Low-cost Task Offloading Scheme for Mobile Edge Cloud and Internet Cloud Using Genetic Algorithm

Offloading a task for a user is required when it can not perform the computation locally. Hence, Mobile Edge Cloud (MEC) can reduce the burden of edge users, as it brought computational capacity near to the edge users. Although MEC has resource constraints hence few users may have to offload to the cloud server also. Thus, choosing MEC or cloud for offloading is crucial as users have latency and energy constraints. We address the challenges of offloading and formulated our optimization problem. Due to the NP-Hardness of the problem, we proposed a meta-heuristic Genetic algorithm called Cost Effective Genetic Algorithm for Tasks Offloading (CEGA). In this approach, MEC will analyze the user’s delay and energy state and it will divide users who should offload to MEC or the cloud. Based on the proposed (CEGA) method we evaluate the performance and observed significant improvement in user delay and energy reduction and overall cost for offloading compared to existing state-of-the-art works.