Journal of Xidian University ›› 2019, Vol. 46 ›› Issue (1): 130-136.doi: 10.19665/j.issn1001-2400.2019.01.021

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Enhanced multi-objective evolutionary algorithm for workflow scheduling on the cloud platform

WANG Yan1,2   

  1. 1. School of Computer Science and Technology, Xi’an Univ. of Posts and Telecommunications, Xi’an 710121, China;
    2. Shaanxi Key Lab. of Network Data Analysis and Intelligent Processing, Xi’an Univ. Of Posts and Telecommunications, Xi’an 710121, China;
  • Received:2018-06-14 Online:2019-02-20 Published:2019-03-05


The complex and dynamic pricing mechanism raises big challenges to the workflow scheduling on the cloud platform. Considering the prices of the virtualized computing and storage resources, a multi-objective optimization model is developed for the workflow running on a cloud platform. Based on the character of the target problem, a real-coding mechanism is developed for the workflow scheduling problem, so that the crossover operators in a real-coded evolutionary based optimizer can be conveniently employed and the solution repairing step in combinatorial optimization algorithms can be skipped. Following the algorithm framework of the MOEA/D, a local search strategy is designed, and a new multi-objective workflow scheduling algorithm is proposed. Experimental studies have illustrated that the proposed algorithm can obtain Pareto optimal solution sets with better coverage and uniformity than the compared algorithms, which will contribute to improving the utilization of the resources on the cloud platform.

Key words: workflow scheduling, cloud computing, evolutionary multi-objective optimization algorithm, local search

CLC Number: 

  • TP301.6