JP2024165558A - Job management program, job management method, and job management device - Google Patents

Abstract

To provide a job management program, job management method and job management device capable of suppressing a charge while keeping the completion deadline time of a job.SOLUTION: A job management program which controls a hybrid system comprising an on-premise computing device operating in an on-premise environment and a cloud computing device operating in a charging cloud environment, causes the on-premise computing device to start execution of a first job when sequentially executing the first job, a second job, and a third job in this order, allocates the second job to the cloud computing device when the execution time of the second job is shorter than the execution time of the third job in a case where the execution of any of the second job and the third job does not complete until the predetermined completion deadline time when executed after the other is executed after the execution completion of the first job, and allocates processing of causing the on-premise computing device to execute the third job to a computer.SELECTED DRAWING: Figure 5

Description

The present invention relates to a job management program, a job management method, and a job management device.

In recent years, with advances in technology, the size and volume of data being handled has increased dramatically. As a result, attention has been focused on HPC (High Performance Computing) systems, which have high processing power for quickly processing data.

HPC systems are expensive, so companies own only a small number of them, and because users are not free to use them, users often use one HPC system individually. When used individually by multiple users, the HPC system executes each submitted job sequentially.

In addition, there are hybrid HPC systems that process jobs using both an on-premise environment using a company's own servers and a cloud environment using servers deployed in an external billing cloud.

When a deadline exists for a business operation that uses the results of a job, the job is required to meet the job's deadline, which is the deadline for completing execution calculated backwards from the deadline. Traditionally, batch processing job schedulers are based on the idea of first-come, first-served. Therefore, in order to shorten delivery times on fixed computer equipment, priority was given to jobs that should be processed as quickly as possible, such as those with an approaching deadline, and the preceding jobs were overtaken or stopped.

In response to this, cloud services have become widespread in recent years, and as long as the usage fee is paid, it is now possible to execute other jobs promptly without stopping the running job. In a hybrid HPC system that combines cloud services and on-premise computing facilities, it is desirable to achieve a good balance between work delivery dates and cloud service usage fees.

A management server technology has been proposed that optimizes the placement of resources for applications using the reducible cost of placement on each resource, including on-premise environments, private cloud environments, and public cloud environments. A container execution management system has also been proposed that manages tasks while referring to the operating status of both the public cloud and on-premise data centres.

JP 2021-60745 A JP 2022-144518 A

However, when deciding where to place resources, simply taking into account the possible cost savings, it can be difficult to meet work deadlines. In addition, when managing tasks by referencing the work status in cloud and on-premise environments, costs are not taken into account, making it difficult to properly place resources in actual operations.

The disclosed technology has been developed in consideration of the above, and aims to provide a job management program, a job management method, and a job management device that can reduce charges while meeting job completion deadlines.

In one aspect of the job management program, job management method, and job management device disclosed in the present application, the job management program controls a hybrid system including an on-premise computing device operating in an on-premise environment and a cloud computing device operating in a billing cloud environment. The job management program causes the on-premise computing device to start execution of the first job when the first job, the second job, and the third job are executed in sequence, and when neither the second job nor the third job is completed by the respective end deadline times that are predetermined to be executed after the other job is executed after the first job is completed, if the expected execution time of the second job is shorter than the expected execution time of the third job, the job management program causes a computer to execute a process of assigning the second job to the cloud computing device and the third job to the on-premise computing device.

In one aspect, the present invention can reduce charges while meeting job completion deadlines.

FIG. 1 is a block diagram of a hybrid HPC system according to an embodiment. FIG. 2 is a diagram illustrating an example of a job management table. FIG. 3 is a diagram illustrating an example of a job execution history table. FIG. 4 is a flow chart showing the entire job execution process. FIG. 5 is a flowchart of a resource selection and execution process performed by the job management device according to the first embodiment. FIG. 6 is a flowchart of a billing amount calculation process performed by the job management device according to the first embodiment. FIG. 7 is a diagram showing an execution state in a case where jobs are executed in the order in which they were submitted, and in a case where the job management device according to the first embodiment executes jobs. FIG. 8 is a diagram illustrating an example of a job execution status by the job management device according to the second embodiment. FIG. 9A is a first flowchart of a resource selection and execution process by the job management device according to the third embodiment. FIG. 9B is a second flowchart of the resource selection and execution process by the job management device according to the third embodiment. FIG. 10 is a diagram showing a comparison of execution results between the job management device according to the second embodiment and the job management device according to the third embodiment. FIG. 11 is a diagram illustrating a comparison of execution status between the job management device according to the second embodiment and the job management device according to the third embodiment. FIG. 12 is a diagram showing the hardware configuration of the job management device.

Below, examples of the job management program, job management method, and job management device disclosed in the present application are described in detail with reference to the drawings. Note that the job management program, job management method, and job management device disclosed in the present application are not limited to the following examples.

FIG. 1 is a block diagram of a hybrid HPC system according to an embodiment. The hybrid HPC system 1 includes a job management device 10, a user terminal device 20, an on-premise computing device 30, and a cloud computing device 40. The job management device 10 is connected to a plurality of user terminal devices 20, on-premise computing devices 30, and cloud computing devices 40 via a network.

There are multiple user terminal devices 20. For example, if the hybrid HPC system 1 is a company-owned system, the user terminal devices 20 are terminal devices owned by each department and are used by employees of each department. The user terminal devices 20 transmit jobs to be executed to the job management device 10. Jobs can be executed on either the on-premise computing device 30 or the cloud computing device 40, but for users requesting job execution, it is preferable that jobs be executed on the on-premise computing device 30 as much as possible because the usage fee is low.

The on-premise computing device 30 is a high-performance computer that operates in an on-premise environment owned by the organization that owns the hybrid HPC system 1. The on-premise computing device 30 is, for example, a supercomputer. There may be one on-premise computing device 30 or multiple on-premise computing devices 30.

The on-premise computing device 30 receives a job execution command from the job management device 10 and executes the input job. After the job is executed, the on-premise computing device 30 transmits a notification of completion of the execution together with the execution result of the job to the job management device 10.

The cloud computing device 40 is a high-performance computer that operates in a cloud environment outside the organization that owns the hybrid HPC system 1. The cloud computing device 40 is, for example, a supercomputer. There may be one cloud computing device 40 or multiple cloud computing devices 40.

The cloud computing device 40 receives a job execution command from the job management device 10 and executes the input job. After the job is executed, the cloud computing device 40 transmits a notification of completion of execution together with the execution result of the job to the job management device 10.

