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ISTC-CC Abstract
Uncertainty-Aware Real-Time Workflow Scheduling in the Cloud
Proceedings of the IEEE International Conference on Cloud Computing (IEEE Cloud 2016). June 27 - July 2, 2016, San Francisco, USA.
Huangke Chen, Xiaomin Zhu, Dishan Qiu, Ling Liu*
National University of Defense Technology Changsha, China
*Georgia Institute of Technology
Scheduling real-time workflows running in the Cloud often need to deal with uncertain task execution times and minimize uncertainty propagation during the workflow runtime. Efficient scheduling approaches can minimize the operational cost of Cloud providers and provide higher guarantee of the quality of services (QoSs) for Cloud consumers. However, most of the existing workflow scheduling approaches is designed for the individual workflow runtime environments that are deterministic. Such static workflow schedulers are inadequate for multiple and dynamic workflows, each with possibly uncertain task execution times. In this paper, we address the problem of minimizing uncertainty propagation in real-time workflow scheduling. We first introduce an uncertainty-aware scheduling architecture to mitigate the impact of uncertainty factors on the quality of workflow schedules. Then we present a dynamic workflow scheduling algorithm (PRS) that can dynamically exploit proactive and reactive scheduling methods. Finally, we conduct extensive experiments using real-world workflow traces and our experimental results show that PRS outperforms two representative scheduling algorithms in terms of costs (up to 60%), resource utilization (up to 40%) and deviation (up to 70%).
FULL PAPER: pdf
