Cloud computing resource scheduling and a survey of its evolutionary approaches

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

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Li, Professor Yun
• Authors: Zhan, Z.-H., Liu, X.-F., Gong, Y.-J., Zhang, J., Chung, H. S.-H., and Li, Y.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: ACM Computing Surveys
• Publisher: ACM Press
• ISSN: 0360-0300
• ISSN (Online): 1557-7341
• Copyright Holders: Copyright © 2015 The Authors
• First Published: First published in ACM Computing Surveys 47(4):63
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher