Journal
INFORMATION SCIENCES
Volume 583, Issue -, Pages 56-72Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2021.11.027
Keywords
Interval optimization; Interval many-objective optimization; Many-objective evolutionary algorithm; Cloud task scheduling
Categories
Funding
- National Key Research and Development Program of China [2018YFC1604000]
- National Natural Science Foundation of China [61806138, U1636220, 61961160707, 61976212, 61772478, 62166027]
- Key R&D program of Shanxi Province (International Cooperation) [201903D421048]
- Key R&D program of Shanxi Province (High Technology) [201903D121119]
- Natural Science Foundation of Jiangxi Province [20212ACB212004]
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This paper explores task scheduling in cloud computing and presents an interval many-objective optimization model and evolutionary algorithm, which consider uncertain factors while improving scheduling efficiency and performance.
Task scheduling is an important research direction in cloud computing. The current research on task scheduling considers mainly the design of scheduling strategies and algorithms and rarely gives attention to the influences of uncertain factors, such as the network bandwidth and millions of instructions per second (MIPS), on the scheduling process. The network bandwidth and MIPS directly affect the performance of a virtual machine (VM), which further influences the scheduling performance. In this paper, uncertain factors are transformed into interval parameters. The make-span, scheduling cost, load balance, and task completion rate are simultaneously considered in the scheduling process. Then, an interval many-objective cloud task scheduling optimization (I-MCTSO) model is designed to simulate real cloud computing task scheduling. To implement this model, an interval many-objective evolutionary algorithm (InMaOEA) is proposed. An interval credibility strategy is employed to improve the convergence performance. The hyper-volume and degree of overlap based on the interval congestion distance strategy are used to increase the population diversity. Simulation results demonstrate the effectiveness and superior performance of InMaOEA in comparision with other algorithms. The proposed approaches can provide decision-makers with an efficient allocation plan for cloud task scheduling. (c) 2021 Elsevier Inc. All rights reserved.
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