Scheduling Real-Time Parallel Applications in Cloud to Minimize Energy Consumption

Cloud computing has become an important paradigm in which scalable resources such as CPU, memory, disk and IO devices can be provided to users to remotely process their applications. In a cloud computing platform, energy consumption accounts for a significant cost portion. This article thus aims to present an energy-efficient scheduling algorithm for processing a user application with a real-time requirement. This problem is formulated as a non-linear mixed integer programming problem. We start with providing an optimal closed-form solution to its relaxation problem that aims to minimize the energy consumption without considering real-time requirements. To meet real-time requirements, we propose how to adjust task placement and resource allocation by making a good tradeoff between energy consumption and task execution time. Lastly, we find two equivalent optimal resource allocation strategies once task placement has been done. We then propose to adjust the start time of task execution such that an application's completion time can be further shortened. Experimental results on two real-case enchmarks and extensive synthetic applications demonstrate that our proposed method finds a schedule that generally has 30 and 20 percent less energy consumption than enhancement heterogeneous earliest finish time (E-HEFT) and genetic algorithm, respectively. Besides, the proposed method has a higher rate to successfully find a feasible schedule than them, and its computation time is close to E-HEFT's, but far less than the genetic algorithm's.

Automated summary

This article presents an energy-efficient scheduling algorithm to address the cost issue caused by energy consumption in cloud computing platforms. The algorithm optimizes the tradeoff between energy consumption and task execution time to meet real-time requirements.