Delay-Energy Aware Task Offloading and VM Migration Policy for Mobile Edge Computing

Due to the rise of mobile users and their demand for various applications, the traditional cloud computing remote servers consume huge energy and execution time because it is far away from the mobile devices. The Mobile edge computing is a technology which offloads the workloads of mobile user device to the edge of the network using Mobile Edge Computing thereby reducing the burden of cloud and reducing the latency and energy consumption. However, offloading of the complex tasks face challenges like increased delay and energy consumption. To solve these challenges, proposed approach designs a Delay-Energy aware Task Offloading Algorithm (DETOA) where the tasks gets partitioned based on the delay, energy and communication constraints. The proposed task partitioning based approach executes the tasks locally, if the MUD can process the task else the tasks are offloaded to MEC and processed remotely. In addition, a VM migration scheme is introduced to prevent service degradation through task and VM replication approach. The proposed approach exhibited better performance in terms of energy, delay and QoS.