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Computation offloading and resource allocation for mobile edge computing with multiple access points

Computation offloading and resource allocation for mobile edge computing with multiple access points

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Mobile edge computing (MEC) is an innovative computing paradigm to enhance the computing capacity of mobile devices (MDs) by offloading computation-intensive tasks to MEC servers. With the widespread deployment of wireless local area networks, each MD can offload computation task to server via multiple wireless access points (WAPs). However, computation offloading can bring a higher system cost if all users select the same access points to offload their tasks. This study proposes a computation offloading strategy and resource allocation optimisation scheme in a multiple wireless access points network with MEC, which aims to minimise the system cost by providing the optimal computation offloading strategy, transmission power allocation, bandwidth assignment, and computation resource scheduling. The proposed scheme decouples the optimisation problem into subproblems of offloading strategy and resource allocation since the problem is NP-hard. The offloading strategy involves the optimal access point selection, which is analysed by the potential game. The resource allocation is obtained using Lagrange multiplier. The authors' analysis and simulation results verify the convergence performance of the proposed scheme, and the proposed scheme outperforms the simple resource allocation scheme and the offloading strategy optimisation scheme in terms of the system cost.

References

    1. 1)
      • 1. Mao, Y., Zhang, J.B., Letaief, K.: ‘Dynamic computation offloading for mobile-edge computing with energy harvesting devices’, IEEE J. Sel. Areas Commun., 2016, 34, (12), pp. 35903605.
    2. 2)
      • 2. Zhao, J., Liu, Y., Gong, Y., et al: ‘A dual-link soft handover scheme for c/u plane split network in high-speed railway’, IEEE Access, 2018, 6, pp. 1247312482.
    3. 3)
      • 3. Abbas, N., Zhang, Y., Taherkordi, A., et al: ‘Mobile edge computing: a survey’, IEEE Internet Things J., 2018, 5, (1), pp. 450465.
    4. 4)
      • 4. Zhang, J., Xia, W., Zhang, Y., et al: ‘Joint offloading and resource allocation optimization for mobile edge computing’. Proc. IEEE Global Commun. Conf. (GLOBECOM), Singapore, December 2017, pp. 16.
    5. 5)
      • 5. Mao, Y., You, C., Zhang, J., et al: ‘A survey on mobile edge computing: the communication perspective’, IEEE Commun. Surv. Tuts., 2017, 19, (4), pp. 23222358.
    6. 6)
      • 6. Lu, W., Gong, Y., Liu, X., et al: ‘Collaborative energy and information transfer in green wireless sensor networks for smart cities’, IEEE Trans. Ind. Inf., 2018, 14, (4), pp. 15851593.
    7. 7)
      • 7. Asghari, S., Navimipour, N.J.: ‘Nature inspired meta-heuristic algorithms for solving the service composition problem in the cloud environments’, Int. J. Commun. systs., 2018, 31, (12), p. e3708.
    8. 8)
      • 8. Baker, T., Al-Dawsari, B., Tawfik, H., et al: ‘GreeDi: an energy efficient routing algorithm for big data on cloud’, Ad Hoc Netw., 2015, 35, pp. 8396.
    9. 9)
      • 9. Naseri, A., Navimipour, N.J.: ‘A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm’, J. Ambient Intell. Humaniz. Comput., 2019, 10, (5), pp. 18511864.
    10. 10)
      • 10. Baker, T., Ngoko, Y., Tolosana-Calasanz, R., et al: ‘Energy efficient cloud computing environment via autonomic meta-director framework’. Proc. IEEE Int. Conf. Developments in eSystems Engineering (DeSE), Abu Dhabi, UAE, December 2013, pp. 198203.
    11. 11)
      • 11. Sheikholeslami, F., Navimipour, N.J.: ‘Auction-based resource allocation mechanisms in the cloud environments: A review of the literature and reflection on future challenges’, Concurrency Computat. Pract. Exper., 2018, 30, (16), p. e4456.
    12. 12)
      • 12. Vakili, A., Navimipour, N.J.: ‘Comprehensive and systematic review of the service composition mechanisms in the cloud environments’, J. Netw. Comput. Appl., 2017, 81, pp. 2436.
