that can be processed by the neural network, assign tasks according to the processable vector, and select the vector that makes formula (2) The minimum allocation plan is used to obtain the task allocation plan For example, in the current server state In the example, if the server's current CPU usage is as high as 90%, the reinforcement learning algorithm can reduce the probability of assigning new tasks to the server; for example, if It indicates that a server has entered a low energy consumption stage, and the reinforcement learning algorithm can prioritize assigning tasks to this server.

[0083] In some embodiments, the process of randomly adjusting the task allocation plan can be stopped when the number of adjustments reaches a predetermined number; or the adjustment of the task plan can be stopped when the non-overlapping combination plan of tasks reaches a predetermined number of permutations and combinations between servers and tasks.

[0084] In some embodiments, the updated energy consumption prediction value and the updated response time prediction value of the updated task allocation scheme of the above operation can be obtained by randomly extracting the second historical operation data of the server from the database; based on the second historical operation data and the current operation data, the energy consumption and response time of the server in a predetermined time period are predicted to obtain the updated energy consumption prediction value and the updated response time prediction value of the updated task allocation scheme.

[0085] In some embodiments, when predicting the energy consumption of each randomly adjusted updated task plan, the current operation data of the server can be obtained again, and historical data can be randomly extracted from the database again. The historical data extracted again can be used as the second historical operation data. The first historical operation data can be extracted from the database when predicting the initial task allocation plan, and the second historical operation data can be extracted from the database when predicting the updated task allocation plan. The first historical operation data and the second historical operation data can be different. The relationship between the second historical operation data and the first historical operation data includes one of the following: the second historical operation data includes the first historical operation data; the first historical operation data includes the second historical operation data; the second historical operation data and the first historical operation data are partially the same; the second historical operation data and the first historical operation data are completely different.

[0086] In some embodiments, the reacquired current operating data and the reacquired second historical data can be input into a trained energy consumption prediction model to output an energy consumption prediction result for the updated task allocation scheme. When predicting the energy consumption of the updated task allocation scheme, the input data for each updated task allocation scheme is different, that is, the historical operating data is different, and the current operating data can be the same or different. The different situation is that when predicting the energy consumption of the current updated task allocation scheme, the time point at which the current operating data is acquired is later than the time point at which the operating data was acquired when predicting the energy consumption of the previous updated task allocation scheme.

[0087] In some embodiments, the update energy consumption prediction value and the update response time prediction value obtained by each update task allocation scheme can be input into the function shown in formula (2), and the output evaluation value is used as the update evaluation value.

[0088] In some embodiments, the initial evaluation value and the updated evaluation value can be compared, and the task allocation scheme corresponding to the smallest evaluation value can be used as the target task allocation scheme. In one example, if the updated evaluation value is less than the initial evaluation value, the target task allocation scheme can be obtained based on the updated task allocation scheme; if the updated evaluation value is greater than or equal to the initial evaluation value, the target task allocation scheme can be obtained based on the initial task allocation scheme.

[0089] According to an embodiment of the present invention, a trained energy consumption prediction model is used to dynamically analyze and predict current and future workloads, enabling the early identification of potential high-energy consumption periods. Task allocation adjustments based on reinforcement learning algorithms can dynamically adjust task allocation strategies based on real-time workload changes, ensuring that each server achieves the optimal task load. By continuously providing feedback on actual operational performance and optimizing decision functions accordingly, the system can maintain efficient operation in complex and changing environments, further reducing resource waste while improving response speed and service quality.

[0090] In some embodiments, the expected thermal load described in operation S240 above can be obtained by: constructing a thermal load-energy consumption relationship based on the proportional relationship between the thermal load of the server and the predicted energy consumption value, where the thermal load-energy consumption relationship can be represented by formula (4); and processing the predicted energy consumption value of the server based on the thermal load-energy consumption relationship to obtain the expected thermal load of the server.

[0091] In some embodiments, a network of temperature and humidity sensors can be deployed within the data center to monitor the temperature and relative humidity of various areas within the data center in real time. Each area can contain at least one server. Based on the load distribution in the task allocation scheme, the expected thermal load of each server node can be calculated.

