Cloud computing resource scheduling and a survey of its evolutionary approaches

Zhi Hui Zhan, Xiao Fang Liu, Yue Jiao Gong, Jun Zhang, Henry Shu Hung Chung, Yun Li

Research output: Contribution to journalArticle

232 Citations (Scopus)

Abstract

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon.

Original languageEnglish
Article number63
Number of pages33
JournalACM Computing Surveys
Volume47
Issue number4
DOIs
Publication statusPublished - 1 Jul 2015

Keywords

  • ant colony optimization
  • cloud computing
  • evolutionary computation
  • genetic algorithm
  • particle swarm optimization
  • resource scheduling

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    Zhan, Z. H., Liu, X. F., Gong, Y. J., Zhang, J., Chung, H. S. H., & Li, Y. (2015). Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Surveys, 47(4), [63]. https://doi.org/10.1145/2788397