Leveraging Computational Reuse for Cost- and QoS-Efficient Task Scheduling in Clouds
- 1.4k Downloads
Cloud-based computing systems could get oversubscribed due to budget constraints of cloud users which causes violation of Quality of Experience (QoE) metrics such as tasks’ deadlines. We investigate an approach to achieve robustness against uncertain task arrival and oversubscription through smart reuse of computation while similar tasks are waiting for execution. Our motivation in this study is a cloud-based video streaming engine that processes video streaming tasks in an on-demand manner. We propose a mechanism to identify various types of “mergeable” tasks and determine when it is appropriate to aggregate tasks without affecting QoS of other tasks. Experiment shows that our mechanism can improve robustness of the system and also saves the overall time of using cloud services by more than 14%.
KeywordsTask aggregation Oversubscription Cloud computing Video stream processing Task scheduling
This research was supported by the Louisiana Board of Regents under grant number LEQSF(2016-19)-RD-A-25.
- 3.Darwich, M., Beyazit, E., Salehi, M.A., Bayoumi, M.: Cost efficient repository management for cloud-based on-demand video streaming. In: Proceedings of the 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, pp. 39–44, April 2017Google Scholar
- 4.Hosseini, M., Salehi, M.A., Gottumukkala, R.: Enabling interactive video stream prioritization for public safety monitoring through effective batch scheduling. In: Proceedings of the 19th IEEE International Conference on High Performance Computing and Communications, HPCC 2017, December 2017Google Scholar
- 5.Li, X., Salehi, M.A., Bayoumi, M., Buyya, R.: CVSS: a cost-efficient and QoS-aware video streaming using cloud services. In: Proceedings of the 16th IEEE/ACM International Conference on Cluster Cloud and Grid Computing, CCGrid 2016, pp. 106–115, May 2016Google Scholar
- 8.Popa, L., Budiu, M., Yu, Y., Isard, M.: DryadInc: reusing work in large-scale computations. In: Proceedings of 1st USENIX workshop on Hot Topics in Cloud Computing, HotCloud 2009, June 2009Google Scholar