Dispatching method and system for computing resources and dispatching center

Abstract

The invention discloses a dispatching system for computing resources. The system comprises supercomputing centers which are applicable to operation of work submitted by clients; a performance computing center which is applicable to computation of work operation performance feature values according to work operation performance parameters and generation of a work performance table, wherein the work performance table is applicable to performance of associated storage on work identifiers, work types, identifiers of the supercomputing centers for performing the work, identifiers of clusters for performing the work, work computing scales, computing node information and the work operation performance feature values, as a work operation performance record; the clients which are applicable to response of requests of submitting the work by users and transmission of submission instructions to the dispatching center, wherein the submission instructions comprise the work types and the computing scales; and the dispatching center which is applicable to matching of at least one record from the work performance table according to the work types of the submitted work and submission of the work to the computing nodes contained by the at least one record. The invention also discloses the corresponding dispatching center and dispatching method.

Description

Technical field [0001] The invention relates to the technical field of cloud computing, in particular to a scheduling method, a scheduling center and a scheduling system of computing resources. Background technique [0002] In recent years, with the gradual development and maturity of high-performance computer technology, the demand for high-performance computing in scientific research, production, education and other fields has grown rapidly, and more and more users are using supercomputer resources to submit assignments. In this case, how to allocate reasonably Cloud computing resources have become a key issue. [0003] At present, the more common scheduling algorithms are based on private computing resources. According to some common hardware resource configurations such as the number of idle nodes, cores, memory, and CPU models of existing resources, users can choose resources by themselves when submitting jobs. However, most of the user groups only pay attention to the runnin...

AI Technical Summary

Problems solved by technology

However, most user groups only pay attention to the running performance and results of jobs. In order to quickly obtain job calculation results, multi-core resources are selected when submitting jobs, which leads to waste of resources, unreasonable configuration of job submission parameters, and failure of job submission (running), etc. Adverse consequences

On the other hand, when users choose computing resources by themselves, they need to have supercomputing knowledge, and the learning cost is too high

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