Workload control support device and workload control support method

Abstract

To provide a workload control support device and workload control support method that control each workload executed using renewable energy while considering cost-effectiveness.SOLUTION: A management computer being a workload control support device executes a parameter determination program which acquires an executable period and a prediction value of a power consumption amount of each of multiple power-consuming workloads scheduled to be executed in future time zones, and calculates a target value of the power consumption amount in the future time zones such that a condition on the utilization rate of renewable energy and a condition on the cost associated with the use of renewable energy in power consumption in the future time zones are satisfied on the basis of the acquired executable period and each prediction value of the power consumption amount; and an IT workload control program which determines the execution timing of each workload in the future time zones on the basis of the calculated target value of the power consumption amount and executes each workload at the determined timing.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a workload control support device and a workload control support method.

Background Art

[0002] So-called decarbonization, which aims to move away from fossil fuels to prevent the emission of greenhouse gases such as carbon dioxide that cause global warming, has attracted attention. In this regard, in a data center (DC), a large number of information processing devices and communication devices for executing predetermined jobs are set up, and a large amount of power is required for their operation. Therefore, attempts have been made to achieve decarbonization by covering such power with renewable energy.

[0003] In this case, it is important to maintain the utilization rate of renewable energy with respect to power consumption (renewable energy utilization rate: renewable energy rate), and it is preferable to maintain this renewable energy rate at a finer time granularity (for example, in units of time rather than in units of days).

[0004] In this regard, Non-Patent Document 1 describes that in a job system including a batch job whose execution timing can be changed and an interactive job whose execution timing cannot be changed, the difference between the power generation amount by renewable energy and the power consumption is reduced by shifting the execution timing of the batch job.

Prior Art Documents

Non-Patent Documents

[0005]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] However, Non-Patent Document 1 does not consider the cost associated with the procurement of renewable energy. Generally, the procurement of renewable energy often incurs a corresponding cost, and such cost considerations cannot be made in Non-Patent Document 1. In particular, the aspect of cost-effectiveness cannot be considered. Further, Non-Patent Document 1 assumes that the power consumption that will occur in the future due to the workload and the plan of the workload are known, but such cases where they are known are not many in reality.

[0007] The present invention has been made in view of such a background, and an object thereof is to provide a workload control support device and a workload control support method capable of controlling each workload executed by renewable energy while considering cost-effectiveness.

Means for Solving the Problems

[0008] One aspect of the present invention for solving the above problems has a processor and a memory. The memory stores predicted values of the executable period and power consumption amount of each of a plurality of workloads that consume power and are scheduled to be executed in a future time period. The processor determines the timing of each workload to be executed in the future time period so as to satisfy the condition of the utilization rate of renewable energy in the power consumption of the future time period and the condition of the cost related to the use of power, based on each predicted value of the executable period and the power consumption amount. It is a workload control support device, characterized by this.

Effect of the Invention

[0009] According to the present invention, each workload executed by renewable energy can be controlled while considering cost-effectiveness. Problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

Brief Description of the Drawings

[0010]

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Embodiments for Carrying Out the Invention

[0011] FIG. 1 is a diagram showing an example of the configuration of a workload control system 1 according to the present embodiment. The workload control system 1 includes one or more data centers 1000 (Data Center: DC). The data centers 1000 are communicably connected by a wide area network 7000.

[0012] The data center 1000 includes a management computer 2000, one or more server devices 3000 used by the administrator or user of the data center 1000, and one or more storage devices 4000 used by the administrator or user of the data center 1000. The server device 3000 and the storage device 4000 are communicably connected via a data network 6000. Also, the management computer 2000, the server device 3000, and the storage device 4000 are communicably connected via a management network 5000.

[0013] Note that the management network 5000, the data network 6000, and the wide area network 7000 are wired or wireless communication networks such as the Internet, a LAN (Local Area Network), a WAN (Wide Area Network), or a dedicated line.

[0014] The server device 3000 and the storage device 4000 execute various types of processing. For example, in addition to processing with a fixed execution time slot (hereinafter also referred to as a time slot), such as a Web application (hereinafter referred to as an interactive job), the server device 3000 and the storage device 4000 execute processing related to artificial intelligence (AI) (for example, processing related to machine learning), which is processing where the time slot is not necessarily fixed but must be executed by at least a certain time period (hereinafter referred to as a batch job). Note that hereinafter, the batch job and the interactive job are collectively referred to as a job.

[0015] The management computer 2000 manages the processing load on the system (data center 1000) (hereinafter referred to as the batch workload) in time slot units due to batch jobs that have been executed and are scheduled to be executed in the future on the server device 3000 and the storage device 4000. Similarly, the management computer 2000 manages the processing load on the system (data center 1000) (hereinafter referred to as the batch interactive workload) in time slot units due to interactive jobs that have been executed and are scheduled to be executed in the future on the server device 3000 and the storage device 4000.

[0016] Note that in this specification, the workload may be referred to as the job (processing) itself.

[0017] By the way, a predetermined amount of power is required to execute each process in the server device 3000 and the storage device 4000. However, in the data center 1000, it is required to consume a predetermined ratio of this power with power derived from renewable energy rather than power from the power grid. That is, in this embodiment, this predetermined ratio (utilization rate as the minimum condition) is referred to as the target value or target rate of the renewable energy utilization rate. And the power generation amount of this renewable energy has the characteristic of fluctuating depending on the time zone.

[0018] Therefore, the management computer 2000 (workload control support device) of this embodiment sets a power consumption target value for the execution of each batch job in a future time slot, taking into account the predicted value of the power generation amount of renewable energy and the perspectives of the renewable energy rate and cost, and controls the execution timing of each batch job so as to maximize the achievement of this power consumption target value (more specifically, determines the batch jobs to be executed in each time slot at the timing before the start of each time slot), thereby supporting the maintenance of an appropriate balance between the renewable energy rate and cost in the data center 1000. Note that hereinafter, "renewable energy" may be abbreviated as "renewable energy".

[0019] Next, FIG. 2 is a diagram for explaining an example of the hardware and functions included in the management computer 2000 (workload control support device).

[0020] The management computer 2000 includes a processing device 11000 (processor) such as a CPU (Central Processing Unit), DSP (Digital Signal Processor), GPU (Graphics Processing Unit), FPGA (Field-Programmable Gate Array), a main storage device 12000 (memory) such as a ROM (Read Only Memory) and a RAM (Random Access Memory), a storage device 8000 such as an HDD (Hard Disk Drive) and an SSD (Solid State Drive), a communication device 16000 configured by a NIC (Network Interface Card), a wireless communication module, a USB (Universal Serial Interface) module, or a serial communication module, an input device 14000 configured by a mouse, a keyboard, etc., and an output device 15000 configured by a liquid crystal display or an organic EL (Electro-Luminescence) display.

[0021] The management computer 2000 stores each program of a parameter determination program 8700, a risk tolerance calculation program 8800, an IT workload control program 8900, and a power consumption price prediction program 9000.

[0022] The parameter determination program 8700 acquires predicted values of the executable periods and power consumption amounts of a plurality of workloads scheduled to be executed in future time slots. Then, based on the predicted values of the executable periods and power consumption amounts, the parameter determination program 8700 calculates a target value of the power consumption amount in a future time slot so as to satisfy the target value of the renewable energy rate in the future time slot and the conditions of the cost related to the use of renewable energy.

[0023] In this embodiment, the delay limit time is used as the executable time. The delay limit time is the latest time that can be set as the execution timing of the batch job.

