Multi-target task scheduling method and system under cloud computing system

Abstract

The invention discloses a multi-target task scheduling method and system under a cloud computing system. The multi-target task scheduling method comprises the steps: taking the minimization of the maximum completion time, the minimization of the maximum equipment workload and the minimization of the total workload of all equipment as targets, and constructing task scheduling under the cloud computing system into a mixed workshop scheduling model; solving the hybrid workshop scheduling model by adopting a hybrid discrete artificial bee colony algorithm embedded with a disturbance structure to obtain a scheduling optimization scheme; and scheduling the tasks under the cloud computing system by utilizing the obtained scheduling optimization scheme. A hybrid discrete artificial bee colony algorithm is adopted, and the flexible task scheduling problem under a cloud computing system is optimized, and an HFS model is modeled; eight disturbance structures are embedded, so that the developmentcapability of the algorithm is enhanced; the adaptive disturbance structure balances the development and exploration capabilities, and the improved following bee mechanism has a deep mining function,so that the local search capability can be further enhanced; and the convergence capability of the algorithm can be improved by designing a good reconnaissance bee algorithm.

Description

technical field

[0001] The present disclosure relates to the technical field of multi-objective optimization, in particular to a multi-objective task scheduling method and system under a cloud computing system. Background technique

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] In a cloud computing system, jobs proposed by users should be assigned to capable devices, and usually each job consists of several consecutive tasks, which should be processed in a certain order on different or the same devices. The whole process can be modeled as a Hybrid Flow Shop Scheduling (HFS) problem. Task scheduling in cloud systems has been studied in recent years, such as Wang et al. developed a multidisciplinary approach to artificial swarm intelligence for heterogeneous computing and cloud scheduling. However, published literature mainly discusses task allocation in clo...

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