ISSN 1000-1239 CN 11-1777/TP

### Resource-Delay-Aware Scheduling for Real-Time Tasks in Clouds

Chen Huangke, Zhu Jianghan, Zhu Xiaomin, Ma Manhao, Zhang Zhenshi

1. (Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073)
• Online:2017-02-01

Abstract: Green cloud computing has become a central issue, and dynamical consolidation of virtual machines (VMs) and turning off the idle hosts show promising ways to reduce the energy consumption for cloud data centers. When the workload of the cloud platform increases rapidly, more hosts will be started on and more VMs will be deployed to provide more available resources. However, the time overheads of turning on hosts and starting VMs will delay the start time of tasks, which may violate the deadlines of real-time tasks. To address this issue, three novel startup-time-aware policies are developed to mitigate the impact of machine startup time on timing requirements of real-time tasks. Based on the startup-time-aware policies, we propose an algorithm called STARS to schedule real-time tasks and resources, such making a good trade-off between the schedulibility of real-time tasks and energy saving. Lastly, we conduct simulation experiments to compare STARS with two existing algorithms in the context of Google's workload trace, and the experimental results show that STARS outperforms those algorithms with respect to guarantee ratio, energy saving and resource utilization.

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