[0024] Note that the parameter determination program 8700 receives a specification of a control policy on which of the conditions of the renewable energy rate and cost to prioritize. When a control policy that prioritizes the renewable energy rate is specified, it identifies a parameter α indicating a pattern of the execution timing of the batch workload that optimizes the utilization rate of renewable energy, and calculates a target value of the power consumption amount in future time slots based on the identified parameter α. On the other hand, when a control policy that prioritizes the cost condition is specified, the parameter determination program 8700 identifies a parameter α indicating a pattern of the execution timing of the batch workload that optimizes the cost related to the use of renewable energy, and calculates a target value of the power consumption amount in future time slots based on the identified parameter α.

[0025] Note that in this embodiment, the parameter α is a parameter indicating the ratio of the batch workloads that are actually executed in a certain time slot among the batch workloads that have been scheduled for execution up to that time slot. In this embodiment, the parameter α has a value from 0 to 1 for each time slot. Note that this is an example, and other values may be adopted as long as the ratio of the batch workloads that are actually executed among the batch workloads scheduled for execution in a certain time slot is reflected.

[0026] The IT workload control program 8900 calculates a risk tolerance indicating the risk due to the uncertainty of the predicted value of the power consumption amount in future time slots by a predetermined algorithm, determines the timing of each workload to be executed in future time slots based on the calculated risk tolerance and the target value of the power consumption amount, and causes each workload to be executed at the determined timing.

[0027] The risk tolerance calculation program 8800 calculates the risk tolerance based on the difference between the predicted power generation amount of renewable energy in a future time slot and the target value of the power consumption amount in the future time slot.

[0028] The power consumption price prediction program 9000 calculates the executable period of the workload scheduled to be executed in a future time slot, the predicted value of the power consumption amount, the predicted value of the power generation amount in the future time slot, the predicted value of the power price in the future time slot, and so on.

[0029] Furthermore, the management computer 2000 stores the databases of the DC power prediction table 8100, the time slot table 8200, the delay limit time prediction distribution table 8300, the user policy table 8400, the workload table 8500, the workload power consumption prediction distribution table 8600, and the predicted workload table 8650.

[0030] The DC power prediction table 8100 stores the predicted power generation amount and price of renewable energy predicted by the power consumption price prediction program 9000, the predicted price of the power provided from the power grid, and their actual values.

[0031] The time slot table 8200 stores the information of each time slot, such as the predicted value and actual value of the power consumption in each time slot, the target value of the power consumption, the parameter α, and the risk tolerance.

[0032] The delay limit time prediction distribution table 8300 stores the information of the distribution of the predicted values of the delay limit time of the workload in each future time slot.

[0033] The user policy table 8400 stores data on policies (user policies) regarding the use of renewable energy by the user, such as the target value of the renewable energy rate (hereinafter referred to as the target rate) and the control policy of the workload. In this embodiment, the renewable energy utilization rate (renewable energy rate) is defined as the ratio of the power consumption by renewable energy to the total power consumption in a certain time period, but it may also be based on other definitions.

[0034] The workload table 8500 stores and accumulates information regarding the execution schedule of each workload (batch workload and interactive workload). The server device 3000 and the storage device 4000 execute each workload according to this workload table 8500.

[0035] The workload power consumption prediction distribution table 8600 stores information on the distribution of the predicted values of the power consumption of each workload.

[0036] The predicted workload table 8650 stores the predicted information of each workload for future time slots. Next, specific examples of each database will be described.

[0037] (DC Power Prediction Table) FIG. 3 is a diagram showing an example of the DC power prediction table 8100. The DC power prediction table 8100 includes a time slot ID 8110 in which identification information of a time slot is set, a time 8120 in which a prediction or measurement target time is set, a predicted value of the power generation amount of renewable energy (power available to the data center 1000) at the target time, which is set as the renewable energy power generation amount prediction 8130, an actual measured value of the power generation amount of renewable energy actually measured at the target time, which is set as the renewable energy power generation amount actual measurement 8140, a predicted value of the price per unit power of renewable energy at the target time, which is set as the renewable energy price prediction 8150, an actual measured value of the price of renewable energy actually set at the target time, which is set as the renewable energy price actual measurement 8160, a predicted price per unit amount (e.g., 1 kW) of power at the target time in a predetermined power system (e.g., a commercial power system), which is set as the system price prediction 8170, and a price per unit amount (e.g., 1 kW) of power actually set in the above power system at the target time, which is set as the system price actual measurement 8180, and is composed of one or more records having each data item.

[0038] Note that each actual measured value and actual performance value in the DC power prediction table 8100 may be input by the user or automatically acquired from a predetermined database.

[0039] (Time slot table) FIG. 4 is a diagram showing an example of a time slot table 8200. The time slot table 8200 includes a time slot ID 8210 for setting identification information of a time slot, a time 8220 for setting the start time of the time slot, a power consumption prediction 8230 for setting a predicted value of the power consumption of the data center 1000 in the time slot (the power consumption related to all facilities or devices of the data center, including the server device 3000, the storage device 4000, and air conditioning equipment (not shown)), a power consumption actual measurement 8240 for setting an actually measured value of the power consumption of the data center 1000 in the time slot, a batch power consumption prediction 8250 for setting a predicted value of the power consumption of the batch workload in the time slot (hereinafter also referred to as batch power consumption), a batch power consumption actual measurement 8260 for setting an actually measured value of the power consumption of the batch workload in the time slot, a power consumption target value 8270 for setting a target value of the power consumption of the data center 1000 in the time slot, a parameter α 8280 for setting a parameter α in the time slot, and a risk tolerance 8290 for setting a risk tolerance in the time slot, and is composed of one or more records having each data item.

[0040] (Delay Bound Time Prediction Distribution Table) FIG. 5 is a diagram showing an example of a delay bound time prediction distribution table 8300. The delay bound time prediction distribution table 8300 includes a time slot ID 8310 for setting identification information of a future time slot, a delay bound time 8320 for setting a delay bound time of the workload in the time slot, and a number 8330 for setting a predicted value of the number of workloads having the delay bound time in the time slot, and is composed of one or more records having each data item.

[0041] (User Policy Table) FIG. 6 is a diagram showing an example of a user policy table 8400. The user policy table 8400 is composed of one or more records having each data item of a policy ID 8410 in which identification information of a user policy is set, a renewable energy target rate 8420 in which a target value (target rate) of a renewable energy rate in the user policy is set, a start date / time 8430 in which an application start date / time of the user policy is set, a target achievement date 8440 in which a deadline for achieving the user policy is set, and a control policy 8450 in which a control policy for renewable energy in the user policy is set.

[0042] In the control policy 8450, "COST" means that in addition to the utilization of renewable energy, the cost related to power utilization is also emphasized, and when the actual utilization rate of renewable energy exceeds the target rate, the utilization of renewable energy is restricted (the insufficient part uses the power of the power grid), and priority is given to reducing the cost related to power utilization. "RE" means that emphasis is placed on the utilization of renewable energy, and no particular restriction is imposed even if the actual utilization rate of renewable energy exceeds the target rate (using renewable energy to the maximum extent). Note that the content of the control policy 8450 shown here is an example, and it is also possible to set information on other policies from the viewpoint of the balance between cost and the utilization of renewable energy.

[0043] In this embodiment, the data in the user policy table 8400 is assumed to be input by the user in advance, but it may be automatically set or changed.

[0044] (Workload Table) FIG. 7 is a diagram showing an example of a workload table 8500. The workload table 8500 includes a workload ID 8510 in which identification information of a workload is set, a power consumption prediction 8520 in which a predicted value of power consumption in the workload is set, a power consumption actual measurement 8530 in which an actually measured value of power consumption in the workload is set, an input time 8540 in which the time (input time) when information on the workload was first set in the management computer 2000 as an execution schedule is set, an execution schedule 8550 in which the execution timing of the workload is set, a changed execution schedule 8560 in which the execution timing changed (delayed) by the IT workload control program 8900 for the workload is set, a delay limit time 8570 in which a delay limit time for the workload is set by the user, and a queue flag 8580 in which information indicating whether or not a queue flag is set for the workload is set, and is composed of one or more records having each data item.