The job management device 10 is, for example, a server device. The job management device 10 receives jobs submitted from each user terminal device 20. Then, the job management device 10 schedules the sequential execution of each submitted job. At this time, the job management device 10 observes the maximum execution time and the end deadline time specified by the user and takes into account the usage fees of the on-premise computing device 30 and the cloud computing device 40. Then, the job management device 10 selects either the on-premise computing device 30 or the cloud computing device 40 as the device to execute each job. Then, the job management device 10 causes each job to be executed by the on-premise computing device 30 or the cloud computing device 40 according to the scheduling. Then, the job management device 10 transmits the execution results of the job to each user terminal device 20. Furthermore, the job management device 10 notifies each user terminal device 20 of the usage fee and charges the usage fee.

The job management device 10 will be described in detail below. As shown in FIG. 1, the job management device 10 has a reception unit 11, a job management table 12, a job execution management unit 13, a job execution history table 14, and a billing amount calculation unit 15.

The reception unit 11 receives jobs submitted by users from the user terminal device 20. Here, the submitted jobs are accompanied by information on the maximum execution time and the end deadline time. The maximum execution time is the upper limit of the time to execute the submitted job, and when the maximum execution time has elapsed since the start of execution of the submitted job, the job ends, and the results achieved up to that point are regarded as the execution result. The end deadline time is the deadline for completing job execution required by the user, and is calculated backwards from the work delivery date, for example. The job management device 10 is required to complete the execution of the job by the end deadline time. In other words, the time obtained by subtracting the maximum execution time from the end deadline time is the execution start deadline time, which is the final deadline for starting execution of the job.

Next, the reception unit 11 registers the received job in the job management table 12. For example, the reception unit 11 adds a new job management record to the job management table 12, and registers the job submission time, the maximum execution time specified by the job submitter, and the end deadline time in the added job management record. Furthermore, the reception unit 11 calculates the execution start deadline time by subtracting the maximum execution time from the end deadline time. Next, the reception unit 11 registers the calculated execution start deadline time in the added job management record. The reception unit 11 also registers information of "waiting for execution start" in the execution status column of the added job management record, indicating that the job is not yet executed.

2 is a diagram showing an example of a job management table. As shown in FIG. 2, the job management table 12 has a job management record represented by each row for each submitted job. The job submission time, maximum execution time, end deadline time, execution start deadline time, and execution status are registered in the job management record. The job management record is generated by the reception unit 11 when the job is submitted, and a job ID (Identifier) is assigned by the reception unit 11. Furthermore, the job submission time, maximum execution time, end deadline time, and execution start deadline time are registered in the job management record. The execution status in the job management table 12 is information indicating whether the job to which the job ID registered in the job management record is assigned is before execution, during execution, or after execution. When the job management record is first created, the execution status is registered as waiting to start by the reception unit 11, and the information is then updated by the job execution management unit 13 according to the execution status of the job. In what follows, a job whose execution state is waiting to start will be referred to as a "job waiting to start execution", a job whose execution state is running will be referred to as a "job currently running", and a job whose execution state is finished will be referred to as a "job that has finished execution".

When the on-premise computing device 30 finishes executing a job and an empty space is created, the job execution management unit 13 executes the following resource selection and execution process. If the on-premise computing device 30 is empty and there is a job to be executed, any job is submitted to it, so if a job exists, the on-premise computing device 30 will execute the job continuously. The job execution management unit 13 checks the job management table 12, and obtains and lists information on jobs waiting to start execution that are registered in the job management table 12. If no jobs waiting to start execution are registered in the job management table 12, the job execution management unit 13 ends the job scheduling process.

When there is one job waiting to start execution, the job execution management unit 13 selects the on-premise computing device 30 as the device that will execute the job. The job execution management unit 13 then submits the job to the on-premise computing device 30 and instructs the on-premise computing device 30 to execute the job.

In addition, when there are multiple jobs waiting to start execution, the job execution management unit 13 sorts the jobs waiting to start execution in the order in which they were submitted. Here, the first job among the sorted jobs waiting to start execution is called the "first execution candidate job," and the second job from the top is called the "second execution candidate job."

The job execution management unit 13 adds the maximum execution time of the first execution candidate job to the current time, and sets the result as the expected execution end time of the first execution candidate job. Next, the job execution management unit 13 compares the expected execution end time of the first execution candidate job with the execution start deadline time of the second execution candidate job. If the expected execution end time of the first execution candidate job is before the execution start deadline time of the second execution candidate job, the job execution management unit 13 selects the on-premise computing device 30 as the device that will execute the first execution candidate job. Then, the job execution management unit 13 submits the first execution candidate job to the on-premise computing device 30 and instructs the on-premise computing device 30 to execute the first execution candidate job.

If the expected execution end time of the first execution candidate job is later than the execution start deadline time of the second execution candidate job, the job execution management unit 13 compares the maximum execution time of the first execution candidate job with the maximum execution time of the second execution candidate job.

If the maximum execution time of the first execution candidate job is equal to or greater than the maximum execution time of the second execution candidate job, the job execution management unit 13 selects the on-premise computing device 30 as a device for executing the first execution candidate job. The job execution management unit 13 then submits the first execution candidate job to the on-premise computing device 30 and instructs the on-premise computing device 30 to execute the first execution candidate job.

Conversely, if the maximum execution time of the first execution candidate job is shorter than the maximum execution time of the second execution candidate job, the job execution management unit 13 selects the cloud computing device 40 as the device that will execute the first execution candidate job. After that, the job execution management unit 13 submits the first execution candidate job to the cloud computing device 40 and instructs the cloud computing device 40 to execute the first execution candidate job.

The job execution management unit 13 then waits until an available space is available in the on-premise computing device 30. While waiting, the job execution management unit 13 monitors whether the execution start deadline of the job waiting to start execution is reached. If the execution start deadline of the job waiting to start execution is not reached while waiting, the job execution management unit 13 executes the resource selection execution process described above. On the other hand, if the execution start deadline of the job waiting to start execution arrives while waiting, the job execution management unit 13 executes the following cloud usage execution process.

The job execution management unit 13 selects a cloud computing device 40 to execute the job awaiting execution whose execution start deadline has arrived. The job execution management unit 13 then submits the job awaiting execution whose execution start deadline has arrived to the cloud computing device 40, and instructs the cloud computing device 40 to execute the job.