[0092] The server's thermal load and energy consumption prediction value can be in a positive proportional relationship, that is, the thermal load increases as the energy consumption increases. Based on this positive proportional relationship, the constructed thermal load-energy consumption relationship can be shown in Formula (4).

[0093] (4)

[0094] in, Indicates the The heat load of each server node, is a proportionality coefficient, is the predicted energy consumption of the node.

[0095] In some embodiments, the expected thermal load of the server can be obtained by inputting the energy consumption prediction value of the target task allocation scheme, that is, the energy consumption prediction value of the initial task allocation scheme, or the energy consumption prediction value of the updated task allocation scheme into formula (4).

[0096] In some embodiments, based on the heat load determined by formula (4), the process of controlling the cooling equipment associated with the data center described in operation S240 above may include the following operations: constructing a heat distribution model of the data center based on the expected heat load and the environmental data of the data center; predicting the environmental change trend of the data center based on the heat distribution model; and controlling the operating parameters of the cooling equipment according to the environmental change trend and the current temperature of the server until the environmental data of the data center meets the predetermined environmental conditions.

[0097] In some embodiments, based on the expected heat load and the environmental data of the data center, the process of constructing a thermal distribution model of the data center may include the following operations: using fluid mechanics to simulate the air flow and heat transfer of the data center to obtain the environmental data of the data center, the environmental data including temperature data and humidity data; determining the expected heat load of the data center based on the expected heat load of the server; and constructing a thermal distribution model based on the expected heat load, temperature data and humidity data of the data center.

[0098] In some embodiments, computational fluid dynamics (CFD) is used to simulate the air flow and heat transfer process inside the data center to construct a heat distribution model. The expression of the heat distribution model can be shown as formula (5).

[0099] (5)

[0100] in, Represents the heat distribution of the entire data center, It is a function expression derived from CFD analysis. is the temperature of each server node, is the relative humidity of each server node, Q is the heat load vector of the data center, that is, the expected heat load of all server nodes in the data center, which can be Add up.

[0101] In some embodiments, an adaptive cooling management system can be constructed based on the heat distribution model to maintain ideal temperature and humidity conditions within the data center by adjusting fan speed and the operating status of the air conditioning system. In one example, computational fluid dynamics (CFD) can be used to simulate the air flow and heat transfer process within the data center to generate a thermal distribution map of the heat distribution model. By analyzing environmental data and operating data collected from the sensor network, the heat distribution model is combined to predict temperature change trends for a predetermined time period in the future. A PID controller is used to adjust the fan speed based on the expected heat load and real-time temperature feedback of each server node. The temperature set point of the air conditioning system is dynamically adjusted based on the overall heat load of the data center and the external environmental conditions, and the relative humidity is controlled to remain between 40% and 60%. Based on the optimization results, specific control instructions are generated to adjust the operating status of the fan equipment and turn the air conditioning system on or off.

[0102] According to an embodiment of the present invention, by real-time monitoring of the temperature and humidity environmental conditions within the data center and combining the task allocation plan to calculate the expected heat load of each server node, the set point of the air conditioning system can be adjusted in advance by predicting the temperature change trend through the heat distribution model to avoid unnecessary energy consumption; combined with the temperature change trend, the fan speed and the operating status of the air conditioning system can be more finely controlled to achieve the purpose of energy conservation and emission reduction, and the working status of the cooling system can be accurately controlled. Using CFD to simulate the air flow and heat transfer process within the data center and construct a heat distribution model can not only accurately predict the air flow and heat transfer process within the data center, but also dynamically adjust the settings of fans and air conditioners according to actual conditions, thereby avoiding excessive or insufficient cooling problems, achieving the optimal cooling effect configuration, and reducing energy consumption.

[0103] In some embodiments, the present invention can also establish a continuous monitoring mechanism to regularly evaluate the effectiveness of optimal cooling effect configurations, task allocation schemes, and energy consumption prediction models, and provide feedback adjustments to the strategies and models based on the latest data analysis results to obtain optimized operating parameters. In one example, a performance indicator value for a server can be determined based on current operating data, where the performance indicator value includes at least one of the following: power usage efficiency, temperature control accuracy, relative humidity level, cooling equipment energy consumption, and server utilization; based on the deviation between the performance indicator value and a predetermined threshold, the model parameters of the energy consumption prediction model are feedback-adjusted to obtain an updated energy consumption prediction model.

