Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 50, Issue 6, Pages 2715-2729Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2933499
Keywords
Cloud computing; Task analysis; Optimization; Sociology; Statistics; Processor scheduling; Dynamic scheduling; Adaptive renumber strategy (ARS); dynamic group learning distributed particle swarm optimization (DGLDPSO); dynamic group learning strategy; large-scale cloud workflow scheduling; master-slave multigroup distributed
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Funding
- Outstanding Youth Science Foundation [61822602]
- National Natural Science Foundation of China [61772207, 61873097]
- Natural Science Foundation of Guangdong Province for Distinguished Young Scholars [2014A030306038]
- Guangdong Natural Science Foundation Research Team [2018B030312003]
- Guangdong-Hong Kong Joint Innovation Platform [2018B050502006]
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Cloud workflow scheduling is a significant topic in both commercial and industrial applications. However, the growing scale of workflow has made such a scheduling problem increasingly challenging. Many current algorithms often deal with small- or medium-scale problems (e.g., less than 1000 tasks) and face difficulties in providing satisfactory solutions when dealing with the large-scale problems, due to the curse of dimensionality. To this aim, this article proposes a dynamic group learning distributed particle swarm optimization (DGLDPSO) for large-scale optimization and extends it for the large-scale cloud workflow scheduling. DGLDPSO is efficient for large-scale optimization due to its following two advantages. First, the entire population is divided into many groups, and these groups are coevolved by using the master-slave multigroup distributed model, forming a distributed PSO (DPSO) to enhance the algorithm diversity. Second, a dynamic group learning (DGL) strategy is adopted for DPSO to balance diversity and convergence. When applied DGLDPSO into the large-scale cloud workflow scheduling, an adaptive renumber strategy (ARS) is further developed to make solutions relate to the resource characteristic and to make the searching behavior meaningful rather than aimless. Experiments are conducted on the large-scale benchmark functions set and the large-scale cloud workflow scheduling instances to further investigate the performance of DGLDPSO. The comparison results show that DGLDPSO is better than or at least comparable to other state-of-the-art large-scale optimization algorithms and workflow scheduling algorithms.
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