The job execution management unit 13 executes the following process regardless of whether the job is executed by the on-premise computing device 30 or the cloud computing device 40. When the job execution starts, the job execution management unit 13 changes the execution state of the job management record corresponding to the job that has started to be executed in the job management table 12 to "in execution". Furthermore, the job execution management unit 13 adds a job execution history record to the job execution history table 14. Then, the job execution management unit 13 registers the job ID of the job that has started to be executed in the added job execution history record, and also registers the selected execution location, the execution ID assigned as identification information for the job execution, and the execution start time.

When the job execution management unit 13 receives a notification of the end of job execution from the on-premise computing device 30 or the cloud computing device 40, it changes the execution state of the job management record corresponding to the finished job in the job management table 12 to "executed". Furthermore, the job execution management unit 13 registers the execution time in the job execution history record corresponding to the finished job in the job execution history table 14. Furthermore, when the job is executed by the cloud computing device 40, the job execution management unit 13 calculates the cloud service usage fee for the execution of the job based on a predetermined calculation method. Then, the job execution management unit 13 registers the cloud service usage fee in the job execution history record corresponding to the finished job.

Here, state 121 in FIG. 2 is the job management table 12 in a state where job with job ID #1 is being executed, and jobs with job IDs #2 and #3 are waiting to start execution. For example, hereinafter, jobs with job IDs #1 to #3 are referred to as "jobs #1 to #3," respectively. When job #1 starts execution while jobs #2 and #3 are waiting to start execution, the job execution management unit 13 transitions the job management table 12 to state 121.

In addition, state 122 in FIG. 2 is the job management table 12 in a state where job #1 has finished execution and jobs #2 and #3 are being executed. For example, when job #1 finishes execution and jobs #2 to #3 are being executed, the job execution management unit 13 transitions the job management table 12 to state 122.

Figure 3 is a diagram showing an example of a job execution history table. When the on-premise computing device 30 starts execution of job #1 from a state where there is no job, the job execution management unit 13 adds a job execution history record of job #1 to the job execution history table 14. Furthermore, the job execution management unit 13 registers the on-premise computing device 30 as the execution location in the job execution history record of job #1, registers the execution ID and the execution start time, and transitions the job execution history table 14 to state 141.

The cloud service usage requirement flag in the job execution history table 14 is information indicating whether or not a cloud service could have been used to execute the job, and is registered by the billing amount calculation unit 15, which will be described later.

Furthermore, when job #2 is executed on the cloud computing device 40 and job #3 is executed on the on-premise computing device 30 from state 141, the job execution management unit 13 adds job execution history records for jobs #2 and #3. After that, when the execution of jobs #1 to #3 is completed, the job execution management unit 13 registers the execution time and cloud service usage fee for each, and transitions the job execution history table 14 to state 142.

The billing calculation unit 15 executes the following billing calculation process when it determines that the billing amount for cloud service usage is calculated when all jobs have been executed and there are no more jobs waiting to be executed. The billing calculation unit 15 calculates the total amount of cloud service usage fees registered in the job execution history table 14.

The billing calculation unit 15 then sorts the jobs that have finished execution and are registered in the job execution history table 14 in the order in which the jobs were submitted. The billing calculation unit 15 then obtains the job submission time of the top job in the sorted result from the job management table 12, and sets this as the initial value of the time when the on-premise computing device 30 can start using the job. The billing calculation unit 15 then sets the initial value of the estimated total cloud service usage time to 0.

Next, the billing calculation unit 15 executes the following process for each job in the sorted jobs, starting from the beginning in sorted order. Here, the job to be processed is called the target job. The billing calculation unit 15 obtains the execution start deadline time of the target job from the job management table 12, and compares it with the available start time of the on-premise computing device 30 at that time.

If the execution start deadline time of the target job is earlier than the available time of the on-premise computing device 30, the billing calculation unit 15 registers "Applicable" as the value of the cloud service usage requirement flag in the job execution history record of the target job in the job execution history table 14. Next, the billing calculation unit 15 adds the execution time of the target job to the estimated total cloud service usage time.

Conversely, if the execution start deadline time of the target job is after the available time of the on-premise computing device 30, "Not applicable" is registered as the value of the cloud service usage requirement flag in the job execution history record of the target job in the job execution history table 14. Next, the billing amount calculation unit 15 adds the execution time of the target job to the available time of the on-premise computing device 30 to update the available time of the on-premise computing device 30.

For example, when the job execution history table 14 after the execution of jobs #1 to #3 is in state 142 of FIG. 3, the billing calculation unit 15 sets the job input time of job #1 as the initial value of the available time. If the execution start deadline time of job #1 is later than the available time of the on-premise computing device 30, the billing calculation unit 15 registers "Not applicable" as the value of the cloud service usage required flag of job #1 in the job execution history table 14. Furthermore, the billing calculation unit 15 adds the execution time of job #1 to 0, which is the initial value of the available time of the on-premise computing device 30, to update the available time of the on-premise computing device 30. Similarly, the billing calculation unit 15 also registers the cloud service usage required flag and updates the available time of the on-premise computing device 30 for job #2. Next, the billing calculation unit 15 compares the execution start deadline time of job #3 with the updated available time of the on-premise computing device 30. If the execution start deadline time of job #3 is before the updated available time of the on-premise computing device 30, the billing calculation unit 15 registers "Applicable" as the value of the cloud service usage requirement flag of job #3 in the job execution history table 14. Furthermore, the billing calculation unit 15 adds the execution time of job #3 to the estimated total cloud service usage time. As a result, the billing calculation unit 15 transitions the job execution history table 14 to state 143 in FIG. 3.

After performing the above process for all sorted jobs, the billing calculation unit 15 divides the execution time by the estimated total cloud service usage time for each job whose cloud service usage requirement flag has the value "applicable". The billing calculation unit 15 then multiplies the total cloud service usage fee by the result of this division to calculate the billing amount for the cloud service usage fee for each job whose cloud service usage requirement flag has the value "applicable".

Then, the billing calculation unit 15 notifies the calculated billing amount for cloud service usage to the user terminal device 2 that submitted the job for which the value of the cloud service usage necessary flag is set to "applicable." The billing calculation unit 15 also notifies the user terminal device 2 that submitted the job for which the value of the cloud service usage necessary flag is set to "not applicable" of the billing amount for usage of the on-premise computing device 30.

Figure 4 is a flowchart showing the entire job execution process. Next, the overall flow of job execution processing in the hybrid HPC system 1 will be explained with reference to Figure 4.

The job management device 10 determines whether or not an available space has been created in the on-premise computing device 30 (step S1).

If an available space is available on the on-premise computing device 30 (step S1: Yes), the job management device 10 executes a resource selection and execution process (step S2).