[0104] In some embodiments, a closed-loop control system can be constructed to automatically collect the above-mentioned current operating data, calculate deviation information, and feed the deviation information as input to the adaptive cooling management system, the task allocation adjustment algorithm, and the energy consumption prediction model to trigger corresponding adjustment actions. Based on the feedback data, the weight parameters in the energy consumption prediction model can be adjusted to make the energy consumption prediction model better fit the data pattern. Optimizing the decision function in the task allocation adjustment algorithm, that is, the decision function in the reinforcement learning algorithm, can ensure that the optimal performance can still be achieved under the updated workload conditions. value.

[0105] In one embodiment, the closed-loop control system may include a data acquisition layer, a deviation analysis and feedback layer, and an execution adjustment layer.

[0106] The data collection layer can obtain current operation data through sensors, monitoring tools and log analysis. The current operation data may include at least one of energy consumption data, environmental condition data, server performance data and task allocation plan data.

[0107] Energy consumption data may include the actual power consumption values of server nodes in different time periods, real-time monitoring results of the overall energy consumption of the data center, energy consumption of information technology (IT) equipment, energy consumption records during each task execution, and energy consumption data of fans and air conditioning systems.

[0108] Environmental condition data can include real-time measurements of temperature and relative humidity in various areas within the data center, temperature difference data between server air inlets and exhaust ports, and parameters that indicate the impact of external ambient temperature on data center cooling efficiency.

[0109] Server performance data may include CPU utilization, memory usage, disk I / O rate, load generated by each application when running, network traffic status and its impact on server performance, etc.

[0110] The task allocation plan data may include the distribution of tasks on different servers, the workload adjustment plan derived from the energy consumption prediction model, and the task allocation plan generated by the reinforcement learning algorithm.

[0111] The deviation analysis and feedback layer uses data collected by the data collection layer to calculate server performance indicators. For example, it can determine power efficiency based on the ratio of the data center's overall energy consumption to IT equipment energy consumption. It can also determine temperature control accuracy based on the difference between the preset temperatures in various areas of the data center. It can also determine relative humidity levels based on real-time relative humidity measurements. It can also determine cooling equipment energy consumption based on fan and air conditioning system energy consumption data. Server utilization can be determined based on CPU utilization, memory utilization, and disk I / O rates.

[0112] In some embodiments, each performance indicator may be provided with an indicator threshold, and a deviation analysis and feedback layer may calculate the deviation between the performance indicator value and the indicator threshold. In the event that the deviation of the indicator values of power efficiency, temperature control accuracy, relative humidity level, cooling equipment energy consumption, and server utilization is within a predetermined deviation, no feedback adjustment may be performed; in the event that the deviation of at least one indicator value of power efficiency, temperature control accuracy, relative humidity level, cooling equipment energy consumption, and server utilization is not within the predetermined deviation, a feedback adjustment operation may be triggered. By providing a fault tolerance space such as a predetermined deviation range, frequent triggering of feedback adjustment operations can be avoided, reducing the energy consumption of the server caused by the frequent execution of unnecessary operations, improving the energy utilization of the server, and extending its service life.

[0113] When the deviation analysis and feedback layer determines that feedback adjustment operations are required, the execution adjustment layer can filter out abnormal performance indicators whose deviations are outside the predetermined deviation range from performance indicators such as power efficiency, temperature control accuracy, relative humidity level, cooling equipment energy consumption, and server utilization, and adjust the components related to the abnormal performance indicators. In one example, the power efficiency indicator is abnormal. Since the power efficiency is obtained based on the energy consumption of the data center, the energy consumption of the data center is obtained based on the energy consumption of each server node, and the energy consumption of the server node is obtained through the energy consumption prediction model, if the power efficiency indicator is abnormal, it may indicate that the energy consumption prediction model has an inaccurate prediction problem. In this case, the true label can be supplemented for the erroneous sample, and the energy consumption prediction model can be continued to be trained using the samples with the supplemented true label to achieve feedback adjustment of the energy consumption prediction model.

