Research On Unloading Strategy Of Mobile Edge Network Computing

The mobile edge network utilizes sufficient computing and storage resources of the edge server to effectively reduce the task delay and computing energy consumption of the smart mobile device,which is the key to solving the contradiction between the limited resource requirements of mobile intelligent terminals and the increasing high performance requirements.Under the 5th generation mobile communication(5th Generation,5G)architecture,the mobile edge network needs to solve the problem of computing offloading and reducing system latency energy consumption under a single server and the joint allocation of communication and resources under multiple servers.This paper focuses on the problem of system computing offloading under single server and the allocation of system resources under multiple servers in mobile edge networks.The main contents of the research are as follows:Based on the existing literature,this paper studies the basic flow of mobile edge computation and unloading,discusses the far and near effects of mobile edge computing,and simulates the effects of near and far effects on system delay and energy consumption.The simulation results of the system show that users farther away from the base station spend more time and energy to calculate the unloading costs,while those nearer to the base station spend less time and energy to calculate the unloading costs..The user's near-far effect affects the delay and energy consumption of the calculation of the unloading and the success rate of the task unloading.Aiming at the far-reaching effect mentioned above,this paper constructs a computational offloading scenario in which a device-to-device(D2D)device acts as a relay under a single server and multiple users.In this scenario,the user farther from the base station selects the D2 D device to act as a relay within the communicable range,and helps the user to forward the calculation and offload task.The signal energy to the base station is guaranteed to be the same at different distances from the base station,by which can overcome the near-far effect between users.In the scenario where the joint D2 D device acts as a relay,this paper proposes a computational offload algorithm based on the task maximum delay constraint.The algorithm classifies the mobile edge device into local computing,partially unloads and completely calculates the unloading,and uploads it to the Mobile Edge Computing(MEC)server for computational unloading to reduce system energy consumption.The algorithm simulation results show that the algorithm can effectively reduce the system energy consumption because it classifies the mobile intelligent device and assigns it according to the priority when the resource is allocated.Compared with the direct calculation of the unloading and local computing,the algorithm can effectively reduce the system energy consumption.Under the premise of ensuring the energy consumption of the system in a single server multi-user scenario,this paper proposes a resource allocation strategy based on communication cooperation for the problem of resource scheduling in multi-server and multi-user.When the load of a MEC server is overheated,search for an available MEC server in the collaboration framework,put the server into the resource pool,find the server with the shortest task execution delay from the resource pool,and assign the execution task to shorten the system's total task execution time.The simulation results of the system algorithm show that the algorithm can effectively reduce the overall delay of the system when the server resources are insufficient,and can fully guarantee the success rate of the system tasks.