Journal of Xidian University ›› 2021, Vol. 48 ›› Issue (6): 32-39.doi: 10.19665/j.issn1001-2400.2021.06.005

• Special Issue:Key Technology of Architecture and Software for Intelligent Embedded Systems • Previous Articles     Next Articles

Optimization of task scheduling oriented to cross microservice chains

ZHANG Yupeng(),WU Zili(),CHEN Ming(),ZHANG Lulu()   

  1. School of Computer Science and Technology,Xidian University,Xi’an 710071,China
  • Received:2021-08-20 Online:2021-12-20 Published:2022-02-24
  • Contact: Zili WU E-mail:zhangyp@xidian.edu.cn;zlwu@xidian.edu.cn;19031211681@stu.xidian.edu.cn;906657317@qq.com

Abstract:

The microservice architecture arranges an application as a set of loosely-coupled fine-grained services,with each microservice independently deployed and updated.The cooperation of services leads to multiple intersecting microservice chains.And the intersection of microservices becomes a key position for resource competition.Therefore,rational allocation of microservices can improve resource utilization,reduce the task response time and solve the problem of resource competition caused by the intersection of microservice chains.However,existing research often ignores or simplifies the conflict problem caused by the intersection of microservice chains,resulting in poor system scheduling.Therefore,aiming at the above problem,this paper takes the resource utilization and the global response time as the measurement indicators to formally characterize the resource consumption of services and the task execution time in the microservice architecture.Combined with the advantages of parallel computing of the ant colony algorithm and local perturbations of simulated annealing algorithm,this paper proposes a chain-oriented task scheduling algorithm (COTSA).Experimental results show that compared with first come first service (FCFS) and ant colony optimization (ACO),the COTSA can effectively improve resource utilization and reduce the overall response time in the complex microservice environment.

Key words: microservice chains, multiobjective optimization, scheduling algorithms, resource utilization

CLC Number: 

  • TP301