[0114] According to an embodiment of the present invention, the data collection layer, the deviation analysis and feedback layer, and the execution adjustment layer can regularly implement the process of regularly evaluating the energy consumption prediction model, task allocation plan, and cooling management, timely locate problems existing in the server energy efficiency improvement system, and perform feedback adjustment on the energy consumption prediction model, task allocation plan, and cooling management to ensure the reliability of the server energy efficiency improvement system, improve the execution efficiency of the server energy efficiency improvement system, and reduce energy consumption.

[0115] Apply the updated energy consumption prediction model, task allocation plan and cooling management strategy, and closely observe the actual operation status of the data center; based on the updated energy consumption prediction model, task allocation plan and cooling management strategy, a detailed optimization report can be generated and provided to managers for reference.

[0116] According to an embodiment of the present invention, the design of a continuous detection mechanism enables the server energy efficiency improvement system to regularly evaluate the actual effects of various energy consumption prediction models, task allocation plans and cooling management strategies, and to make feedback adjustments to the strategies and models based on the latest data analysis results. The closed-loop control system can not only promptly discover and resolve potential problems, but also continuously optimize operating parameters to ensure that the system is always in the best operating state. By generating detailed optimization reports, managers can intuitively understand the operating status of the system and make more scientific decisions based on this, further improving the management efficiency and energy efficiency performance of the data center.

[0117] Figure 4 A flowchart of a task execution method according to another embodiment of the present invention is shown.

[0118] like Figure 4 As shown, the task execution method of this embodiment may include operations S410 to S460.

[0119] In operation S410, the server operating parameters are collected and pre-processed by using a sensor network to obtain standardized server operating data. In one embodiment, operation S410 may refer to operation S210.

[0120] In operation S420, a machine learning algorithm is used to train an energy consumption prediction model on the standardized server operation data to obtain an energy consumption prediction model based on historical data.

[0121] In operation S430, the energy consumption prediction model is used to dynamically analyze and predict current and future server workloads to obtain an optimized workload allocation strategy. In one embodiment, operations S420 to S430 can refer to operation S220. The energy consumption prediction model does not need to be trained before each prediction. The energy consumption prediction model can be pre-trained and can be directly called to perform predictions when predicting energy consumption.

[0122] In operation S440, the optimized workload distribution strategy is applied to the intelligent workload distribution system, and a reinforcement learning algorithm is used to dynamically adjust the distribution of tasks among multiple servers to obtain a task distribution solution. In one embodiment, operation S440 can refer to operation S230.

[0123] In operation S450, by real-time monitoring of the temperature and humidity environment conditions in the data center and combining the task allocation plan, an adaptive cooling management method is used to automatically adjust the working state of the fan equipment to obtain the optimal cooling effect configuration. In one embodiment, operation S450 can refer to operation S240.

[0124] In operation S460, a continuous monitoring mechanism is established to regularly evaluate the effectiveness of the optimal cooling effect configuration, task allocation plan, and energy consumption prediction model, and feedback adjustments are made to the strategy and model based on the latest data analysis results to obtain optimized operating parameters. In one embodiment, operation S460 can refer to the process described above of determining the performance index value of the server based on current operating data, and feedback-adjusting the model parameters of the energy consumption prediction model, the decision function of the task allocation plan, and the cooling management system based on the deviation between the performance index value and a predetermined threshold.

[0125] According to an embodiment of the present invention, by adopting a sensor network to collect and preprocess the server operating parameters, standardized data processing is achieved, the quality and consistency of the data are ensured, and a reliable data basis is provided for the training of the energy consumption prediction model, thereby improving the accuracy and reliability of the model. By using the energy consumption prediction model to dynamically analyze and predict the current and future server workloads, an optimized workload distribution strategy is implemented, which not only helps to reduce energy consumption, but also ensures that the service quality is not affected. The effect of each solution is quantified through comprehensive evaluation indicators to ensure efficient use of resources and improve overall energy utilization efficiency. By applying the optimized workload distribution strategy to the intelligent workload distribution system, dynamic adjustment of tasks among multiple servers is achieved, ensuring that the task allocation of each server reaches the optimal state, further reducing unnecessary energy consumption, and improving the system's response speed and service quality.

