Evolutionary Optimization of Energy Consumption and Makespan of Workflow Execution in Clouds

Lining Xing, Jun Li (), Zhaoquan Cai and Feng Hou
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Lining Xing: School of Mathematics and Big Data, Foshan University, Foshan 528225, China
Jun Li: School of Management, Hunan Institute of Engineering, Xiangtan 411104, China
Zhaoquan Cai: Shanwei Institute of Technology, Shanwei 516600, China
Feng Hou: School of Mathematical and Computational Sciences, Massey University, Palmerston North 4442, New Zealand

Mathematics, 2023, vol. 11, issue 9, 1-18

Abstract: Making sound trade-offs between the energy consumption and the makespan of workflow execution in cloud platforms remains a significant but challenging issue. So far, some works balance workflows’ energy consumption and makespan by adopting multi-objective evolutionary algorithms, but they often regard this as a black-box problem, resulting in the low efficiency of the evolutionary search. To compensate for the shortcomings of existing works, this paper mathematically formulates the cloud workflow scheduling for an infrastructure-as-a-service (IaaS) platform as a multi-objective optimization problem. Then, this paper tailors a knowledge-driven energy- and makespan-aware workflow scheduling algorithm, namely EMWSA. Specifically, a critical task adjustment-based local search strategy is proposed to intelligently adjust some critical tasks to the same resource of their successor tasks, striving to simultaneously reduce workflows’ energy consumption and makespan. Further, an idle gap reuse strategy is proposed to search the optimal energy consumption of each non-critical task without affecting the operation of other tasks, so as to further reduce energy consumption. Finally, in the context of real-world workflows and cloud platforms, we carry out comparative experiments to verify the superiority of the proposed EMWSA by significantly outperforming 4 representative baselines on 19 out of 20 workflow instances.

Keywords: mathematical model; cloud computing; workflow scheduling; evolutionary algorithm; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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