An Adaptive Task Offloading Framework for Mobile Edge Computing Environment: Towards Achieving Seamless Energy-Efficient Processing

With unprecedented growth in the use of mobile and small IoT devices, real-time and critical applications need a resource-rich computational environment. These small mobile devices have limited processing resources and battery life. The processing-intensive tasks can be offloaded to a resource-rich environment. Mobile Cloud Computing is the solution where the process-intensive task can be offloaded to the cloud server. Still, most of the real-time applications cannot afford high latency due to offloading to a remote cloud server. Mobile Edge Computing (MEC) has become one of the most suitable solutions where the processing is done at the nearby edge nodes to reduce the latency of real-time applications. However, Mobile Edge computing infrastructure supports processing a massive amount of information through offloading and on-demand access. The process-intensive or data-intensive tasks can be offloaded to a nearby miniature cloud server called an edge server in the MEC environment. Thus, with the help of MEC, mobile devices can perform more processing with less power consumption and minimized latency. This research study aims to minimize energy consumption through the optimal offloading decision and enhance the mobile device’s application execution performance and battery life while performing processor and data-intensive tasks.