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

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.