On the other hand, if there is no free space in the on-premise computing device 30 (step S1: No), the job management device 10 determines whether there is a job waiting to start execution whose execution start deadline has arrived (step S3). If there is no job waiting to start execution whose execution start deadline has arrived (step S3: No), the job execution process returns to step S1.

On the other hand, if there is a job waiting to start execution whose execution start deadline has arrived (step S3: Yes), the job management device 10 executes a cloud-utilizing execution process (step S4). Specifically, the job execution management unit 13 selects a cloud computing device 40 to execute the job waiting to start execution whose execution start deadline has arrived. Then, the job execution management unit 13 submits the job waiting to start execution whose execution start deadline has arrived to the cloud computing device 40, and instructs the cloud computing device 40 to execute the job.

Then, the job management device 10 determines whether to end the operation based on the presence or absence of a shutdown instruction, etc. (step S5). If the operation is to be continued (step S5: No), the job execution process returns to step S1.

On the other hand, if the operation is to be terminated (step S5: Yes), the job management device 10 terminates the job execution process.

Figure 5 is a flowchart of the resource selection execution process by the job management device according to the first embodiment. The flow shown in Figure 5 corresponds to an example of the process executed in step S2 of Figure 4. Next, the flow of the resource selection execution process by the job management device 10 according to the present embodiment will be described with reference to Figure 5.

The job execution management unit 13 refers to the job management table 12 and lists jobs waiting to start execution (step S101).

Next, the job execution management unit 13 determines whether or not there is one or more jobs waiting to start execution (step S102). If there is no job waiting to start execution (step S102: No), the job execution management unit 13 ends the resource selection execution process.

In contrast, if there is one or more jobs waiting to start execution (step S102: Yes), the job execution management unit 13 determines whether there is one job waiting to start execution (step S103).

If there is one job waiting to start execution (step S103: Yes), the job execution management unit 13 selects the on-premise computing device 30 as a resource to execute the one job waiting to start execution (step S104).

Then, the job execution management unit 13 executes the job that is waiting to start execution on the on-premise computing device 30 (step S105). After that, the job execution management unit 13 ends the resource selection and execution process.

On the other hand, if there are multiple jobs waiting to start execution (step S103: No), the job execution management unit 13 sorts the jobs waiting to start execution in the order in which they were submitted (step S106).

Next, the job execution management unit 13 calculates the estimated execution end time of the first execution candidate job, which is the first job in the sorted result, by adding the maximum execution time of the first execution candidate job to the current time (step S107).

Next, the job execution management unit 13 determines whether the expected execution end time of the first execution candidate job is later than the execution start deadline time of the second execution candidate job (step S108).

If the expected end time of the first execution candidate job is later than the execution start deadline time of the second execution candidate job (step S108: Yes), the job execution management unit 13 determines whether the maximum execution time of the first execution candidate job is longer than the maximum execution time of the second execution candidate job (step S109).

If the maximum execution time of the first execution candidate job is longer than the maximum execution time of the second execution candidate job (step S109: Yes), the job execution management unit 13 selects the cloud computing device 40 as a resource for executing the first execution candidate job (step S110).

Then, the job execution management unit 13 causes the cloud computing device 40 to execute the first execution candidate job (step S111). After that, the job execution management unit 13 ends the resource selection and execution process.

On the other hand, if the expected end time of the first execution candidate job is before the execution start deadline time of the second execution candidate job (step S108: No), the job execution management unit 13 executes the following process. Similarly, if the maximum execution time of the first execution candidate job is equal to or greater than the maximum execution time of the second execution candidate job (step S109: No), the job execution management unit 13 also executes the following process. The job execution management unit 13 selects the on-premise computing device 30 as a resource for executing the first execution candidate job (step S112).

Then, the job execution management unit 13 executes the first execution candidate job on the on-premise computing device 30 (step S113). After that, the job execution management unit 13 ends the resource selection and execution process.

Figure 6 is a flowchart of the billing amount calculation process by the job management device according to the first embodiment. Next, the flow of the billing amount calculation process by the job management device 10 according to the present embodiment will be described with reference to Figure 6.

The billing calculation unit 15 calculates the total amount of cloud service usage fees registered in the job execution history table 14 (step S201).

Next, the billing calculation unit 15 sorts the jobs that have been completed and are registered in the job execution history table 14 in the order in which they were submitted (step S202).

Next, the billing calculation unit 15 obtains the job input time of the first job in the sorted result from the job management table 12, and sets it as the initial value of the available start time of the on-premise computing device 30 (step S203).

Next, the billing calculation unit 15 sets the estimated total cloud service usage time to an initial value of 0 (step S204).

Next, the billing calculation unit 15 assigns consecutive numbers from 1 to the first job in the sorting result through to the last job. Here, it is assumed that the last job is assigned N. The billing calculation unit 15 then sets n to 1 (step S205).

Next, the billing calculation unit 15 determines whether n is greater than N (step S206).

If n is less than or equal to N (step S206: No), the billing calculation unit 15 selects the job to which the number n is assigned as the target job (step S207).

The execution start deadline time of the target job is obtained from the job management table 12, and it is determined whether it is before the time when the on-premise computing device 30 can start being used (step S208).

If the execution start deadline time of the target job is before the time when the on-premise computing device 30 can start using the target job (step S208: Yes), the billing amount calculation unit 15 sets the value of the cloud service usage requirement flag of the target job to "applicable" (step S209).

Next, the billing calculation unit 15 updates the estimated total cloud service usage time by adding the execution time of the job assigned number n (step S210).

In contrast, if the execution start deadline time of the target job is later than the time when the on-premise computing device 30 can start using the target job (step S208: No), the billing amount calculation unit 15 sets the value of the cloud service usage requirement flag of the target job to "not applicable" (step S211).

Next, the billing calculation unit 15 adds the execution time of the target job to the available time of the on-premise computing device 30 to update the available time of the on-premise computing device 30 (step S212).

Then, the billing calculation unit 15 increments n by 1 (step S213). Then, the billing calculation process returns to step S206. This allows the billing calculation unit 15 to set the value of the cloud service usage requirement flag for the sorted jobs from the first job to the last job.

On the other hand, if n is greater than N (step S206: Yes), the billing amount calculation unit 15 sets n to 1 (step S214).

Next, the billing calculation unit 15 selects the job to which the number n is assigned as the target job (step S215).

Next, the billing calculation unit 15 determines whether the value of the cloud service usage requirement flag for the target job is "applicable" (step S216). If the value of the cloud service usage requirement flag for the target job is "not applicable" (step S216: No), the billing calculation process proceeds to step S218.