    13. 13)
      • 13. Azad, P., Navimipour, N.J.: ‘An energy-aware task scheduling in the cloud computing using a hybrid cultural and ant colony optimization algorithm’, Int. J. Cloud Appl. Comput. (IJCAC), 2017, 7, (4), pp. 2040.
    14. 14)
      • 14. Al-khafajiy, M., Baker, T., Waraich, A., et al: ‘Iot-fog optimal workload via fog offloading’. Proc. IEEE/ACM Int. Conf. on Utility and Cloud Computing Companion (UCC Companion), Zurich, Switzerland, December 2018, pp. 359364.
    15. 15)
      • 15. Li, Q., Zhao, J., Gong, Y., et al: ‘Energy-efficient computation offloading and resource allocation in fog computing for internet of everything’, China Commun., 2019, 16, (3), pp. 3241.
    16. 16)
      • 16. Al-khafajiy, M., Baker, T., Al-Libawy, H., et al: ‘Fog computing framework for internet of things applications’. Proc. IEEE Proc. IEEE Int. Conf. Developments in eSystems Engineering (DeSE), Cambridge, UK, September 2018, pp. 7177.
    17. 17)
      • 17. Al-khafajiy, M., Baker, T., Al-Libawy, H., et al: ‘Improving fog computing performance via fog-2-fog collaboration’, Future Gener. Comput. Syst., 2019, 100, pp. 266280.
    18. 18)
      • 18. Tao, X., Ota, K., Dong, M., et al: ‘Performance guaranteed computation offloading for mobile-edge cloud computing’, IEEE Wirel. Commun. Lett., 2017, 6, (6), pp. 774777.
    19. 19)
      • 19. You, C., Huang, K., Chae, H., et al: ‘Energy-efficient resource allocation for mobile-edge computation offloading’, IEEE Trans. Wirel. Commun., 2017, 16, (3), pp. 13971411.
    20. 20)
      • 20. Chen, X., Jiao, L., Li, W., et al: ‘Efficient multi-user computation offloading for mobile-edge cloud computing’, IEEE/ACM Trans. Netw., 2016, 24, (5), pp. 27952808.
    21. 21)
      • 21. Feng, J., Zhao, L., Du, J., et al: ‘Computation offloading and resource allocation in D2D-enabled mobile edge computing’. Proc. IEEE Int. Conf. Commun. (ICC), Kansas City, MO, July 2018, pp. 16.
    22. 22)
      • 22. Ma, X., Zhang, S., Li, W., et al: ‘Cost-efficient workload scheduling in cloud assisted mobile edge computing’. Proc. IEEE/ACM 25th Int. Symp. Quality of Service (IWQoS), Vilanova i la Geltru, Spain, June 2017, pp. 110.
    23. 23)
      • 23. Chen, M.-H., Liang, B., Dong, M.: ‘Joint offloading and resource allocation for computation and communication in mobile cloud with computing access point’. Proc. IEEE Int. Conf. Comput. Commun. (INFOCOM), Atlanta, GA, USA, May 2017, pp. 19.
    24. 24)
      • 24. Du, J., Zhao, L., Feng, J., et al: ‘Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee’, IEEE Trans. Commun., 2018, 66, (4), pp. 15941608.
    25. 25)
      • 25. Vu, T.T., Van Huynh, N., Hoang, D.T., et al: ‘Offloading energy efficiency with delay constraint for cooperative mobile edge computing networks’. Proc. IEEE Global Commun. Conf. (GLOBECOM), Abu Dhabi, United Arab Emirates, December 2018, pp. 16.
    26. 26)
      • 26. Ma, X., Lin, C., Xiang, X., et al: ‘Game-theoretic analysis of computation offloading for cloudlet-based mobile cloud computing’. Proc. ACM Int. Conf. Modeling, Analysis, and Simulation of Wireless and Mobile Systems, Cancun, Mexico, November 2015, pp. 271278.
    27. 27)
      • 27. Zhang, J., Xia, W., Cheng, Z., et al: ‘An evolutionary game for joint wireless and cloud resource allocation in mobile edge computing’. Proc. 9th Int. Conf. Wireless Commun. Signal Processing (WSCP), Nanjing, China, October 2017, pp. 16.
    28. 28)
      • 28. Wang, Y., Sheng, M., Wang, X., et al: ‘Mobile-edge computing: partial computation offloading using dynamic voltage scaling’, IEEE Trans. Commun., 2016, 64, (10), pp. 42684282.
    29. 29)
      • 29. Zhang, K., Mao, Y., Leng, S., et al: ‘Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks’, IEEE Access, 2016, 4, pp. 58965907.
    30. 30)
      • 30. Zhao, J., Yang, T., Gong, Y., et al: ‘Power control algorithm of cognitive radio based on non-cooperative game theory’, China Commun., 2013, 10, (11), pp. 143154.
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