[0126] It should be noted that, unless it is clearly stated that there is a sequence of execution between different operations shown in the flowchart in the embodiments of the present invention, or there is a sequence of execution between different operations in technical implementation, otherwise, the execution order of multiple operations may not be prioritized, and multiple operations may also be executed simultaneously.

[0127] Based on the above task execution method, the present invention also provides a task execution device. Figure 5 The device is described in detail.

[0128] Figure 5 A structural block diagram of a task execution device according to an embodiment of the present invention is shown.

[0129] like Figure 5 As shown, the task execution device 500 of this embodiment includes a processing module 510 , a prediction module 520 , an adjustment module 530 and a control module 540 .

[0130] The processing module 510 is configured to process the current operating parameters of the server in response to the energy consumption prediction request for the initial task allocation solution to obtain current operating data.

[0131] The prediction module 520 is used to predict the energy consumption and response time of the server in a predetermined time period according to the current operation data and the first historical operation data of the server to obtain a prediction result.

[0132] The adjustment module 530 is used to adjust the initial task allocation plan according to the prediction result until the evaluation index based on the adjusted task allocation plan meets the predetermined index condition, thereby obtaining the target task allocation plan.

[0133] The control module 540 is used to control the operation of the cooling equipment associated with the data center according to the expected heat load obtained based on the prediction results and the environmental data of the data center where the server is located, until the environmental data of the data center meets the predetermined environmental conditions, so that the server executes the target task allocation plan under the predetermined environmental conditions.

[0134] According to an embodiment of the present invention, in response to an energy consumption prediction request, the server's energy consumption and response time in a predetermined time period are predicted based on the server's current operating data and first historical operating data to obtain a prediction result; the initial task allocation plan is adjusted based on the prediction result until a predetermined indicator condition is met; and the operation of the cooling equipment is controlled based on the predicted thermal load and the environmental data of the data center so that the server executes the target task allocation plan under predetermined environmental conditions. Since the energy consumption is predicted in combination with the current operating data and the first historical operating data during the task execution process, it is beneficial to predict energy consumption based on the distribution characteristics of the operating data, improve the accuracy of energy consumption prediction, and provide a basis for adjusting the task allocation plan under low energy consumption conditions. When adjusting the task allocation plan, the predetermined indicator condition can be set to minimum energy consumption, so that a target task allocation plan with minimum energy consumption can be obtained. When controlling the operation of the cooling equipment, it can be controlled based on the server's thermal load and the environment of the data center, which can achieve precise adjustment, reduce energy waste, and improve energy utilization. The task execution method of the present invention can reduce energy consumption and improve energy utilization by predicting energy consumption, optimizing the task allocation plan based on minimum energy consumption, and closed-loop control of coordinated cooling control, and has the effect of improving the energy efficiency of the server and enhancing the execution efficiency of the system.

[0135] Optionally, the task execution device may further include a first determination module and a second determination module.

[0136] The first determining module is used to determine the energy consumption prediction value of the data center according to the energy consumption prediction value of the server.

[0137] The second determination module is used to determine the evaluation index according to the energy consumption prediction value and energy consumption prediction weight of the data center, and the response time prediction value and response time weight.

[0138] Optionally, the adjustment module 530 may include a first determination submodule, an adjustment submodule, a second determination submodule, and a third determination submodule.

[0139] A first determining submodule is configured to determine an initial evaluation value for an initial task allocation scheme based on a predicted value of energy consumption of the server and a predicted value of response time of the server;

[0140] The adjustment submodule is used to randomly adjust the initial task allocation plan to obtain an updated task allocation plan.

[0141] The second determining submodule is configured to determine an updated evaluation value for updating the task allocation scheme according to the updated energy consumption prediction value and the updated response time prediction value of the updated task allocation scheme.

