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Home > Archive>Volume 29, Issue 11, 2018 >3306-3325. DOI:10.13328/j.cnki.jos.005479
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Multi-Objective Workflow Scheduling Based on Delay Transmission in Mobile Cloud Computing
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National Key Research and Development Program of China (2016YFC0800803); National Natural Science Foundation of China (61802095, 61802167, 61572162, 61572251, 61702144); Zhejiang Provincial National Science Foundation (LQ17F020003); Zhejiang Provincial Key Science and Technology Project Foundation (2018C01012); Fundamental Research Funds for the Central Universities

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    Abstract:

    The integration between cloud computing and mobile Internet promotes the development of mobile cloud computing. The tasks of workflow can be migrated to cloud that can not only improve the computing capacity of mobile device, but also reduce the energy consumption of battery. However, a great amount of data transmission introduced by using unreasonable tasks scheduling strategies can damage the QoS (quality of service) of workflow and increase the energy consumption of mobile device. In this paper, a multi-objective workflow scheduling is proposed based on delay transmission mechanism (MOWS-DTM) to optimize execution time of workflow and energy consumption of mobile device in mobile cloud computing environment. MOWS-DTM, derived from genetic algorithm, is combined with the process of workflow scheduling and takes both task scheduling location and execution sequence into consideration in coding strategy. When mobile user is moving, wireless network signal of mobile device is changing with the pace of different location. The stronger the network signal, the less time it takes to transmit data with fixed size, and the less energy the mobile device will consume. Moreover, there are many non-critical tasks reside in workflow, and increasing their execution time will not affect the makespan of workflow. Therefore, the delay transmission mechanism (DTM), incorporated in the process of workflow scheduling, can optimize the energy consumption of mobile device and the makespan of workflow simultaneously. Simulation results demonstrate significant multi-objective performance improvement of MOWS-DTM over the MOHEFT algorithm and RANDOM algorithm.

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周业茂,李忠金,葛季栋,李传艺,周筱羽,骆斌.移动云计算中基于延时传输的多目标工作流调度.软件学报,2018,29(11):3306-3325

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History
  • Received:July 20,2017
  • Revised:September 16,2017
  • Adopted:November 14,2017
  • Online: December 05,2017
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