mca 19(16): e4

Research Article

Mobility and Fault Aware Adaptive Task Offloading in Heterogeneous Mobile Cloud Environments

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  • @ARTICLE{10.4108/eai.3-9-2019.159947,
        author={Abdullah  Lakhan and Xiaoping Li},
        title={Mobility and Fault Aware Adaptive Task Offloading in Heterogeneous Mobile Cloud Environments},
        journal={EAI Endorsed Transactions on Mobile Communications and Applications},
        volume={5},
        number={16},
        publisher={EAI},
        journal_a={MCA},
        year={2019},
        month={1},
        keywords={Task Offloading, Software Defined Network (SDN), MATOA, Mobility, DHEFT, Edge server (cloudlet), DC (data center), Workflow Task Scheduling},
        doi={10.4108/eai.3-9-2019.159947}
    }
    
  • Abdullah Lakhan
    Xiaoping Li
    Year: 2019
    Mobility and Fault Aware Adaptive Task Offloading in Heterogeneous Mobile Cloud Environments
    MCA
    EAI
    DOI: 10.4108/eai.3-9-2019.159947
Abdullah Lakhan1, Xiaoping Li1,*
  • 1: School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, and Ministry of Education, Southeast University, Nanjing 211189, China
*Contact email: xpli@seu.edu.cn

Abstract

Nowadays, Mobile Cloud Computing (MCC) has become a predominant prototype for fetching the benefits of cloud computing to mobile devices’ propinquity. Service availability in addition to performance enhancement and mobility features is a preliminary goal in MCC. This paper proposes a mobility aware adaptive offloading framework, known as Mob-Cloud, which includes a mobile device as a thick client, ad-hoc networking, cloudlet DC, and remote cloud services, to augment the performance and availability of the MCC services. However, the impact of dynamic changes in a mobile content (e.g., network status, bandwidth, latency, and location) for the task offloading model observes through proposing a mobility aware adaptive task offloading algorithm (MATOA), which makes a task offloading decision at runtime on selecting optimal wireless network channels and suitable resources for offloading. In this paper, we are formulating the decision problem, and it is well-known as an NP-hard problem. Nonetheless, MATOA has the following phases for the entire Mob-Cloud model: (i) adaptive offloading decision based on real-time information, (ii) workflow task scheduling phase, (iii) mobility model phase to motivate end-user invoke cloud services seamlessly while roaming, and (iv) faulttolerant phase to deal with failure (either network or node). We carry out actual real-life experiments at the implemented instruments to evaluate the overall performance of the MATOA algorithm. Evaluation results prove that MATOA adopts dynamic changes on offloading decision during run-time, and meet an enormous reduction in the total response time with the improved service availability whilst in comparison with the baseline task offloading strategies.