[0142] The third determination submodule is used to determine the target task allocation plan according to the relationship between the updated evaluation value and the initial evaluation value.

[0143] Optionally, the third determining submodule may include a first result unit and a second result unit.

[0144] The first result unit is used to obtain a target task allocation plan according to the updated task allocation plan when the updated evaluation value is less than the initial evaluation value.

[0145] The second result unit is used to obtain a target task allocation plan according to the initial task allocation plan when the updated evaluation value is greater than or equal to the initial evaluation value.

[0146] Optionally, the second determining submodule may include an extraction unit and a prediction unit.

[0147] The extraction unit is used to randomly extract the second historical operation data of the server from the database.

[0148] The prediction unit is used to predict the energy consumption and response time of the server in a predetermined time period based on the second historical operation data and the current operation data, and obtain an updated energy consumption prediction value and an updated response time prediction value of the updated task allocation scheme.

[0149] Optionally, the relationship between the second historical operating data and the first historical operating data includes one of the following: the second historical operating data includes the first historical operating data; the first historical operating data includes the second historical operating data; the second historical operating data and the first historical operating data are partially identical; the second historical operating data and the first historical operating data are completely different.

[0150] Optionally, the task execution device may further include a construction module and a result module.

[0151] The building module is used to build a heat load-energy consumption relationship according to the proportional relationship between the heat load of the server and the energy consumption prediction value.

[0152] The result module is used to process the energy consumption prediction value of the server based on the heat load-energy consumption relationship to obtain the expected heat load of the server.

[0153] Optionally, the control module 540 may include a construction submodule, a prediction submodule, and a control submodule.

[0154] The construction submodule is used to build a thermal distribution model of the data center based on the expected heat load and the environmental data of the data center.

[0155] The prediction submodule is used to predict the environmental change trend of the data center based on the thermal distribution model.

[0156] The control submodule is used to control the operating parameters of the cooling equipment according to the environmental change trend and the current temperature of the server until the environmental data of the data center meets the predetermined environmental conditions.

[0157] Optionally, the construction submodule may include a simulation unit, a determination unit and a construction unit.

[0158] The simulation unit is used to simulate the air flow and heat transfer in the data center by using fluid mechanics to obtain the environmental data of the data center, which includes temperature data and humidity data.

[0159] The determining unit is configured to determine an expected heat load of the data center according to the expected heat load of the server.

[0160] The building unit is used to build a heat distribution model according to the expected heat load, temperature data and humidity data of the data center.

[0161] Optionally, the prediction module 520 may include a processing submodule, an acquisition submodule, a division submodule, a training submodule, and an adjustment submodule.

[0162] The processing submodule is used to process the current operation data and the first historical operation data of the server by using the energy consumption prediction model.

[0163] The acquisition submodule is used to acquire a data sample set, wherein the data in the data sample set includes a time point, operating data at the time point, and an actual energy consumption value at the time point.

[0164] The partitioning submodule is used to randomly divide the data in the data sample set into K non-overlapping data sample subsets, where K is an integer greater than 1.

[0165] The training submodule is used to use any data sample subset as a validation set and the remaining K-1 data sample subsets as training sets, use the operating data of at least two time points in the training set as input data, and use the actual energy consumption values at at least two time points after the time points as labels to train the initial energy consumption prediction model and obtain the performance indicators of the initial energy consumption prediction model.

[0166] The adjustment submodule is used to adjust the model parameters of the initial energy consumption prediction model according to the performance index, and return to the operation of training the initial energy consumption prediction model until the performance index meets the predetermined index value to obtain the energy consumption prediction model.

[0167] Optionally, the task execution device may further include a third determination module and an adjustment module.

[0168] The third determination module is used to determine the performance indicator value of the server based on the current operation data, wherein the performance indicator value includes at least one of the following: power utilization efficiency, temperature control accuracy, relative humidity level, cooling equipment energy consumption and server utilization.

[0169] The adjustment module is used to feedback and adjust the model parameters of the energy consumption prediction model according to the deviation between the performance indicator value and the predetermined threshold value to obtain an updated energy consumption prediction model.

[0170] Optionally, the processing module 510 may include a rejection submodule and a processing submodule.

