Research on Offloading Strategy for Mobile Edge Computing Based on Improved Grey Wolf Optimization Algorithm

With the development of intelligent transportation and rapid growth application data, tasks offloading vehicles in vehicle-to-vehicle communication technology are continuously increasing. To further improve service efficiency computing platform, energy-efficient low-latency mobile-edge-computing (MEC) methods urgently needed, which can solve insufficient capacity vehicle terminals. Based on an improved gray-wolf algorithm designed, adaptive joint strategy for vehicular edge is proposed, does not require cloud-computing support. This first establishes model, takes task delays, energy consumption, MEC server resources as constraints; secondly, a system-utility function designed to transform problem into constrained optimization problem; finally, optimal solution computation obtained based algorithm. The simulation results show that proposed effectively reduce system delay total consumption.