Research On Service Selection Mechanism Of Energy Consumption And Delay Awareness In Mobile Edge Computing Environment

With the spread of mobile devices and the growth of application demand,mobile edge computing has become one of the indispensable technologies of our time.It reduces data latency by performing tasks at the edge of the network and creates a better service environment for mobile users.However,because edge computing is close to mobile users to provide services,the number and computing power of edge servers are limited,and serious competition for shared resources will occur between mobile applications.At the same time,service requests uploaded by mobile users are becoming more and more complex and diversified.How to choose a suitable service so as to minimize the interaction delay of mobile users and the energy consumption of mobile devices is the main challenge for mobile users.Therefore,service selection in the mobile edge computing environment is a classic NP-hard problem.Based on the above situation,this paper proposes a hybrid algorithm(HGAPSO),which combines genetic algorithm and particle swarm optimization algorithm for service selection in mobile edge computing system to optimize interaction delay and energy consumption.Specifically,the main contributions of this article are as follows:(1)This paper models the service selection problem in the mobile edge computing environment as a multi-objective optimization problem,with the purpose of optimizing the interaction time between the user and the server and the energy consumption of mobile devices.(2)This paper combines the local search function in PSO with the global search advantage of GA,and map each task in the service request to the corresponding type of candidate service instance sequence under the predetermined user movement path.(3)This paper conducts simulation experiments to verify the effectiveness of the proposed HGAPSO.The simulation results show that compared with other optimization algorithms,the HGAPSO algorithm can provide better service selection solutions with lower interaction delay and lower energy consumption.