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Volume 40 Issue 7
Jul.  2018
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ZHANG Long, CAO Bin. Stochastic Programming and Buyer-seller Game Methods for Workload Distribution in an Ad-hoc Mobile Cloud[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1731-1737. doi: 10.11999/JEIT170895
Citation: ZHANG Long, CAO Bin. Stochastic Programming and Buyer-seller Game Methods for Workload Distribution in an Ad-hoc Mobile Cloud[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1731-1737. doi: 10.11999/JEIT170895

Stochastic Programming and Buyer-seller Game Methods for Workload Distribution in an Ad-hoc Mobile Cloud

doi: 10.11999/JEIT170895
Funds:

The National Natural Science Foundation of China (61701059), The Science and Technology Research Project of Chongqing Municipal Education Commission of China (KJ1500406, KJ1500408), The Doctoral Fund of Chongqing University of Posts and Telecommunications (A2014-92), The Research and Innovation Project of Graduated Students in Chongqing (CYS17218)

  • Received Date: 2017-09-22
  • Rev Recd Date: 2018-02-23
  • Publish Date: 2018-07-19
  • In order to solve the limitation of processing capacity and energy of single mobile equipment, the conception of Ad-hoc mobile cloud is proposed recently, in which a mobile device can use the idle resources at other neighboring devices for processing data and storage in Ad-hoc manner. To this end, this paper designs a workload distribution for offloading among mobile equipment. Considering the random and intermittent connections between mobile equipment caused by the movement in wireless network, a stochastic programming method is adopted to take posterior recourse actions to compensate for inaccurate predictions. Moreover, in order to motivate the available mobile equipment for offloading while maximizing their utilities, a distributed multi-stage Stochastic buyer/seller Game for Workload Distribution (SGWD) is formulated. Numerical results show the effectiveness of SGWD compared with the benchmark method in terms of communication cost, the delay, energy consumption and the payoff.

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  • [2] MACH P and BECVAR Z. Mobile edge computing: A survey on architecture and computation offloading[J]. IEEE Communications Surveys & Tutorials, 2017, 19(3): 1628-1656. doi: 10.1109/COMST.2017.2682318.
    ROST P, BERNARDOS C, and DOMENICO A. Cloud technologies for flexible 5G radio access networks[J]. IEEE Communications Magazine, 2014, 52(5): 68-76. doi: 10.1109/ MCOM.2014.6898939.
    [3] BHARDWAJ S, JAIN L, and JAIN S. Cloud computing: A study of infrastructure as a service (IAAS)[J]. International Journal of Information Technology & Web Engineering, 2010, 2(1): 60-63.
    [4] JARARWEH Y, TAWALBEH L, ABABNEH F, et al. Resource efficient mobile computing using cloudlet infrastructure[C]. IEEE Ninth International Conference on Mobile Ad-hoc and Sensor Networks (MSN), Dalian, 2013: 373-377. doi: 10.1109/MSN.2013.75.
    [5] CHEN W, LEA C T, and LI K. Dynamic resource allocation in Ad-hoc mobile cloud computing[C]. IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, USA, 2017: 1-6. doi: 10.1109/WCNC.2017. 7925613.
    [6] SHILA D M, SHEN W, CHENG Y, et al. AMCloud: Toward a secure autonomic mobile Ad hoc cloud computing system[J]. IEEE Wireless Communications, 2017, 24(2): 74-81. doi: 10.1109/MWC.2016.1500119RP.
    [7] ZAGHDOUDI B, AYED H K B, and GNICHI I. A protocol for setting up Ad hoc mobile clouds over spontaneous MANETs: A proof of concept[C]. Cloudification of the Internet of Things (CIoT), Paris, France, 2016: 1-6. doi: 10.1109/CIOT.2016.7872919.
    [8] KHAN R and KHAN S U. Design and implementation of Ad-hoc collaborative proxying scheme for reducing network energy waste[J]. Digital Communications and Networks, 2017, 3(2): 118-128. doi: 10.1016/j.dcan.2016.11.001.
    [9] DUAN Q. Modeling and performance analysis for composite networkCompute service provisioning in software-defined cloud environments[J]. Digital Communications and Networks, 2015, 1(3): 181-190. doi: 10.1016/j.dcan.2015.05. 003.
    [10] TRUONG-HUU T, THAM C K, and NIYATO D. To offload or to wait: An opportunistic offloading algorithm for parallel tasks in a mobile cloud[C]. IEEE CloudCom, Singapore, 2014: 182-189. doi: 10.1109/CloudCom.2014.33.
    [11] FERNANDO N, LOKE S W, and RAHAYA W. Dynamic mobile cloud computing: Ad hoc and opportunistic job sharing[C]. IEEE International Conference on Utility and Cloud Computing (UCC), Victoria, NSW, 2011: 281-286. doi: 10.1109/UCC.2011.45.
    [12] TRUONG-HUU T, THAM C K, and NIYATO D. A stochastic workload distribution approach for an Ad-hoc mobile cloud[C]. IEEE CloudCom 2014, Singapore, 2014: 174-181. doi: 10.1109/CloudCom.2014.32.
    [13] GRAND M and BOYD S. CVX: Matlab software for disciplined convex programming[EB/OL]. version 1.21. Global Optimization, 2008: 155-210.
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