[0171] The elimination submodule is used to eliminate abnormal parameters from the current operating parameters to obtain intermediate operating parameters.

[0172] The processing submodule is used to perform standardization processing on the intermediate operating parameters based on the distribution characteristic values of the intermediate operating parameters to obtain current operating data.

[0173] According to embodiments of the present invention, any multiple modules among the processing module 510, prediction module 520, adjustment module 530, and control module 540 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present invention, at least one of the processing module 510, prediction module 520, adjustment module 530, and control module 540 may 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 may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination of these. Alternatively, at least one of the processing module 510, prediction module 520, adjustment module 530, and control module 540 may be at least partially implemented as a computer program module that, when executed, performs the corresponding functionality.

[0174] Figure 6 A block diagram of an electronic device suitable for implementing a task execution method according to an embodiment of the present invention is shown.

[0175] like Figure 6 As shown, an electronic device 600 according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 602 or programs loaded from a storage unit 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or related chipsets and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0176] Various programs and data required for the operation of the electronic device 600 are stored in the RAM 603. The processor 601, ROM 602, and RAM 603 are connected to each other via a bus 604. The processor 601 executes the programs in the ROM 602 and / or RAM 603 to perform various operations according to the method flow of the embodiment of the present invention. It should be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also execute the programs stored in the one or more memories to perform various operations according to the method flow of the embodiment of the present invention.

[0177] According to an embodiment of the present invention, electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to bus 604. Electronic device 600 may also include one or more of the following components connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or modem. Communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. Removable media 611, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 610 as needed, so that computer programs read from the removable media can be installed into storage section 608 as needed.

[0178] The present invention 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 incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0179] According to an embodiment of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but 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 invention, 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 invention, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above, and / or one or more memories other than ROM 602 and RAM 603.

[0180] The embodiments of the present invention further 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 cause the computer system to implement the task execution method provided by the embodiments of the present invention.

[0181] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when the computer program is executed by the processor 601. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0182] 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 609, and / or installed from a removable medium 611. 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.

[0183] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609 and / or installed from a removable medium 611. When the computer program is executed by the processor 601, the above-described functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0184] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention 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 the case of 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).

[0185] 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 invention. 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.

[0186] It will be understood by those skilled in the art that the features described in the various embodiments of the present invention may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention may be combined and / or coupled in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or couplings fall within the scope of the present invention.

[0187] The above describes embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. 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 invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A task execution method, characterized in that: The method comprises: In response to the energy consumption prediction request for the initial task allocation plan, processing the current operating parameters of the server to obtain current operating data; The energy consumption prediction model is used to process the current operation data and the first historical operation data of the server, and the energy consumption and response time of the server in a predetermined time period are predicted to obtain a prediction result. The energy consumption prediction model is trained in the following manner: a data sample set is obtained, and the data in the data sample set includes a time point, operation data at the time point, and an actual energy consumption value at the time point; the data in the data sample set is randomly divided into K non-overlapping data sample subsets, where K is an integer greater than 1; any data sample subset is used as a validation set, and the remaining K-1 data sample subsets are used as training sets, and the operation data of at least two time points in the training set are used as input data, and the actual energy consumption value of the time point after the at least two time points is used as a label to train the initial energy consumption prediction model to obtain a performance index of the initial energy consumption prediction model; the model parameters of the initial energy consumption prediction model are adjusted according to the performance index, and the operation of training the initial energy consumption prediction model is returned until the performance index meets the predetermined index value, thereby obtaining the energy consumption prediction model; Adjusting the initial task allocation plan according to the prediction result until the evaluation index based on the adjusted task allocation plan meets the predetermined index condition, thereby obtaining a target task allocation plan; Based on the expected heat load obtained based on the prediction result and the environmental data of the data center where the server is located, the operation of the cooling equipment associated with the data center is controlled until the environmental data of the data center meets the predetermined environmental conditions, so that the server executes the target task allocation plan under the predetermined environmental conditions.