[0045] Note that when the workload is an interactive workload, the value of the execution schedule 8550 is automatically set to be the same as the value of the input time 8540. Also, when it is determined that the execution timing indicated by the execution schedule 8550 is delayed, the queue flag 8580 is automatically set to "Y". The usage method of the queue flag will be described later.

[0046] (Workload Power Consumption Prediction Distribution Table) FIG. 8 is a diagram showing an example of a workload power consumption prediction distribution table 8600. The workload power consumption prediction distribution table 8600 includes a workload ID 8610 in which identification information of a workload is set, a power consumption 8620 in which a range of predicted values of power consumption of the workload is set, and a probability 8630 in which the probability that the predicted value of the power consumption is realized is set, and is composed of one or more records having each data item. The workload power consumption prediction distribution table 8600 is generated at any time (the distribution of predicted values of power consumption is statistically calculated) and updated when the actually measured values of past power consumption of each workload are acquired.

[0047] (Predicted Workload Table) FIG. 9 is a diagram showing an example of a predicted workload table 8650. The workload table 8650 includes a predicted workload ID 8655 in which identification information of the workload of a predicted future time slot is set, a predicted time 8660 representing the time when the prediction of the workload is made, a time slot 8665 indicating the time slot in which the workload is predicted, a power consumption prediction 8670 in which a predicted value of the power consumption in the predicted workload is set, a delay limit time prediction 8675 in which a predicted value of the delay limit time in the predicted workload is set, and a queue flag 8680 in which information indicating whether or not a queue flag is set for the predicted workload is set, and is composed of one or more records having each data item.

[0048] Each program described above is executed by the processing device 11000 reading (the program stored in the main storage device 12000 or the storage device 8000). Each program can be recorded on a recording medium and distributed, for example. Note that all or part of the management computer 2000 may be realized using virtual information processing resources provided using virtualization technology, process space separation technology, or the like, such as a virtual server provided by a cloud system. Also, all or part of the functions provided by the management computer 2000 may be realized by a service provided by a cloud system via an API (Application Programming Interface) or the like. Next, the processing executed by the management computer 2000 will be described.

[0049] <Workload Control Process> FIG. 10 is a flowchart for explaining the outline of a workload control process, which is a process for controlling each workload in the data center 1000. The workload control process is repeatedly executed at a predetermined time (for example, every hour), a predetermined time interval (for example, a predetermined number of minutes before the start of each time slot), or a predetermined timing (a time specified by the user).

[0050] First, the management computer 2000 executes a data update process S1 for predicting the power generation amount and price of the power (power generated by renewable energy or energy from the power grid) used for the operation of the data center 1000, the power consumption in the data center 1000, and the distribution of the delay limit times of each workload in the data center 1000, and accumulating these past data. The details of the data update process S1 will be described later.

[0051] Based on the data predicted and accumulated in the data update process S1, the management computer 2000 executes a workload deployment process S2 for deploying (inputting) the workloads to be actually executed among the workloads scheduled to be executed in the most recent time slot among each workload in the data center 1000. The details of the workload deployment process S2 will be described later. The above processes are repeatedly executed. Next, the details of the data update process S1 will be described.

[0052] <Data Update Process> FIG. 11 is a flowchart for explaining the details of the data update process S1. The power consumption price prediction program 9000 predicts the power generation amount of renewable energy and the price per unit power in each time slot after the current time (S10). Specifically, for example, the power consumption price prediction program 9000 acquires the values of the time 8120, the measured renewable energy power generation amount 8140, and the measured renewable energy price 8160 of each record in the DC power prediction table 8100, and based on a predetermined algorithm (for example, performing time series analysis, performing machine learning to create a prediction model) for the acquired values, predicts the power generation amount of renewable energy and the price per unit power in each time slot after the current time. The power consumption price prediction program 9000 stores the predicted power generation amounts and prices in the predicted renewable energy power generation amount 8130 and the predicted renewable energy price 8150 of the records of each time slot in the DC power prediction table 8100, respectively.

[0053] Note that the power consumption price prediction program 9000 acquires the power generation amount of renewable energy and the price per unit power in past time slots from a predetermined device (for example, an external database or server), and stores the acquired power generation amount and price as actual values in the measured renewable energy power generation amount 8140 and the measured renewable energy price 8160 of the record related to the time slot in the DC power prediction table 8100 (S10).

[0054] Furthermore, the power consumption price prediction program 9000 predicts the price per unit power of the power in the power grid in each time slot after the current time (S11). Specifically, for example, the power consumption price prediction program 9000 acquires the values of the time 8120 and the measured grid price 8180 of each record in the DC power prediction table 8100, and based on a predetermined algorithm (for example, performing time series analysis, performing machine learning to create a prediction model) for the acquired values, predicts the price per unit power of the power in the power grid in each time slot after the current time. The power consumption price prediction program 9000 stores the predicted prices in the predicted grid price 8170 of the records of each time slot in the DC power prediction table 8100.

[0055] Note that the power consumption price prediction program 9000 acquires the price per unit power of the power system in past time slots from a predetermined device (for example, an external database or server), and stores the acquired price as an actual value in the grid price actual measurement 8180 of the record related to the time slot in the DC power prediction table 8100 (S11).

[0056] Furthermore, the power consumption price prediction program 9000 predicts the power consumption of the entire data center 1000 and the power consumption of the batch workload in each time slot after the current time (S12). Specifically, for example, the power consumption price prediction program 9000 acquires the time 8220, the measured power consumption 8240, and the measured batch power consumption 8260 of each record in the time slot table 8200, and based on a predetermined algorithm (for example, performing time series analysis, performing machine learning to create a prediction model) for each acquired value, predicts the power consumption of the entire data center 1000 and the power consumption of the batch workload in each time slot after the current time. The power consumption price prediction program 9000 stores the predicted power consumption values in the power consumption prediction 8230 and the batch power consumption prediction 8250 of the records of each time slot in the time slot table 8200.

[0057] Note that the power consumption price prediction program 9000 acquires the power consumption of the entire data center 1000 and the power consumption of the batch workload in past time slots from a predetermined device (for example, an external database or server), and stores each acquired power consumption value as an actual value in the measured power consumption 8240 and the measured batch power consumption 8260 of the record related to the time slot in the DC power prediction table 8100 (S12).

[0058] Furthermore, the power consumption price prediction program 9000 predicts the latency limit time of the batch workload of the data center 1000 in each time slot after the current time (S13). Specifically, for example, the power consumption price prediction program 9000 acquires the execution schedule 8550 (the time when the past workload was actually executed) or the modified execution schedule 8560 of the workload table 8500, and the latency limit time 8570, and based on a predetermined algorithm (for example, performing time series analysis, performing machine learning to create a prediction model) for each acquired value, predicts the distribution of the latency limit time of each batch workload in each time slot after the current time. The power consumption price prediction program 9000 stores the data of the predicted latency limit time distribution in the latency limit time 8320 and the count 8330 of the records of each time slot in the latency limit time prediction distribution table 8300 respectively. Also, the power consumption price prediction program 9000 creates new records in the predicted workload table 8650 for the number of counts 8330 predicted in each time slot, and stores the data of the time slot and the latency limit time in the time slot 8665 and the latency limit time prediction 8670 of the predicted workload table 8650 respectively. After that, the processes after S10 are repeated.

