Research Of Offloading Approach Of Computation Task In Application Of Mobile Edge Computing

Due to the powerful computing capabilities of cloud service centers,cloud computing has become a research direction that has attracted much attention in the past few decades.The applications and services based on cloud computing technology also greatly improve people's lives.However,due to the development of the Internet of Things and 5G technology,hundreds of millions of edge devices will be deployed at the edge of the network.Massive edge devices will generate enormous computing demands.If cloud computing technology is adopted for these computing requirements,it will cause network overload and high transmission delay.To understand this type of problem,Mobile Edge Computing(MEC)technology emerged.Mobile edge computing refers to the provision of an IT service environment and cloud computing capabilities at the edge of the mobile network,emphasizing proximity to mobile users to reduce network operation and service delays,thereby improving user experience.Mobile edge computing technology can effectively use the computing and storage capabilities of edge devices to meet the needs of edge devices for efficient computing,low latency,and low energy consumption.The most concerning technical indicators in mobile edge computing are latency and energy consumption,and computing offloading in mobile edge computing is a key technology that affects user experience.Therefore,mobile edge computing offloading technology has aroused the research enthusiasm of many scholars at home and abroad.Therefore,this paper mainly studies the computational offloading decision-making problem in mobile edge computing and optimizes the time delay and energy consumption in offloading.With the development of 5G,computationally intensive and complex applications in smart cities have grown rapidly.The resources in mobile terminal devices in smart cities are limited,and new applications have higher requirements for the delay,bandwidth,security,and energy consumption.As a technology to reduce latency and energy consumption in mobile edge computing(MEC),computing offloading can effectively solve the high latency and high energy consumption problems of real-time video analysis in smart cities.Aiming at the offloading scenario of multi-user and multiMEC edge computing,this paper proposes an improved chaotic quantum particle swarm optimization algorithm to jointly optimize time delay and energy consumption.By comparing with other heuristic algorithms,the improved chaotic quantum particle swarm algorithm proposed in this paper can effectively reduce the delay and energy consumption of edge computing offloading.Experimental results show that the improved Chaotic Quantum Particle Swarm Optimization(ICQPSO)can effectively avoid premature convergence,has stronger global search capabilities,and can more effectively solve multi-dimensional and complex NP-complete problems.With the popularization of smart devices,the number of edge devices in mobile edge computing will greatly increase.Therefore,it is difficult to meet the requirements of all terminal devices for efficient computing and low latency only by relying on the computing power of the edge server.To meet the computing needs of end-users in the community,this paper proposes a computing offloading task scheduling algorithm based on the adaptive covariance matrix evolution algorithm(Covariance Matrix Adaptation Evolution Strategy,CMAES)for the three-tier mobile edge computing architecture.To meet the computing needs of all users as much as possible,this paper uses the Short Job First(SJF)task calculation method in the edge server and cloud server and simultaneously optimizes the time delay and energy consumption of offloading tasks.The experimental results show that,compared with other heuristic algorithms,the algorithm proposed in this paper has better performance in computing offloading of the three-layer mobile edge computing architecture.