Energy Minimization And Offloading Number Maximization In Wireless Mobile Edge Computing

With the development of the information age,the emergence of 5G technology makes the communication rate leap forward and the time delay become lower and lower.At present,the computing capacity of computing devices is also steadily improving.As one of its key technologies,mobile edge computing is becoming more and more important.With the rapid development of Mobile Edge Computing,user devices(UE)can enjoy a better user experience than before by offloading their tasks to a nearby Edge cloud(MEC).This paper mainly analyses the task scheduling policy between UE and MEC in the context of mobile edge computing.This paper first analyzes and points out the shortcomings of the existing mobile edge computing network models,and proposes a new network model for mobile edge computing scenarios.We combine multiple edge clouds and multiple users,and enable devices to have the right to choose edge cloud to make up for the deficiency in the traditional network model.In addition,we conduct an indepth analysis of the new mobile edge computing network model and explain how edge computing works under the new network model.At the same time,the objective to be optimized under the model and the characteristics of the working mode of the system are analyzed in depth.The physical problem and mathematical problem are proposed from the aspects of energy consumption and the number of successful offloaded tasks.This paper starts from the physical model,analyzes the characteristics of the model,optimizes the traditional ant colony algorithm(ACO)according to these characteristics,combines load balancing with ant colony algorithm,and proposes a load balancing ant colony algorithm to solve the problem.In this way,the ant colony algorithm,which only has the ability to select the shortest path,has added the capacity of load balancing,which can effectively carry out task scheduling to prevent the excessive load of a certain edge cloud and at the same time make the overall energy consumption as small as possible.And then combining with the physical model and mathematical model,the problem has carried on the strict formula derivation and transformation.Eventually we convert the problem into minimum cost maximum flow problem(MCMF)by transforming the physical model and the parameters of the original physical model.To solve the original problem by solving the MCMF problem,greatly reduce the difficulty of solving problem.Finally,the advantages of the two algorithms are proved by simulation.