Issue No. 06 - June (2018 vol. 29)
Jie Zhu , School of Computer ScienceJiangsu Key Laboratory of Big Data Security & Intelligent Processing
Xiaoping Li , School of Computer Science and EngineeringKey Laboratory of Computer Network and Information Integration
Ruben Ruiz , Grupo de Sistemas de Optimización Aplicada, Instituto Tecnológico de Informática, Ciudad Politécnica de la Innovación, Edifico 8G, Acc. B. Universitat Politčcnica de Valčncia, Camino de Vera s/n, Valčncia, Spain
Xiaolong Xu , School of Computer Science, Nanjing University of Posts & Telecommunications, Yancheng, China
We consider a special workflow scheduling problem in a hybrid-cloud-based workflow management system in which tasks are linearly dependent, compute-intensive, stochastic, deadline-constrained and executed on elastic and distributed cloud resources. This kind of problems closely resemble many real-time and workflow-based applications. Three optimization objectives are explored: number, usage time and utilization of rented VMs. An iterated heuristic framework is presented to schedule jobs event by event which mainly consists of job collecting and event scheduling. Two job collecting strategies are proposed and two timetabling methods are developed. The proposed methods are calibrated through detailed designs of experiments and sound statistical techniques. With the calibrated components and parameters, the proposed algorithm is compared to existing methods for related problems. Experimental results show that the proposal is robust and effective for the problems under study.
Cloud computing, Dynamic scheduling, Processor scheduling, Heuristic algorithms, Genetic algorithms
J. Zhu, X. Li, R. Ruiz and X. Xu, "Scheduling Stochastic Multi-Stage Jobs to Elastic Hybrid Cloud Resources," in IEEE Transactions on Parallel & Distributed Systems, vol. 29, no. 6, pp. 1401-1415, 2018.