In contrast, if the value of the cloud service usage requirement flag for the target job is "Applicable" (step S216: Yes), the billing calculation unit 15 divides the execution time by the estimated total cloud service usage time. Then, the billing calculation unit 15 multiplies the total cloud service usage fee by the result of the division to calculate the billing amount for the cloud service usage fee for the target job (step S217).

Then, the billing amount calculation unit 15 determines whether n has reached N (step S218). If n is less than N (step S218: No), the billing amount calculation unit 15 increments n by 1 (step S219). Then, the billing amount calculation process returns to step S215.

On the other hand, if n reaches N (step S218: Yes), the billing calculation unit 15 notifies the user terminal device 2 that is the sender of a job for which the cloud service usage flag has a value of "applicable" of the cloud service usage fee billing amount for that job as the usage fee billing amount. In addition, the billing calculation unit 15 notifies the user terminal device 2 that is the sender of a job for which the cloud service usage flag has a value of "not applicable" of the usage fee billing amount for the on-premise computing device 30 (step S220).

Figure 7 shows the execution status when jobs are executed sequentially in the order in which they were submitted, and when the job management device according to the first embodiment executes the jobs. In graphs 201 and 202, the horizontal axis represents time, and the vertical axis represents instances. End deadline times 211 to 213 are the end deadline times of jobs #1 to #3, respectively. Here, a case will be described in which one on-premise computing device 30 executes jobs one at a time.

Graph 201 shows the execution results when jobs are executed sequentially in the order in which they were submitted. The execution of jobs with a diagonal line pattern represents job execution by the cloud computing device 40. In this case, the on-premise computing device 30 starts executing job #2 after finishing execution of job #1. Then, since the execution start deadline time for job #3 arrives while job #2 is being executed, job #3 is executed by the cloud computing device 40.

In contrast, graph 202 shows the results of job execution by the job management device 10 according to the first embodiment. In this case, after the execution of job #1 is completed, the execution time of job #3 is longer than the execution time of job #2, so job #2 is executed by the cloud computing device 40. Then, job #3 is executed by the on-premise computing device 30.

In this way, the job management device 10 according to this embodiment executes jobs with short execution times on the cloud computing device 40 and executes jobs with long execution times on the on-premise computing device 30. This makes it possible to reduce overall charges in the hybrid HPC system 1.

As described above, the job management device according to this embodiment executes a job in an on-premise environment if the predicted execution end time of the job is earlier than the execution start deadline time of the next job. Also, if the maximum execution time of the job is equal to or longer than the maximum execution time of the next job, the job management device executes the job in an on-premise environment. Conversely, if the predicted execution end time of the job is after the execution start deadline time of the next job and the maximum execution time is shorter than the maximum execution time of the next job, the job management device executes the job in a cloud environment. Furthermore, if there is no free space in the on-premise environment and the execution start deadline time calculated from the specified end deadline time of a specific job arrives, the job management device executes the job in a cloud environment.

This allows jobs with as short an execution time as possible to be executed in a cloud environment while adhering to the specified deadline for each job. This allows you to reduce charges while still adhering to the job's deadline.

(Modification)
In the first embodiment, the job management device 10 uses the maximum execution time to determine whether to execute a job on the on-premise computing device 30 or the cloud computing device 40. However, the maximum execution time is a time that is overestimated in order to complete a job, and the execution is usually completed in a shorter time than the maximum execution time. Therefore, the job management device 10 according to this modified example predicts the execution time of each job from the execution results of past jobs and determines whether to execute the job on the on-premise computing device 30 or the cloud computing device 40. Details of the on-premise computing device 30 according to this modified example are described below.

The job execution management unit 13 in this modified example has in advance an execution time prediction coefficient according to the characteristics of the job, which is calculated from the execution results of many jobs executed in the past. The job characteristics include the type of calculation, the number of calculations, etc.

The job execution management unit 13 identifies an execution time prediction coefficient according to the characteristics of the first execution candidate job. Then, the job execution management unit 13 multiplies the maximum execution time of the first execution candidate job by the execution time prediction coefficient of the first execution candidate job to calculate the predicted execution time of the first execution candidate job. Next, the job execution management unit 13 adds the predicted execution time of the first execution candidate job to the current time, and sets the result as the predicted execution end time of the first execution candidate job.

Next, the job execution management unit 13 compares the predicted execution end time of the first execution candidate job with the execution start deadline time of the second execution candidate job. If the predicted execution end time of the first execution candidate job is before the execution start deadline time of the second execution candidate job, the job execution management unit 13 selects the on-premise computing device 30 as the device that will execute the first execution candidate job. Then, the job execution management unit 13 submits the first execution candidate job to the on-premise computing device 30 and instructs the on-premise computing device 30 to execute the first execution candidate job.

Conversely, if the predicted execution end time of the first execution candidate job is later than the execution start deadline time of the second execution candidate job, the job execution management unit 13 identifies an execution time prediction coefficient according to the characteristics of the second execution candidate job. Then, the job execution management unit 13 multiplies the maximum execution time of the second execution candidate job by the execution time prediction coefficient of the second execution candidate job to calculate the predicted execution time of the second execution candidate job. Next, the job execution management unit 13 compares the predicted execution time of the first execution candidate job with the predicted execution time of the second execution candidate job.

If the expected execution time of the first execution candidate job is equal to or longer than the expected execution time of the second execution candidate job, the job execution management unit 13 selects the on-premise computing device 30 as the device that will execute the first execution candidate job.

Conversely, if the expected execution time of the first execution candidate job is shorter than the expected execution time of the second execution candidate job, the job execution management unit 13 selects the cloud computing device 40 as the device to execute the first execution candidate job.

As described above, the job management device according to this modified example predicts the actual execution time of each job from the maximum execution time based on the results of past job execution, and determines whether to execute the job in an on-premise environment or a cloud environment. The maximum execution time is estimated with a margin of error, and there are many cases where the maximum execution time differs significantly from the actual execution time. Therefore, the execution time predicted based on the results of past job execution can be considered a more realistic value, and the job execution environment can be more appropriately selected. Therefore, it is possible to more appropriately reduce charges while meeting the job completion deadline.