[0059] Next, FIG. 12 is a flowchart for explaining the workload deployment process S2. The parameter determination program 8700 executes a parameter determination process S20 for determining the pattern of the parameter α in each time slot after the most recent time slot.

[0060] Also, the risk tolerance calculation program 8800 executes a risk tolerance calculation process S21 for calculating the risk tolerance in each time slot after the most recent time slot.

[0061] Then, the IT workload control program 8900 determines the batch workload to be executed in the most recent time slot based on the target value of the power consumption amount calculated based on the pattern of the parameter α determined in the parameter determination process S20 and the risk tolerance calculated in the risk tolerance calculation process S21, and executes an IT workload control process S22 that deploys the determined batch workload together with the interactive workload. Hereinafter, the details of the parameter determination process S20, the risk tolerance calculation process S21, and the IT workload control process S22 will be described.

[0062] <Parameter Determination Process> FIG. 13 is a flowchart for explaining an example of the parameter determination process S20. In this parameter determination process, in order to realize the operation policy specified by the user in the user policy table 8400, the deployment amount of the ideal batch workload in each future time slot is determined. The deployment amount of the batch workload is determined by specifying a parameter α that determines the execution ratio of the batch workload in each time slot. Since the deployment amount of the batch workload is determined by specifying the parameter α, the power consumption amount in each time slot can be calculated, and thereby the utilization rate of the renewable energy (renewable energy rate per unit time) and the cost related to the utilization of the renewable energy can be calculated. Therefore, by calculating them for the possible parameter α, the optimal parameter α for realizing the operation policy specified by the user is determined.

[0063] First, the parameter determination program 8700 executes a parameter pair creation process S1000 and executes a parameter creation process S1000 for creating one or more lists (patterns) of the parameter α for each time slot after the most recent one. The details of the parameter creation process S1000 will be described later.

[0064] The parameter determination program 8700 acquires one pattern from the patterns of the parameter α determined in the parameter creation process S1000 (S1010).

[0065] The parameter determination program 8700 calculates the utilization rate of renewable energy (renewable energy rate per unit time f_re) and the cost f_cost related to the use of renewable energy in all time slots in which the parameter α is calculated (S1020). In the present embodiment, the cost f_cost related to the use of renewable energy only considers the power cost of renewable energy, but this may include grid power cost and other costs.

[0066] That is, the parameter determination program 8700 adds the value obtained by dividing the predicted power consumption 8230 of the time slot table 8200 by the batch predicted power consumption 8250, the value of the batch predicted power consumption 8250, and the value obtained by multiplying the parameter α to calculate the target power consumption value. By referring to the predicted renewable energy generation amount 8230 (renewable energy available for supply to the data center 1000) in the DC power prediction table 8100, it is determined whether the power consumption of the target power consumption value can be covered by renewable energy. If it cannot be covered, it is specified that the difference will be covered by the power grid. Thereby, the parameter determination program 8700 can calculate the utilization rate of renewable energy (renewable energy rate per unit time f_re). Further, the parameter determination program 8700 can calculate the cost f_cost related to the use of renewable energy by multiplying the predicted value of the power consumption of renewable energy by the predicted renewable energy price 8150 in the DC power prediction table 8100.

[0067] Next, the parameter determination program 8700 checks whether the control policy is "COST" (S1030). For example, the parameter determination program 8700 refers to the user policy table 8400 and checks whether the value of the control policy 8450 in the latest record is "COST".

[0068] When the control policy is "COST" (S1030: YES), the parameter determination program 8700 executes the process of S1070. When the control policy is "RE" (S1030: NO), the parameter determination program 8700 executes the process of S1040.

[0069] In S1040, the parameter determination program 8700 sets the time unit renewable energy rate f_re as the first objective function. In this case, since the user emphasizes the utilization of renewable energy, the parameter α that maximizes the time unit renewable energy rate f_re is determined.

[0070] The parameter determination program 8700 checks whether the first objective function has been set for all patterns of the parameter α (S1050). When the first objective function has been set for all patterns of the parameter α (S1050: YES), the parameter determination program 8700 executes the process of S1060. When there is a pattern of the parameter α for which the first objective function has not been set (S1050: NO), the parameter determination program 8700 repeats the processes after S1010 to obtain that pattern of the parameter α.

[0071] In S1060, the parameter determination program 8700 identifies the pattern of the parameter α with the maximum value of the first objective function among the multiple patterns of the parameter α created in the parameter creation process S1000, and sets the identified result in the parameter α8280 of the record corresponding to each time slot in the time slot table 8200. Thus, the parameter determination process S20 ends.

[0072] On the one hand, in S1070, the parameter determination program 8700 sets the cost f_cost as the first objective function. In this case, since the user attaches importance to the cost related to power utilization in addition to the utilization of renewable energy, when the actual utilization rate of renewable energy exceeds the target rate, the parameter α is determined so as to minimize the cost f_cost within the range where the renewable energy rate per unit time does not fall below the target rate. However, when the actual utilization rate of renewable energy is below the target rate, priority is given to achieving a utilization rate of renewable energy equal to or higher than the target rate, and the parameter α is determined so as to maximize the renewable energy rate per unit time f_re.

[0073] Also, the parameter determination program 8700 sets a constraint condition in the first objective function that the renewable energy rate per unit time f_re is equal to or higher than the renewable energy target rate (meeting the minimum condition of the renewable energy rate) (S1080). Note that the parameter determination program 8700 uses the value of the renewable energy target rate 8420 in the latest record of the user policy table 8400 as the renewable energy target rate.

[0074] The parameter determination program 8700 checks whether the first objective function has been executed for all patterns of the parameter α (S1080). If the first objective function has been executed for all patterns of the parameter α (S1080: YES), the parameter determination program 8700 executes the process of S1090. If there is a pattern of the parameter α for which the first objective function has not been set (S1080: NO), the parameter determination program 8700 repeats the processes after S1010 to obtain that pattern of the parameter α.

[0075] In S1100, the parameter determination program 8700 checks whether there is a pattern of parameter α that satisfies the constraint conditions. If there is a pattern of parameter α that satisfies the constraint conditions (S1100: YES), the parameter determination program 8700 executes the process of S1110. If there is no pattern of parameter α that satisfies the constraint conditions (S1100: NO), the parameter determination program 8700 executes the process of S1120.

[0076] In S1110, the parameter determination program 8700 identifies the pattern of parameter α when the value of the first objective function is the minimum among the patterns of the plurality of parameters α created in the parameter creation process S1010, and sets the identification result to the parameter α8280 of the record related to each time slot in the time slot table 8200. Thus, the parameter determination process S20 ends.

[0077] In S1120, the parameter determination program 8700 sets the time unit renewable energy rate f_re as the second objective function.

[0078] Then, the parameter determination program 8700 identifies the pattern of parameter α when the value of the second objective function is the maximum among the patterns of the plurality of parameters α created in the parameter creation process S1010, and sets the identification result to the parameter α8280 of the record related to each time slot in the time slot table 8200 (S1130). Thus, the parameter determination process S20 ends.

[0079] <Parameter Creation Process> Figures 14 and 15 are flowcharts for explaining the details of the parameter creation process S1000 (divided into two figures for the sake of layout). In the parameter creation process S1000, all possible combinations of parameters α are created. Since the batch workload has a delay limit time 8570 set by the user, it is not possible to delay the execution indefinitely. Therefore, the deployment amount of the batch workload cannot be completely freely determined in each time slot. Accordingly, restrictions are also necessary for the parameter α in each time slot. Restrictions are imposed on the parameter α based on the information of the predicted distribution of the delay limit time in the delay limit time prediction distribution table 8300, and combinations of parameters α that can be taken within that range are created. As shown in Figure 14, the parameter determination program 8700 selects the most recent time slot (S2000). Specifically, the parameter determination program 8700 refers to the time slot table 8200 and selects the record indicating the time 8220 that is the closest future time to the current time.

