Research Area: Cloud Computing
In the past few years, more and more business-to-consumer and enterprise applications run in the heterogeneous clouds. Such cloud bag-of-tasks applications are usually budget constrained, and their scheduling is an essential problem for cloud provider. The problem is even more complex and challenging when the accurate knowledge about task execution time is unknown in advance. Focusing on these challenges, we first build a cloud resource management architecture and stochastic task model, which divides cloud task into two execution parts. Then, we deduce bag-of-tasks applications schedule length (Makespan) and total cost according to heterogeneous clouds online feedback information of task first part execution. Thirdly, we formulate this stochastic scheduling problem as a linear programming problem. Lastly, we propose a time and cost multiobjective stochastic task scheduling genetic algorithm, in which can find Pareto optimal schedules for stochastic cloud task that meet its budget constraint. The extensive simulation experiments were carried out on a heterogeneous cloud platform with 400 virtual machines, and tasks were derived from Parallel Workloads Archive and the analysis data of real-world cloud systems. The experimental results show that our proposed stochastic task scheduling genetic algorithm can get shorter schedule length and lower cost with task budget constraints.
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Author(s) Name: Xiaoyong Tang, Xiaochun Li, Zhuojun Fu
Journal name: Concurrency and Computation: Practice and Experience
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Publisher name: Wiley
DOI: 10.1002/cpe.4210
Volume Information: Volume29, Issue19
