Research On Computation Offloading And Base Station Energy Saving Strategies Based On MEC

With the continuous development of Internet of Things(Io T)technology and mobile intelligent terminals,various computationally intensive and delay sensitive applications are growing explosively,posing higher requirements for network latency,bandwidth,and power consumption and other indicators.Traditional processing methods have become more and more difficult to meet the needs of users,and mobile edge computing(MEC)has gradually attracted people’s attention.With the advantage of being close to mobile terminals,mobile edge computing can allocate computing,network and cache resources to users,which greatly reduces user delay and energy consumption,but also increases the energy consumption of edge systems.As a key technology of mobile edge computing,resource allocation usually involves key issues such as computation offloading,cache management,resource joint optimization,and task deployment.This thesis mainly studies the problem of computation offloading and base station energy-saving in mobile edge computing scenarios,and proposes a mobile base station edge computing offloading strategy based on fast response IMGA algorithm and a mobile base station edge energy-saving scheduling strategy based on BPGA algorithm.The main work of this thesis is as follows:In the computation offloading scenario of MEC,aiming at maximizing the user’s integrated satisfaction(UIS)of resource allocation,a base station edge computation offloading strategy is proposed.Firstly,a user mobility model,an edge cache strategy,an offloading decision model,and a user’s integration satisfaction model are established based on the characteristics of user requests.Next,designing a resource allocation plan that comprehensively considers the resource conditions of local,edge,and cloud data centers.Then,with the aim of maximizing the value of UIS,the offloading decision is constructed as an optimization problem.Finally,the Improved Metropolis Genetic Algorithm(IMGA)that introduces the metropolis criterion is presented in detail,and the optimal result of resource allocation is obtained after solving the problem.In order to facilitate the solution,the IMGA transforms the offloading decision and resource allocation problems under continuous time into optimization problems under continuous time slices,and dynamically updates the size of edge resources under each time slice through changes in user location and resource occupation time.Through simulation and comparison with other strategies,it is proved that this computation offloading strategy has certain advantages.In the energy-saving scenario of the MEC system,while reducing the energy consumption of the edge system,it meets the user’s demand for resource allocation,a base station edge energy-saving strategy is proposed to address the energy consumption problem that operators are concerned about.The specific algorithm is a combination of genetic algorithm and backtracking pruning operations,so it is named Backtracking Pruning Genetic Algorithm(BPGA).The entire base station edge energysaving strategy divides the entire resource allocation process into two parts: base station sleep and offloading decision.Firstly,a backtracking algorithm is used to solve various base station sleep situations,and followed by solving the offloading decision and resource allocation problems for each scenario.In order to reduce the time complexity of the algorithm,a pruning method is added in the backtracking process.If the value of UIS in the base station sleep situation is too small,the deployment of the base station will be abandoned.In the determined base station sleep situation,with the aim of minimizing the energy consumption of the edge system,the offloading decision and resource allocation are made in combination with user mobility,edge cache,system energy consumption,and user request models.Finally,the optimal base station sleep scheme and the corresponding system energy consumption are obtained through the BPGA.The superiority of this strategy was verified by comparing simulation results with other strategies.