Research Output

FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks

  Mobile cloud computing has emerged as a promising paradigm to facilitate computation-intensive and delay-sensitive mobile applications. Computation offloading services at the edge mobile cloud environment are provided by small-scale cloud infrastructures such as cloudlets. While offloading tasks to in-proximity cloudlets enjoys benefits of lower latency and smaller energy consumption, new issues related to the cloudlets are rising. For instance, unbalanced task distribution and huge load gaps among heterogeneous mobile cloudlets are becoming challenging with respect to network dynamics and distributed task offloading. In this paper, we propose 'FairEdge', a Fairness-oriented computation offloading scheme to enable balanced task distribution for mobile Edge cloudlet networks. By integrating the balls-and-bins theory with fairness index, our solution promotes effective load balancing with limited information at low computation cost. The evaluation results from extensive simulations and experiments with real-world datasets show that FairEdge outperforms conventional task offloading methods, it can achieve a network fairness up to 0.85 and reduce the unbalanced task offload by 50%.

  • Type:

    Article

  • Date:

    10 January 2020

  • Publication Status:

    Published

  • Publisher

    Institute of Electrical and Electronics Engineers (IEEE)

  • DOI:

    10.1109/access.2020.2965562

  • Funders:

    Edinburgh Napier Funded

Citation

Lai, S., Fan, X., Ye, Q., Tan, Z., Zhang, Y., He, X., & Nanda, P. (2020). FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks. IEEE Access, 8, 13516-13526. https://doi.org/10.1109/access.2020.2965562

Authors

Keywords

Mobile Cloudlets; Load balancing; Edge Computing; Fair Task Offloading

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