Next, a second embodiment will be described. A hybrid HPC system 1 according to this embodiment is also shown in FIG. 1. A job management device 10 according to this embodiment causes an on-premise computing device 30 to execute a job whose execution start deadline is as close as possible and whose maximum execution time is as long as possible. Then, when the on-premise computing device 30 becomes capable of executing a job waiting to start execution by the execution start deadline, the job management device 10 causes the on-premise computing device 30 to execute the job. Details of the selection of a job execution environment by the job management device 10 according to this embodiment will be described below. In the following description, the functions of each part similar to those of the first embodiment will be omitted.

When the on-premise computing device 30 finishes executing a job and an available resource is created, the job execution management unit 13 executes the following resource selection and execution process. The job execution management unit 13 checks the job management table 12, and obtains and lists information on jobs waiting to start execution that are registered in the job management table 12. If no jobs waiting to start execution are registered in the job management table 12, the job execution management unit 13 ends the job scheduling process.

When there is one job waiting to start execution, the job execution management unit 13 selects the on-premise computing device 30 as the device that will execute the job. The job execution management unit 13 then submits the job to the on-premise computing device 30 and instructs the on-premise computing device 30 to execute the job.

On the other hand, if there are multiple jobs waiting to start execution, the job execution management unit 13 sorts the acquired jobs waiting to start execution in ascending order of execution start deadline time and descending order of maximum execution time. Here, the first job among the sorted jobs waiting to start execution is called the "first execution candidate job," and the second job from the top is called the "second execution candidate job." After this, the job execution management unit 13 performs the same process as in Example 1.

That is, the job execution management unit 13 according to this embodiment differs from the job execution management unit 13 according to the first embodiment in that it sorts the acquired pending jobs in ascending order of execution start deadline time and descending order of maximum execution time. By sorting in this manner, the job execution management unit 13 executes the jobs with the earliest execution start deadline time on the on-premise computing device 30. Furthermore, the job execution management unit 13 executes the jobs with the latest execution start deadline time on the on-premise computing device 30 after the execution of the jobs with the earlier execution start deadline time is completed, or executes them on the cloud computing device 40 when the execution start deadline time arrives before that.

Figure 8 is a diagram showing an example of the execution status of a job by a job management device according to the second embodiment. In graphs 203 and 204, the horizontal axis represents time and the vertical axis represents instances. End deadline times 211 to 213 are the end deadline times of jobs #1 to #3, respectively. Here, a case will be described in which one on-premise computing device 30 executes jobs one by one. The execution of a job marked with a diagonal line pattern represents job execution by a cloud computing device 40.

Graph 203 shows the execution result when no free space is available on the on-premise computing device 30 by the execution start deadline of the next job to be executed. After the execution of job #1 is completed, the job management device 10 causes the on-premise computing device 30 to execute job #3 because the execution start deadline of job #2 is later than the execution start deadline of job #3. Thereafter, when the execution start deadline of job #2 arrives while job #3 is being executed, the job management device 10 causes the cloud computing device 40 to execute job #2, as shown in graph 203.

In contrast, when the execution of job #3 ends before the execution start deadline of job #2, the job management device 10 performs resource selection and execution processing for job #2 at that time. In this case, since the on-premise computing device 30 is available and there is only job #2, the job management device 10 selects the on-premise computing device 30 as the resource for executing job #2. Then, the job management device 10 causes the on-premise computing device 30 to execute job #2, as shown in graph 204.

In the above explanation, the job execution management unit 13 selected the execution environment for each job using the maximum execution time. However, instead of the maximum execution time, the job execution management unit 13 may use the result of multiplying the maximum execution time by the execution time prediction coefficient of the target job.

As described above, the job management device according to this embodiment waits until the execution start deadline of each job, and if the job can be executed in the on-premises environment by that execution start deadline, executes the job in the on-premises environment; otherwise, executes the job in the cloud environment. This allows more jobs to be executed in the on-premises environment, and makes it possible to more appropriately reduce charges while meeting the job end deadline.

Next, a third embodiment will be described. A hybrid HPC system 1 according to this embodiment is also shown in FIG. 1. A job management device 10 according to this embodiment compares two or more jobs and determines a job to be executed by an on-premise computing device 30 so as to shorten the total usage time of the cloud computing device 40 as much as possible. Details of the selection of a job execution environment by the job management device 10 according to this embodiment will be described below. In the following explanation, explanations of the functions of each part similar to those of the first embodiment will be omitted. Here, a case will be described in which there are multiple on-premise computing devices 30, each of which executes one job at a time sequentially.

When the on-premise computing device 30 finishes executing a job and an available resource is created, the job execution management unit 13 executes the following resource selection and execution process. The job execution management unit 13 checks the job management table 12, and obtains and lists information on jobs waiting to start execution that are registered in the job management table 12. If no jobs waiting to start execution are registered in the job management table 12, the job execution management unit 13 ends the job scheduling process.

When there is one job waiting to start execution, the job execution management unit 13 selects the on-premise computing device 30 as the device that will execute the job. The job execution management unit 13 then submits the job to the on-premise computing device 30 and instructs the on-premise computing device 30 to execute the job.

On the other hand, if there are multiple jobs waiting to start execution, the job execution management unit 13 sorts the acquired jobs waiting to start execution in ascending order of execution start deadline time and descending order of maximum execution time.

The job execution management unit 13 adds the maximum execution time of the top job in the sorted result to the current time, and regards the result as the expected execution end time of the top job. In addition, for each job currently being executed in the on-premise computing device 30, the job execution management unit 13 adds the maximum execution time to the execution start time for each currently being executed job, and regards the sum as the expected execution end time of the currently being executed job. Then, the job execution management unit 13 regards the earliest expected execution end time of the expected execution end times of the top job and all currently being executed jobs in the on-premise computing device 30 as the expected time when the on-premise computing device 30 becomes available.

The job execution management unit 13 then extracts jobs from the sorted jobs whose execution start deadline time is earlier than the expected time when the on-premise computing device 30 becomes available. The job execution management unit 13 then sets the extracted jobs as the first to Nth on-premise execution candidate jobs in the order of sorting.

The job execution management unit 13 then selects a target on-premise execution candidate job from each of the first to Nth on-premise execution candidate jobs in order, and executes the following resource selection and execution process. The job execution management unit 13 adds the maximum execution time of the target on-premise execution candidate job to the current time, and sets the result as the initial value of the predicted time when the on-premise computing device 30 can start using the job. The job execution management unit 13 also sets the initial value of the estimated cloud service total usage time to 0.

