Mobile Edge Computing Task Migration Algorithm Based on Vehicle Network

Mobile edge computing (MEC) is an important technology that can improve the speed and security of mobile computing. This paper proposes two task migration algorithms for mobile edge computing, integer linear programming (ILP) and greedy heuristic algorithm. Both methods aim to minimize migration energy consumption. The ILP method sets relevant optimization constraints based on the minimum energy cost to solve the migration problem. The greedy heuristic algorithm is based on the Dijkstra algorithm, which converts the migration energy consumption into path weights, forms a weighted undirected graph, and uses the shortest path method to solve the migration energy consumption minimization problem. In this paper, the Markov chain is used to predict vehicle positions, integrating position prediction and task transfer. Experimental results show that ILP consumes nearly the same energy as the greedy heuristic. However, under the same energy consumption, the time delay of the greedy heuristic algorithm is much smaller than that of the ILP algorithm, thus establishing the advantage of the greedy algorithm in the migration scheme. Finally, based on the selection of task refresh frequency, the energy consumption of the greedy heuristic algorithm is further reduced.