[0080] The parameter determination program 8700 determines whether the selected time slot is the last time slot (S2010). Specifically, the parameter determination program 8700 checks whether the selected time slot is the last time slot (for example, the time slot 12 hours later) with a preset timing.

[0081] If the selected time slot is the last time slot (S2010: YES), the parameter determination program 8700 executes the process of S2060. If the selected time slot is not the last time slot (S2010: NO), the parameter determination program 8700 executes the process of S2020.

[0082] In S2060, the parameter determination program 8700 decides to deploy all the batch workload because this is the last time slot, sets the parameter α of the selected time slot (the last time slot) to 1, and the parameter creation process S1000 ends.

[0083] In S2020, the parameter determination program 8700 determines whether the selected time slot is the first time slot. Specifically, the parameter determination program 8700 checks whether the time 8220 of the record selected in S2000 indicates the time closest to the current time in the future.

[0084] If the selected time slot is the first time slot (S2020: YES), the parameter determination program 8700 executes the process of S2030. If the selected time slot is not the first time slot (S2020: NO), the parameter determination program 8700 executes the process of S2070.

[0085] From S2030 to S2050 are the processes when the selected time slot is the first time slot. In the first time slot, since a batch workload has already been set for that time slot, the delay limit time is obtained based on the information of those batch workloads, and the power consumption is predicted. On the other hand, from S2070 to S2110 are the processes when the selected time slot is not the first time slot. If it is not the first time slot, since no batch workload is set for those time slots, those batch workloads have not yet been registered in the workload table 8500. Therefore, prediction of those batch workloads is required, and the acquisition of the predicted delay limit time and the prediction of power consumption are performed.

[0086] In S2030, the parameter determination program 8700 acquires the batch workload set in the selected time slot and all the batch workloads currently accumulated as a queue in the time slot immediately preceding the selected time slot. Specifically, the parameter determination program 8700 refers to the workload table 8500 and acquires the data of the record related to the selected time slot and the data of all records with the queue flag 8580 being "Y".

[0087] The parameter determination program 8700 sorts each batch workload acquired in S2030 in ascending order of the delay limit time (in the order of earliest) (S2040). Specifically, the parameter determination program 8700 refers to the workload table 8500 and sorts each record in ascending order of the delay limit time 8570 of each record acquired in S2030 such that the delay limit time is closer to the current time.

[0088] The parameter determination program 8700 calculates the total workload power consumption prediction value PB, which is the total value of the predicted power consumption Pb of each batch workload sorted in S2040 (S2050). Specifically, the parameter determination program 8700 refers to the workload table 8500 and sums up the values of the power consumption prediction 8520 of each record related to the workload sorted in S2040. After that, the process of S2110 is performed.

[0089] On the other hand, in S2070, the parameter determination program 8700 acquires the predicted values of the number of batch workloads to be deployed (executed) in the currently selected time slot and the delay limit time of the batch workload, respectively. Specifically, the parameter determination program 8700 refers to the delay limit time prediction distribution table 8300 and acquires the values of the delay limit time 8320 and the number 8330 of the record related to the currently selected time slot.

[0090] The parameter determination program 8700 calculates the predicted power consumption value Pb per batch workload in the currently selected time slot by dividing the predicted value of the batch power consumption in the currently selected time slot, which is calculated in S2070, by the number of batch workloads (S2080).

[0091] Specifically, the parameter determination program 8700 refers to the time slot table 8200, acquires the batch power consumption prediction 8250 of the record related to the currently selected time slot, and divides the acquired power consumption value by the value of the number 8330 acquired in S2070.

[0092] The parameter determination program 8700 stores the calculated predicted value of the batch power consumption in the prediction workload table 8650 (S2085).

[0093] Specifically, the parameter determination program 8700 stores the predicted value Pb calculated in S2080 in the power consumption prediction 8670 of the record having the time slot 8665 that matches the selected time slot in the record having the latest prediction time 8660.

[0094] The parameter determination program 8700 acquires the batch workload for which the predicted value Pb of the power consumption was obtained in S2070 and the batch workload currently accumulated as a queue among the batch workloads of the immediately preceding time slot. Then, the parameter determination program 8700 rearranges the acquired batch workloads in ascending order of the delay bound time (S2090).

[0095] Specifically, for example, the parameter determination program 8700 refers to the workload table 8500 and acquires the data of the record whose queue flag 8580 is "Y". Further, the parameter determination program 8700 refers to the prediction workload table 8650 and acquires the data of the record whose prediction time 8660 is the latest and whose queue flag 8675 is "Y", and the data of the record whose prediction time 8660 is the latest and whose time slot 8665 is the same as the selected time slot. The parameter determination program 8700 targets the delay bound time prediction 8675 of the prediction workload table 8650 and the delay bound time 8570 of the record of the acquired workload table 8500 for rearrangement.

[0096] The parameter determination program 8700 calculates the total predicted workload power consumption value PB, which is the total value of the predicted power consumption Pb of each batch workload rearranged in S2090 (S2100). Specifically, the parameter determination program 8700 sums the predicted values Pb indicated by the power consumption prediction 8520 or the power consumption prediction 8670 of each record where the queue flag 8580 is "Y", the prediction time 8660 is the latest and the queue flag 8675 is "Y", or the prediction time 8660 is the latest and the time slot is the same as the selected time slot 8665 in S2090. After that, the process of S2110 is performed.

[0097] In S2110, the parameter determination program 8700 identifies all the workloads that cannot be set (delayed) in the time slots after the selected time slot among the workloads for which the predicted power consumption value Pb was calculated in S2050 or S2100, and calculates the total value of the predicted power consumption Pb of the identified workloads (the total predicted power consumption value PB' of non-delayable workloads). Specifically, the parameter determination program 8700 refers to the workload table 8500 and the predicted workload table 8650, identifies the workloads of the records where the time of the delay limit time 8570 or the predicted delay limit time 8675 is the same as the time of the selected time slot, and sets the total value of the predicted power consumption Pb of the identified workloads as the total predicted power consumption value PB' of non-delayable workloads. The power consumption by the batch workload corresponding to PB' will definitely be consumed in that time slot.

[0098] Then, as shown in FIG. 15, the parameter determination program 8700 sets PB' / PB as the lower limit value α_min of the parameter α in the selected time slot (S2120).

[0099] In this way, the parameter determination program 8700 prioritizes the execution of batch workloads with earlier delay limit times at earlier timings.

[0100] Then, the parameter determination program 8700 arbitrarily determines one or more values of α for the currently selected time slot that are equal to or greater than the lower limit value α_min (S2130).

[0101] The parameter determination program 8700 sets the power consumption determination value P to 0 (S2140).

[0102] The parameter determination program 8700 adds the predicted power consumption value Pb of each workload to the determination value P in the order of the workloads rearranged in S2040 or S2090 (S2150, S2160). The parameter determination program 8700 repeats this addition until the determination value P exceeds the multiplication value of PB calculated in S2050 or S2100 and α set in S2130 (S2170: NO).

[0103] When the power consumption value P becomes equal to or greater than the multiplication value of PB and α (S2170: YES), the parameter determination program 8700 accumulates the workloads that were not the targets of the multiplication in the queue (S2180). Specifically, the parameter determination program 8700 refers to the workload table 8500 and the predicted workload table 8650, and sets the queue flag 8580 or the queue flag 8680 of the record related to the workload that was not the target of the multiplication to "Y".

