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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.

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