Information processing device and job scheduling method

A job scheduling method, comprising:

• when a job is executed by one or more nodes in a system, receiving by a computer, designation of a number of nodes to be used by an application related to execution of the job, abnormality occurrence probability of the one or more nodes in the system, a ratio of processing time of an abnormal node to processing time of a normal node in the system, and benchmark time involved in executing a benchmark that is executed in the job prior to the application;
• creating a performance model that outputs an expected value of resource consumption amount involved in executing the job, from an expected value of execution time involved in executing the job, the number of nodes to be used, and a first number of spare nodes for the job, based on the received designation;
• and
• determining a second number of the spare nodes that minimizes the expected value of the resource consumption amount using the created performance model.

An information processing device, comprising:

• a memory;
• and
• a processor coupled to the memory and the processor configured to:
• when a job is executed by one or more nodes in a system, receive designation of a number of nodes to be used by an application related to execution of the job, abnormality occurrence probability of the one or more nodes in the system, a ratio of processing time of an abnormal node to processing time of a normal node in the system, and benchmark time involved in executing a benchmark that is executed in the job prior to the application;
• create a performance model that outputs an expected value of resource consumption amount involved in executing the job, from an expected value of execution time involved in executing the job, the number of nodes to be used, and a first number of spare nodes for the job, based on the received designation;
• and
• determine a second number of the spare nodes that minimizes the expected value of the resource consumption amount using the created performance model.