A Knowledge-Based Adaptive Discrete Water Wave Optimization for Solving Cloud Workflow Scheduling

Shuo Qin

doi:10.1109/TCC.2021.3087642

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A Knowledge-Based Adaptive Discrete Water Wave Optimization for Solving Cloud Workflow Scheduling

2023-01-01

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DOI：10.1109/TCC.2021.3087642

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Abstract

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Workflow scheduling in cloud environments has become a significant topic in both commercial and industrial applications. However, it is still an extraordinarily challenge to generate effective and economical scheduling schemes under the deadline constraint especially for the large scale workflow applications. To address the issue, this article investigates the cloud workflow scheduling problem with the aim of minimizing the whole cost of workflow execution whereas maintaining its execution time under a predetermined deadline. A novel knowledge-based adaptive discrete water wave optimization (KADWWO) algorithm is developed based on the problem-specific knowledge of cloud workflow scheduling. In the proposed KADWWO, a discrete propagation operator is designed based on the idle time knowledge of hourly-based cost model to adaptively explore the huge search space. The adaptive refraction operator is employed to avoid stagnation and expand the available resource pool. Meanwhile, the dynamic grouping based breaking operator is designed to exploit the excellent block structure knowledge of task allocation scheme and corresponding resource to intensify the local region and accelerate convergence. Extensive simulation experiments on the well-known scientific workflow demonstrate that the KADWWO approach outperforms several recent state-of-the-art algorithms.

Keywords:

Cloud computing

Scheduling

Processor scheduling

Optimal scheduling

Job shop scheduling

Task analysis

Statistics

Workflow scheduling

cloud computing

deadline constraint

cost minimization

water wave optimization

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