Next, the job execution management unit 13 selects jobs one by one in numerical order from among the first to Nth on-premise execution candidate jobs other than the target on-premise execution candidate job, and performs the following processing in order. Hereinafter, the job selected by the job execution management unit 13 is referred to as the selected job. If the execution start deadline of the selected job is earlier than the expected time when the on-premise computing device 30 will become available, the job execution management unit 13 adds the maximum execution time of the selected job to the estimated cloud service total usage time. On the other hand, if the execution start deadline of the selected job is after the expected time when the on-premise computing device 30 will become available, the job execution management unit 13 adds the maximum execution time of the selected job to the expected time when the on-premise computing device 30 will become available.

As a result, the job execution management unit 13 calculates the estimated cloud service total usage time value for the N target on-premise execution candidate jobs. Then, the job execution management unit 13 selects the on-premise execution candidate job for which the estimated cloud service total usage time value is the smallest. Then, the job execution management unit 13 selects the on-premise computing device 30 as a resource for executing the selected on-premise execution candidate job. Thereafter, the job execution management unit 13 submits the selected on-premise execution candidate job to the on-premise computing device 30, and causes the on-premise computing device 30 to execute the selected on-premise execution candidate job.

Figure 9A is a first flowchart of the resource selection execution process by the job management device according to the third embodiment. Also, Figure 9B is a second flowchart of the resource selection execution process by the job management device according to the third embodiment. Next, the flow of the resource selection execution process by the job management device 10 according to the present embodiment will be described with reference to Figures 9A and 9B.

The job execution management unit 13 refers to the job management table 12 and lists jobs waiting to start execution (step S301).

Next, the job execution management unit 13 determines whether or not there is one or more jobs waiting to start execution (step S302). If there is no job waiting to start execution (step S302: No), the job execution management unit 13 ends the resource selection execution process.

In contrast, if there is one or more jobs waiting to start execution (step S302: Yes), the job execution management unit 13 determines whether there is one job waiting to start execution (step S303).

If there is one job waiting to start execution (step S303: Yes), the job execution management unit 13 selects the on-premise computing device 30 as a resource to execute the one job waiting to start execution (step S304).

Then, the job execution management unit 13 executes the job that is waiting to start execution on the on-premise computing device 30 (step S305). After that, the job execution management unit 13 ends the resource selection and execution process.

In contrast, if there are multiple jobs waiting to start execution (step S303: No), the job execution management unit 13 sorts the jobs waiting to start execution in ascending order of execution start deadline time and descending order of maximum execution time (step S306).

Next, the job execution management unit 13 adds the maximum execution time of the top job in the sorted result to the current time to calculate the expected execution end time of the top job (step S307).

The job execution management unit 13 also calculates the expected execution end time for each running job in the on-premise computing device 30 by adding the maximum execution time to the execution start time for each running job (step S308).

Next, the job execution management unit 13 determines the earliest expected execution end time among the expected execution end times of the leading job and all running jobs in the on-premise computing device 30 as the expected time when availability will occur in the on-premise computing device 30 (step S309).

Next, the job execution management unit 13 extracts jobs from the sorted results whose execution start deadline time is earlier than the expected time when the on-premise computing device 30 will be available. The job execution management unit 13 then determines the extracted jobs as the first to Nth on-premise execution candidate jobs in the order of sorting (step S310).

Next, the job execution management unit 13 sets n=1 (step S311).

Next, the job execution management unit 13 sets the nth on-premise execution candidate job as the target on-premise execution candidate job (step S312).

Next, the job execution management unit 13 adds the maximum execution time of the target on-premise execution candidate job to the current time and sets the result as the initial value of the predicted time when the on-premise computing device 30 can start using (step S313).

The job execution management unit 13 also sets the initial value of the estimated total cloud service usage time to 0 (step S314).

Next, the job execution management unit 13 selects one of the first to Nth on-premise execution candidate jobs, other than the nth on-premise execution candidate job, that is the lowest-numbered unselected job, as a selected job (step S315).

The job execution management unit 13 determines whether the execution start deadline time of the selected job is earlier than the expected time when the on-premise computing device 30 becomes available (step S316).

If the execution start deadline time of the selected job is earlier than the expected time when the on-premise computing device 30 will be available (step S316: Yes), the maximum execution time of the selected job is added to the estimated total cloud service usage time (step S317).

In contrast, if the execution start deadline of the selected job is after the expected time when the on-premise computing device 30 becomes available (step S316: No), the job execution management unit 13 adds the maximum execution time of the selected job to the expected time when the on-premise computing device 30 becomes available (step S318).

Then, the job execution management unit 13 determines whether all on-premise execution candidate jobs other than the n-th on-premise execution candidate job have been considered (step S319). If there are on-premise execution candidate jobs that have not been considered (step S319: No), the resource selection and execution process returns to step S315.

On the other hand, if all on-premise execution candidate jobs other than the n-th on-premise execution candidate job have been considered (step S319: Yes), the job execution management unit 13 proceeds to the next process. The job execution management unit 13 determines whether n=N, i.e., whether all on-premise execution candidate jobs have been considered as target on-premise execution candidate jobs (step S320).

If n=N is not true (step S320: No), the job execution management unit 13 increments n by 1 (step S321). After that, the resource selection and execution process returns to step S312.

On the other hand, if n=N (step S320: Yes), the job execution management unit 13
An on-premise execution candidate job that has the smallest estimated total cloud service usage time is selected (step S322).

Then, the job execution management unit 13 causes the on-premise computing device 30 to execute the selected on-premise execution candidate job (step S323).

Figure 10 is a diagram showing a comparison of execution results between the job management device according to the second embodiment and the job management device according to the third embodiment. Here, a case where jobs #11 to #13 registered in the job management table 12 shown in Figure 10 are executed will be described.

The job execution history table 14 showing the execution results when a job is executed using the job management device 10 according to the second embodiment is as shown in state 301. The job execution history table 14 showing the execution results when a job is executed using the job management device 10 according to the third embodiment is as shown in state 302. In this case, the total cloud service usage fee is reduced from 16.4 to 15.2 when the job is executed using the job management device 10 according to the third embodiment, compared to when the job is executed using the job management device 10 according to the second embodiment. In other words, charges can be more appropriately reduced.

FIG. 11 is a diagram showing a comparison of the execution status between the job management device according to the second embodiment and the job management device according to the third embodiment. In graphs 303 and 304, the horizontal axis represents time and the vertical axis represents instances. End deadline times 311 to 313 are the end deadline times of jobs #11 to #13, respectively. The execution of jobs marked with a diagonal line pattern represents job execution by the cloud computing device 40.