[0104] The parameter determination program 8700 selects the next time slot of the currently selected time slot and repeats the processing after S2010 (S2190).

[0105] Note that in S2130, the parameter determination program 8700 sets a plurality of values (for example, if the lower limit value α_min is 0.1, 0.1, 0.2, 0.3, 0.4,..., 1) as the value of α for the currently selected time slot, and performs the processing after S2140 for each of them to create a plurality of patterns of the parameter α.

[0106] <Risk tolerance calculation process> FIG. 16 is a flowchart for explaining an example of the risk tolerance calculation process S21. Based on the parameter α of each time slot determined in the parameter determination process S20, the risk tolerance calculation program 8800 calculates the target power consumption value of each time slot, and stores the calculated target power consumption values in the target power consumption value 8270 of the time slot table 8200 (S3000).

[0107] For example, the risk tolerance calculation program 8800 refers to the time slot table 8200, and adds the value obtained by dividing the predicted power consumption 8230 of each time slot record by the batch predicted power consumption 8250, and the value obtained by multiplying the value of the batch power consumption 8250 and the parameter α.

[0108] The risk tolerance calculation program 8800 selects one of the time slots for which the parameter α was calculated in the parameter determination process S20 (S3010).

[0109] The risk tolerance calculation program 8800 acquires the parameter α of the selected time slot (S3020).

[0110] The risk tolerance calculation program 8800 checks whether the acquired parameter α is 1 (S3030).

[0111] If the acquired parameter α is 1 (S3030: YES), the risk tolerance calculation program 8800 executes the process of S3040. If the acquired parameter α is not 1 (S3030: NO), the risk tolerance calculation program 8800 executes the process of S3070.

[0112] In S3070, the risk tolerance calculation program 8800 sets the risk tolerance related to the selected time slot to the minimum value (1 in this embodiment), and stores this in the time slot table 8200 (specifically, the risk tolerance 8290 of the record related to the selected time slot in the time slot table 8200). After that, the process of S3060 is performed.

[0113] In S3040, the risk tolerance calculation program 8800 executes a per - perspective risk tolerance calculation process S3040 for calculating the risk tolerance from the renewable energy perspective and the risk tolerance from the cost perspective. The details of the per - perspective risk tolerance calculation process S3040 will be described later.

[0114] Then, based on the risk tolerance from the renewable energy perspective and the risk tolerance from the cost perspective calculated in S3040, the risk tolerance calculation program 8800 calculates the risk tolerance for the currently selected time slot (S3050).

[0115] For example, the risk tolerance calculation program 8800 calculates the product value of the risk tolerance from the renewable energy perspective and the risk tolerance from the cost perspective, or the power value (e.g., square root) of that product value. Note that the calculation method described here is just an example, and other calculation methods may be adopted as long as the magnitudes of the values of the risk tolerance from the renewable energy perspective and the risk tolerance from the cost perspective are reflected in the risk tolerance for the currently selected time slot.

[0116] Also, the risk tolerance calculation program 8800 may reflect the risk tolerance in the currently selected time slot of the control policy. For example, the risk tolerance calculation program 8800 obtains the control policy 8450 of the latest record in the user policy table 8400. When the control policy is "RE", the value of the risk tolerance from the renewable energy perspective or the value obtained by multiplying the risk tolerance from the renewable energy perspective by a predetermined coefficient may be used as the risk tolerance for the currently selected time slot.

[0117] The risk tolerance calculation program 8800 determines whether the risk tolerance has been calculated for all time slots in which the parameter α has been calculated by the parameter determination process S20 (S3060).

[0118] When the risk tolerance has been calculated for all time slots (S3060: YES), the risk tolerance calculation process ends. If there are time slots for which the risk tolerance has not been calculated (S3060: NO), the risk tolerance calculation program 8800 repeats the processes after S301 to select a time slot for which the risk tolerance has not been calculated.

[0119] <Risk Tolerance Calculation Process for Each Perspective> FIG. 17 is a flowchart for explaining the details of the risk tolerance calculation process S3040 for each perspective. The risk tolerance calculation program 8800 calculates a renewable energy perspective risk tolerance (tolerance for the risk that the utilization of renewable energy is not sufficient because the renewable energy generation rate does not reach the target rate) so that the value becomes smaller as the generated power of the renewable energy in the selected time slot exceeds the target value of the power consumption (S4000).

[0120] For example, the risk tolerance calculation program 8800 calculates the renewable energy perspective risk tolerance by (a predetermined negative coefficient) × (target value of power consumption - generated power of renewable energy). Note that the formula shown here is just an example, and other formulas representing a monotonic decrease may be used.

[0121] The risk tolerance calculation program 8800 checks whether the price per unit power of the renewable energy in the selected time slot is greater than the price per unit power of the grid (S4010). Specifically, the risk tolerance calculation program 8800 refers to the DC power prediction table 8100 and checks by specifying the values of the renewable energy price prediction 8150 and the grid price prediction 8170 in the record related to the selected time slot.

[0122] When the price per unit power of renewable energy is greater than the price per unit power of the grid (S4010: YES), the risk tolerance calculation program 8800 executes the process of S4030. When the price per unit power of renewable energy is less than or equal to the price per unit power of the grid (S4010: NO), the risk tolerance calculation program 8800 executes the process of S4020.

[0123] In S4030, the risk tolerance calculation program 8800 calculates the cost perspective risk tolerance (tolerance for the risk of cost increase due to excessive use of renewable energy) such that the value increases as the power generation amount of renewable energy in the selected time slot exceeds the target value of power consumption. Thus, the risk tolerance calculation process S3040 for each perspective ends.

[0124] For example, the risk tolerance calculation program 8800 calculates the renewable energy perspective risk tolerance by (a coefficient of a predetermined positive value) × (target value of power consumption - power generation amount of renewable energy). Note that the formula shown here is just an example, and other formulas representing monotonic increase may also be used.

[0125] In S4020, the risk tolerance calculation program 8800 calculates the cost perspective risk tolerance such that the value decreases as the power generation amount of renewable energy in the selected time slot exceeds the target value of power consumption. Thus, the risk tolerance calculation process S3040 for each perspective ends.

[0126] For example, the risk tolerance calculation program 8800 calculates the renewable energy perspective risk tolerance by (a coefficient of a predetermined negative value) × (target value of power consumption - power generation amount of renewable energy). Note that the formula shown here is just an example, and other formulas representing monotonic decrease may also be used.

[0127] <IT workload control process> Figures 18 and 19 are flowcharts for explaining the IT workload control process S22 (divided into two figures for the sake of the layout). In this IT workload control process, the batch workload to be actually deployed is determined so as to approach the target power consumption value in each time slot determined by the value of the calculated parameter α. At this time, in addition to approaching the target power consumption value, considering the deviation from the predicted power consumption of each batch workload, in the time slots with a small risk tolerance, the batch workload to be deployed is determined so that the sum of the deviations from the predicted power consumption of each batch workload is minimized as much as possible.

[0128] As shown in Figure 18, the IT workload control program 8900 calculates a weight value (first weight value) related to the risk tolerance in the most recent time slot (S5000).

[0129] For example, the IT workload control program 8900 sets the reciprocal of the risk tolerance in the most recent time slot as the first weight value. Note that the method for calculating the first weight value shown here is just an example, and the first weight value can be a monotonically decreasing function with respect to the risk tolerance.

[0130] The IT workload control program 8900 calculates a weight value (second weight value) related to the risk tolerance in the time slots after the most recent time slot (S5010).