2. The method according to claim 1, characterized in that The prediction result includes a predicted value of energy consumption of the server and a predicted value of response time of the server; The evaluation index is determined by the following method: Determining a predicted energy consumption value of the data center based on the predicted energy consumption value of the server; The evaluation index is determined according to the energy consumption prediction value and energy consumption prediction weight of the data center, and the response time prediction value and response time weight.

3. The method according to claim 2, characterized in that The initial task allocation plan is adjusted according to the prediction result until the evaluation index based on the adjusted task allocation plan meets the predetermined index condition, thereby obtaining a target task allocation plan, including: Determining an initial evaluation value for the initial task allocation scheme according to the predicted value of the energy consumption of the server and the predicted value of the response time of the server; Randomly adjusting the initial task allocation plan to obtain an updated task allocation plan; Determining an update evaluation value for the update task allocation scheme based on the update energy consumption prediction value and the update response time prediction value of the update task allocation scheme; The target task allocation scheme is determined according to the relationship between the updated evaluation value and the initial evaluation value.

4. The method according to claim 3, characterized in that Determining the target task allocation scheme according to the relationship between the updated evaluation value and the initial evaluation value includes: When the updated evaluation value is less than the initial evaluation value, obtaining the target task allocation plan according to the updated task allocation plan; In a case where the updated evaluation value is greater than or equal to the initial evaluation value, the target task allocation plan is obtained according to the initial task allocation plan.

5. The method according to claim 3, characterized in that The update energy consumption prediction value and update response time prediction value of the update task allocation scheme are obtained as follows: Randomly extracting second historical operation data of the server from the database; Based on the second historical operation data and the current operation data, the energy consumption and response time of the server in the predetermined time period are predicted to obtain the updated energy consumption prediction value and the updated response time prediction value of the updated task allocation scheme.

6. The method according to claim 5, characterized in that The relationship between the second historical operation data and the first historical operation data includes one of the following: The second historical operation data includes the first historical operation data; The first historical operation data includes the second historical operation data; The second historical operation data is partially identical to the first historical operation data; The second historical operating data is completely different from the first historical operating data.

7. The method according to claim 2, characterized in that Also includes: Constructing a heat load-energy consumption relationship based on a proportional relationship between the heat load of the server and the energy consumption prediction value; The energy consumption prediction value of the server is processed based on the heat load-energy consumption relationship to obtain the expected heat load of the server.

8. The method according to claim 7, characterized in that The step of controlling the operation of cooling equipment associated with the data center according to the expected heat load obtained based on the prediction result and environmental data of the data center where the server is located, until the environmental data of the data center meets a predetermined environmental condition, includes: constructing a heat distribution model of the data center based on the expected heat load and environmental data of the data center; predicting an environmental change trend of the data center based on the heat distribution model; According to the environmental change trend and the current temperature of the server, the operating parameters of the cooling device are controlled until the environmental data of the data center meets the predetermined environmental conditions.

9. The method according to claim 8, characterized in that The step of constructing a heat distribution model of the data center based on the expected heat load and environmental data of the data center includes: Using fluid mechanics to simulate air flow and heat transfer in the data center to obtain environmental data of the data center, the environmental data including temperature data and humidity data; determining an expected heat load of the data center based on the expected heat load of the server; The heat distribution model is constructed according to the expected heat load of the data center, the temperature data, and the humidity data.

10. The method according to claim 1, characterized in that The method further comprises: Determining a performance indicator value of the server based on the current operating data, wherein the performance indicator value includes at least one of the following: power usage efficiency, temperature control accuracy, relative humidity level, cooling equipment energy consumption, and server utilization; According to the deviation between the performance indicator value and the predetermined threshold, the model parameters of the energy consumption prediction model are adjusted through feedback to obtain an updated energy consumption prediction model.

11. The method according to claim 1, wherein The processing of the current operating parameters of the server to obtain the current operating data includes: Eliminating abnormal parameters from the current operating parameters to obtain intermediate operating parameters; Based on the distribution characteristic value of the intermediate operating parameter, the intermediate operating parameter is standardized to obtain the current operating data.

12. 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 11.

13. 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 11 are implemented.

14. A computer program product comprising a computer program or instructions, 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 11 are implemented.

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