Graph 303 shows the execution result by the job management device 10 according to the second embodiment. In this case, the job management device 10 according to the second embodiment executes job #13, which has the earliest execution start deadline time and the longest maximum execution time, on the on-premise computing device 30. Then, since the execution start deadline times of jobs #11 and #12 arrive while job #13 is being executed, the job management device 10 according to the second embodiment executes jobs #11 and #12 on the cloud computing device 40.

In contrast, graph 304 shows the execution result by the job management device 10 according to the third embodiment. In this case, the job management device 10 according to the third embodiment selects job #12 as a job with an execution start deadline as early as possible and a maximum execution time as long as possible, and executes it on the on-premise computing device 30 as a job that shortens the total cloud service usage time as much as possible. Then, since the execution start deadline time of job #13 arrives while job #12 is being executed, the job management device 10 according to the third embodiment executes job #13 on the cloud computing device 40. Also, since the execution start deadline time of job #11 does not arrive by the time the execution of job #12 is completed, the job management device 10 according to the third embodiment can execute job #11 on the on-premise computing device 30. Then, the job management device 10 according to the third embodiment can complete the execution of all of jobs #11 to #13 by the end deadline time.

In the above explanation, the job execution management unit 13 selected the execution environment for each job using the maximum execution time. However, instead of the maximum execution time, the job execution management unit 13 may use the result of multiplying the maximum execution time by the execution time prediction coefficient of the target job.

As described above, the job management device according to this embodiment considers two or more jobs together and executes in an on-premises environment the job with the earliest possible execution start deadline and the longest possible maximum execution time, which will result in the shortest possible total cloud service usage time. This allows more jobs to be executed in an on-premises environment, and makes it possible to more appropriately reduce charges while still meeting job end deadlines.

(Hardware configuration)
12 is a diagram showing a hardware configuration of the job management apparatus 10. Next, an example of a hardware configuration for realizing each function of the job management apparatus 10 will be described with reference to FIG.

As shown in FIG. 12, the job management device 10 has, for example, a CPU (Central Processing Unit) 91, a memory 92, a hard disk 93, and a network interface 94. The CPU 91 is connected to the memory 92, the hard disk 93, and the network interface 94 via a bus.

The network interface 94 is an interface for communication between the job management device 10 and an external device. The network interface 94 relays communication between the CPU 91 and, for example, the user terminal device 20, the on-premise computing device 30, and the cloud computing device 40.

The hard disk 93 is an auxiliary storage device. The hard disk 93 stores the job management table 12 and the job execution history table 14 illustrated in FIG. 1. The hard disk 93 also stores programs that realize the functions of the reception unit 11, the job execution management unit 13, and the billing amount calculation unit 15 illustrated in FIG. 1.

Memory 92 is a main storage device. Memory 92 may be, for example, a dynamic random access memory (DRAM).

The CPU 91 reads various programs from the hard disk 93, expands them into the memory 92, and executes them. In this way, the CPU 91 realizes the functions of the reception unit 11, the job execution management unit 13, and the billing amount calculation unit 15 illustrated in FIG. 1.

REFERENCE SIGNS LIST 1 Hybrid HPC system 10 Job management device 11 Reception unit 12 Job management table 13 Job execution management unit 14 Job execution history table 15 Billing amount calculation unit 20 User terminal device 30 On-premise computing device 40 Cloud computing device

Claims (7)

A job management program for controlling a hybrid system including an on-premise computing device operating in an on-premise environment and a cloud computing device operating in a billing cloud environment, comprising:
When the first job, the second job, and the third job are executed in this order,
Initiating execution of the first job on the on-premise computing device;
and if neither the second job nor the third job is completed by a respective end deadline time that is predetermined to be executed after the other job is executed after the first job is completed, and if an expected execution time of the second job is shorter than an expected execution time of the third job, the second job is assigned to the cloud computing device and the third job is assigned to the on-premise computing device. causing the on-premise computing device to execute the third job after completion of execution of the first job;
2. The job management program according to claim 1, further comprising: causing the computer to execute a process of causing the on-premise computing device to execute the second job after completion of the third job, when execution of the third job is completed by an execution start deadline time for finishing the second job by an end deadline time of the second job. The job management program according to claim 1, characterized in that the computer is caused to execute a process of acquiring the maximum execution time and the end deadline time of each of the first job, the second job, and the third job, and determining, based on the maximum execution time and the predicted execution time, whether or not the execution of the third job will be completed by a predetermined end deadline time of the third job if the execution of the third job is started after the execution of the first job is completed. The job management program according to claim 3, characterized in that the computer is caused to execute a process of calculating the predicted execution time for each of the first job, the second job, and the third job by multiplying the maximum execution time by an execution time prediction coefficient that represents the ratio between the maximum execution time based on past execution results of the same type of job and the actual execution result, and determining whether or not the execution of the third job will be completed by a predetermined deadline for the end of the third job if the execution of the third job is started after the execution of the first job is completed, based on the calculated predicted execution time. obtaining a maximum execution time and a deadline for completion of each of the second job and the third job;
calculating an execution start deadline time for each of the second job and the third job by subtracting the maximum execution time from the end deadline time;
acquiring execution times, which are execution results, for the second job and the third job,
2. The job management program according to claim 1, further comprising: identifying a job to be executed on the cloud computing device when the second job and the third job are executed in sequence based on the execution start deadline time; and calculating a usage fee of the cloud computing device for each of the second job and the third job by prorating a total amount of usage fees of the cloud computing device based on the execution time of the identified job. A job management method in a hybrid system including an on-premise computing device operating in an on-premise environment and a cloud computing device operating in a billing cloud environment, comprising:
A job management device
When the first job, the second job, and the third job are executed in this order,
Initiating execution of the first job on the on-premise computing device;
and when neither the second job nor the third job is completed by a respective end deadline time that is predetermined to be executed after the other job is executed after the first job is completed, if an expected execution time of the second job is shorter than an expected execution time of the third job, the second job is assigned to the cloud computing device and the third job is assigned to an on-premise computing device. A hybrid system including an on-premise computing device operating in an on-premise environment, a cloud computing device operating in a billing cloud environment, and a job management device,
The job management device includes:
a reception unit that sequentially receives the first job, the second job, and the third job;
and a job execution management unit that, when sequentially executing the first job, the second job, and the third job in an order of acceptance by the acceptance unit, starts execution of the first job on the on-premises computing device, and, when neither the second job nor the third job is completed by a respective end deadline time that is predetermined to be executed after the other job is executed after the first job is completed, assigns the second job to the cloud computing device and assigns the third job to an on-premises computing device if an expected execution time of the second job is shorter than an expected execution time of the third job.

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