[0131] For example, the IT workload control program 8900 sets the reciprocal of the average value of the risk tolerances in the time slots after the most recent time slot as the second weight value. Note that the method for calculating the second weight value shown here is just an example, and the second weight value can be a monotonically decreasing function with respect to the risk tolerance.

[0132] The IT workload control program 8900 sets the predicted value of the power consumption other than the batch workload in the most recent time slot to the variable Po (S5020). Specifically, the IT workload control program 8900 refers to the time slot table 8200 and sets Po to the value obtained by subtracting the value of the batch power consumption prediction 8250 from the value of the power consumption prediction 8230 of the record related to the most recent time slot.

[0133] The IT workload control program 8900 acquires the batch workload set in the most recent time slot and all the batch workloads currently stored as a queue in the time slot immediately preceding the most recent time slot (S5030). Specifically, the IT workload control program 8900 refers to the execution schedule 8550 of each record in the workload table 8500 and acquires the record related to the most recent time slot and the record with the queue flag 8580 being "Y".

[0134] The IT workload control program 8900 sorts each batch workload acquired in S5030 in ascending order of the delay limit time (earliest order) (S5040). Specifically, the IT workload control program 8900 stores the records acquired in S5030 in the order in which the delay limit time 8570 of the record related to that time slot is closest to the current time.

[0135] Then, the parameter determination program 8700 adds the predicted values of the power consumption of each sorted batch workload to Po in that sorted order (S5040). Specifically, the parameter determination program 8700 adds the values of the power consumption prediction 8520 of each record sorted in S5030 to Po in order (batch workloads with the same delay limit time are added to the predicted value of the power consumption at once).

[0136] When Po exceeds the power consumption target value in the addition process of S5040, the parameter determination program 8700 identifies the time slot ts to which the batch workload obtained by adding the predicted value of power consumption belongs (S5050). As can be understood from the above, there may be a plurality of batch workloads belonging to this time slot ts.

[0137] The parameter determination program 8700 creates one or more sets of batch workloads obtained by dividing the batch workloads belonging to the time slot ts into two groups (for example, when the number of batch workloads belonging to the time slot ts is 3, four sets of "0 and 1", "1 and 2", "2 and 1", and "1 and 0" are created) (S5060).

[0138] The parameter determination program 8700 resets Po to the value in S5020 (S5070).

[0139] Subsequently, as shown in FIG. 19, the parameter determination program 8700 selects one of the plurality of sets created in S5060 (S5080).

[0140] The parameter determination program 8700 combines the batch workloads of one group in the set selected in S5080 with the batch workloads whose delay limit time is shorter (earlier) than the time slot ts and stores them as the first group, and combines the batch workloads of the other group in the set selected in S5080 with the batch workloads whose delay limit time is longer (later) than or equal to the time slot ts and stores them as the second group (S5090).

[0141] The parameter determination program 8700 determines whether the sum of the power consumption Po other than the batch workloads and the power consumption of the batch workloads in the first group approximates the power consumption target value (S5100).

[0142] For example, the parameter determination program 8700 determines whether the sum of Po set in S5070 and the power consumption of the batch workload related to the first group (obtained from the power consumption prediction 8520 of the workload table 8500) is equal to or greater than the power consumption target value and less than or equal to (the power consumption target value + a predetermined error tolerance n%).

[0143] When the sum is approximate to the power consumption target value (S5100: YES), the parameter determination program 8700 executes the process of S5110. When the sum is not approximate to the power consumption target value (S5100: NO), the parameter determination program 8700 repeats the processes after S5080 to select another set.

[0144] In S5110, the parameter determination program 8700 calculates the value of the evaluation function g representing the severity of the deviation from the predicted value of the power consumption of the batch workloads of the first group and the second group with respect to the power consumption of the batch workloads.

[0145] For example, the parameter determination program 8700 calculates the value of the evaluation function g for the group of batch workloads obtained in S5080 by the following formula.

[0146] Evaluation function g = (first weight value) × (total sum of deviations of the first group) + (second weight value) × (total sum of deviations of the second group)

[0147] As a method for calculating the total sum of deviations of the first group, the parameter determination program 8700 calculates the absolute value of the difference between the predicted value of the power consumption (obtained from the power consumption prediction 8520 of the workload table 8500) of each batch workload belonging to the first group and the past average predicted value or median value of the power consumption (calculated from the power consumption 8620 and probability 8630 of the workload power consumption prediction distribution table 8600), and obtains the total by summing up the calculated absolute values. The same applies to the total sum of deviations of the second group.

[0148] Note that the evaluation function shown here is just an example, and any function that takes into account the tolerance of the deviation between the predicted value and the actual value is acceptable.

[0149] The parameter determination program 8700 checks whether the value of the evaluation function g has been calculated for all the sets of workloads created in S5060 (S5120). If the value of the evaluation function g has been calculated for all sets of workloads (S5120: YES), the parameter determination program 8700 executes the process of S5130. If there is a set of workloads for which the value of the evaluation function g has not been calculated (S5120: NO), the parameter determination program 8700 repeats the processes after S5080 to obtain that set of workloads.

[0150] In S5130, the parameter determination program 8700 compares the values of the evaluation function g for each set of workloads, searches for the set with the minimum value of the evaluation function g, and identifies the first group and the second group associated with this set.

[0151] Then, the parameter determination program 8700 deploys the batch workloads of the first group identified in S5130 to be executed in the nearest time slot (S5140). For example, the parameter determination program 8700 refers to the workload table 8500, sets the time of the nearest time slot in the post-change execution schedule 8560 of the records related to each batch workload of the first group, and sets "N" in the queue flag 8580 respectively.

[0152] Also, the parameter determination program 8700 sets the batch workloads of the second group identified in S5130 to wait in the queue (not to be executed in the nearest time slot) (S5150). For example, the parameter determination program 8700 refers to the workload table 8500 and sets "Y" in the queue flag 8580 of the records related to each batch workload of the second group respectively. Thus, the IT workload control process S22 ends.

[0153] Thereafter, the server device 3000 and the storage device 4000 execute each workload (job) according to the content of the workload table 8500 (S5160).

[0154] <Workload Transfer Information Screen> FIG. 20 is a diagram showing an example of the workload transfer information screen 13000. The workload transfer information screen 13000 includes a pre-transfer information display column 13100 in which the power consumption values 13101 in each time slot are displayed when the IT workload control process S22 is not executed (when the execution timing (time slot) of the batch job is not changed), a post-transfer information display column 13200 in which the actual power consumption values 13102 in each time slot after the IT workload control process S22 is executed are displayed, an effect display column 13300 in which information indicating the effect of the execution of the IT workload control process S22 is displayed, a schedule change history display column 13400 in which information on the batch workload whose execution timing (time slot) has been changed by the IT workload control process S22 is displayed, and an approval designation column 13500 for closing the workload transfer information screen 13000.

[0155] In each of the pre-transfer information display column 13100 and the post-transfer information display column 13200, for comparison, the actual power generation values 13103 of the renewable energy in each time slot are displayed.

[0156] In the effect display column 13300, information such as the increase rate of the utilization rate of renewable energy per unit time and the decrease rate of cost in a past predetermined period, which are calculated based on the IT workload control process S22, is displayed.

[0157] In the schedule change history display column 13400, information 13401 on the batch workload whose execution time slot has been changed by the IT workload control process S22 and information 13402 on the batch workload whose execution slot has been transferred from the latest time slot to a later time slot are displayed.

[0158] Note that although past data is displayed on this workload transfer information screen 13000, information (such as power consumption) on the batch workload for the nearest time slot to be executed in the future may also be displayed.

