Mobility-Aware Workload Distribution and Task Allocation for Mobile Edge Computing Networks

Mobile Edge Computing (MEC) is a promising computing paradigm that provides computing and storage services for mobile and big data applications. MEC servers are deployed at base stations to establish a mobile edge network (MEN) where mobile users can offload tasks to nearby servers to speed up their mobile applications. However, challenges such as the quality of workload distribution in edge computing environments still need to be tackled. Most studies and offloading strategies assume mobile users are stationary, but in reality, users move and this affects workload distribution and response time of mobile applications. In this work, we propose a solution that addresses these challenges by reducing energy consumption and enhancing QoS by assigning mobile tasks to MEC servers in the users’ predicted trajectories using a Random waypoint Model. We propose OptiMEC for scheduling mobile tasks in a Mobile Edge Computing (MEC) environment in 5G networks that utilize central-base stations. We consider the task properties, user mobility, and delay constraints in our proposed algorithmic scheme and we also propose an energy-load balancing heuristic. The problem is formulated as an optimization and constraint satisfaction problem and we propose a near-optimal solution for scheduling mobile tasks to MEC servers. The results of our simulation experiments show that our proposed solution can significantly reduce energy consumption in Mobile Edge Networks (MENs) and improve QoS by executing tasks under constrained time.