[0159] As described above, based on the predicted values of the delay limit time and power consumption of each workload, the workload control support device of the present embodiment calculates the target value of the power consumption in a future time slot so as to satisfy the target value of the renewable energy rate and the conditions of the cost related to the use of renewable energy, and based on the calculated target value of the power consumption, determines the timing of each workload to be executed in a future time slot, and causes each workload to be executed at the determined timing.

[0160] That is, the workload control support device of the present embodiment determines the target value of the power consumption in consideration of the cost and the renewable energy rate, and based on this target value of the power consumption, determines and executes the timing of each workload to be executed in a future time slot. Thereby, it becomes possible to use renewable energy and execute workloads according to the needs of the user in consideration of the cost and the renewable energy rate.

[0161] In this way, the workload control support device of the present embodiment can control each workload executed by renewable energy while considering the cost-effectiveness.

[0162] In addition, the workload control support device of the present embodiment accepts from the user the designation of a control policy regarding which of the renewable energy rate and the cost is to be emphasized. When a control policy that emphasizes the renewable energy rate is designated, it specifies a pattern of the parameter α that optimizes the utilization rate of renewable energy, and based on the specified pattern, calculates the target value of the power consumption. On the other hand, when a policy that emphasizes the cost is designated by the user, it specifies the parameter α that optimizes the cost related to the use of renewable energy, and based on the specified pattern, calculates the target value of the power consumption.

[0163] Accordingly, an appropriate target value of power consumption can be set based on the option of emphasizing cost or emphasizing the renewable energy ratio.

[0164] In addition, when a control policy that emphasizes cost is specified, the workload control support device according to the present embodiment calculates a target value of power consumption based on a pattern of parameter α that satisfies the user's target value of the renewable energy ratio and optimizes the cost related to the use of renewable energy.

[0165] Accordingly, a target value of power consumption that emphasizes cost can be set only when the target value of the renewable energy ratio is achieved. Thereby, stable use of renewable energy can be ensured.

[0166] In addition, the workload control support device according to the present embodiment creates a pattern of parameter α that satisfies the delay limit time, and calculates a target value of power consumption based on the pattern of parameter α and the predicted value of power consumption so as to satisfy the conditions of the renewable energy ratio and cost.

[0167] Accordingly, it is possible to appropriately allocate the execution timings of the workloads that can be delayed and calculate the predicted value of power consumption.

[0168] In addition, the workload control support device according to the present embodiment calculates a risk tolerance indicating the risk due to the uncertainty of the predicted value of power consumption, and determines the timing of each workload based on the risk tolerance, the difference between the predicted value of power consumption of each workload and the past power consumption value, and the target value of power consumption.

[0169] Accordingly, it is possible to set an appropriate execution timing of the workload in consideration of the uncertainty of the predicted value. In addition, the execution timing of the workload can be determined in consideration of the risk based on the deviation between the predicted value and the actual result of power consumption.

[0170] In addition, the workload control support device according to the present embodiment calculates the risk tolerance based on the difference between the predicted value of the power generation amount related to renewable energy and the target value of the power consumption amount.

[0171] Thereby, the risk derived from the uncertainty of the power generation amount of renewable energy can be considered.

[0172] In addition, the workload control support device according to the present embodiment displays information on the timing and power consumption of each workload scheduled to be executed, or information on each executed workload and their power consumption.

[0173] Thereby, the user can confirm whether the execution timing of the workload is appropriately determined.

[0174] The present invention is not limited to the above-described embodiments, and can be implemented using any components without departing from the gist thereof. The embodiments and modifications described above are merely examples, and the present invention is not limited to these contents as long as the features of the invention are not impaired. Also, although various embodiments and modifications have been described above, the present invention is not limited to these contents. Other aspects conceivable within the scope of the technical idea of the present invention are also included in the scope of the present invention.

[0175] For example, a part of each function provided in each device according to the present embodiment may be provided in another device, or functions provided in another device may be provided in the same device.

[0176] Also, the configuration of the program described in the present embodiment is an example, and for example, a part of the program may be incorporated into another program, or a plurality of programs may be configured as one program.

[0177] Also, in the present embodiment, the delay limit time is used as the executable time, but the executable time may be specifically specified.

[0178] In addition, in this embodiment, although the case of the workload in the data center is described as the workload, it is also applicable to information processing executed in other facilities or networks.

Explanation of Signs

[0179] 1 Workload control system 1000 Data center 2000 Management computer

Claims

1. A processor and a memory are included. the memory stores a predicted value of an executable period and a predicted value of a power consumption amount for each of a plurality of power-consuming workloads scheduled to be executed in a future time period; The processor determines a timing of each workload to be executed in the future time period based on the predicted values ​​of the executable period and the power consumption amount so as to satisfy a condition of a utilization rate of renewable energy in the power consumption in the future time period and a condition of a cost related to the use of power. A workload control support device comprising:

2. the processor calculates a target value of the amount of power consumption for the future time period based on the predicted values ​​of the executable period and the amount of power consumption so as to satisfy the utilization rate condition and the cost condition, and determines a timing for each workload to be executed in the future time period based on the calculated target value of the amount of power consumption; 2. A workload control assistance device according to claim 1.

3. The processor, accepting a parameter designation indicating a policy of which of the utilization rate condition and the cost condition is to be prioritized; When a policy that emphasizes the condition of the utilization rate is specified, a pattern of execution timing of each workload that optimizes the utilization rate of renewable energy is identified, and a target value of the amount of power consumption in the future time period is calculated based on the identified pattern; when a policy that emphasizes the cost condition is specified, identifying a pattern of execution timing of each workload that optimizes the cost related to power usage, and calculating a target value of the power consumption amount for the future time period based on the identified pattern; 2. The workload control assistance device according to claim 1.

4. The processor, When a policy that emphasizes the cost condition is specified, a pattern of execution timing of each workload that satisfies the minimum condition for the utilization rate of renewable energy and optimizes the cost related to the utilization of electricity is identified, and a target value of the amount of power consumption in the future time period is calculated based on the identified pattern.

4. The workload control assistance device according to claim 3.

5. The processor, creating an execution timing condition for each workload that satisfies an executable period for each of the workloads; calculating a target value of the amount of power consumption in the future time period based on the created execution timing condition and the predicted value of the amount of power consumption so as to satisfy the utilization rate condition and the cost condition; 3. The workload control assistance device according to claim 2.

6. the processor calculates a risk tolerance indicating a risk due to uncertainty of the predicted value of the power consumption in the future time period using a predetermined algorithm, and determines a timing of each workload to be executed in the future time period based on the calculated risk tolerance, a difference between the predicted value of the power consumption of each workload in the future time period and the past power consumption value of each workload, and the calculated target value of the power consumption.

3. The workload control assistance device according to claim 2.

7. 7. The workload control support device of claim 6, wherein the processor calculates the risk tolerance based on the difference between a predicted value of power generation related to renewable energy in the future time period and the calculated target value of power consumption in the future time period.

8. 2. The workload control support device of claim 1, wherein the processor displays information on the timing and power consumption of each workload to be executed in the determined future time period, or information on each workload executed and the power consumption of each workload.

9. 2. The workload control assistance device according to claim 1, wherein the processor causes a predetermined device to execute each of the workloads at the determined timing.

10. An information processing device, Obtaining a predicted execution period and power consumption of each of a plurality of power-consuming workloads scheduled to be executed in a future time period; a workload control process for determining a timing for each workload to be executed in the future time period so as to satisfy a condition regarding a utilization rate of renewable energy in power consumption in the future time period and a condition regarding a cost related to power usage, based on the obtained predicted values ​​of the executable period and the power consumption, and executing each workload at the determined timing; The workload control support method includes:

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