| 5G technology plays a leading role in the transformation of the information age.Communication technologies are changing with each passing day,network bandwidth is constantly improving,smart devices are rapidly spreading,and the total amount of information data is growing rapidly.All this changes not only brings many opportunities,but also devote challenges to the development of the information industry.The information society generates a large amount of data all the time.How to make good use of these massive data,optimize the computing resource allocation strategy while realizing the high availability of cloud computing center,and provide users with high speed and low latency service has become the research hotspot and difficulty for researchers and engineers.Mobile edge computing is an important part of 5G technology.It is not only a hot topic of theoretical research,but also widely used in application fields.The main idea of mobile edge computing is to sink some functions of cloud computing centers to base stations,base stations cluster deployment of edge cloud,shorten the distance between services and users,achieve faster user demand response,and timely handle all kinds of tasks submitted by users.This paper studies resource allocation strategies from three aspects:single base station multi-user,single base station multi-user and multi-base station multi-user.The specific work is as follows:(1)In order to improve the resource utilization and user experience of large cloud computing centers in the mobile edge computing(MEC)environment,for a single base station,a dynamic computing resource and spectrum resource allocation algorithm(KDSAA)based on the K-means algorithm is proposed,which mainly analyzes the current situation of the traditional average resource allocation method and virtual machine allocation method,studies the comprehensive needs of users,simulates the resources as "fluid",uses the auction algorithm to allocate,and linearly solves the maximum edge cloud throughput and transmission delay.Simulation results show that the proposed algorithm can effectively improve the edge cloud throughput and reduce the transmission delay.(2)5G base station has the characteristics of high speed,low delay and large connection,but the coverage range of a single base station is small and the power consumption is large,so it needs the cooperation of multiple base stations to provide efficient resource services for users.In this case,the paper puts forward multiple base stations based on particle swarm optimization(PSO)algorithm single-user reverse resource allocation strategy,by multiple stations cloud cluster deployment edge,edge cloud allowance and user tasks demand according to each base station resources,make the base station "agent" matching task by itself,balance the number of base station call,as far as possible to deal with more tasks,Improve the resource utilization rate and user experience of edge cloud,balance the load of base station,and relieve the network pressure.Experimental results show the effectiveness of this strategy.(3)In the multi-base station and multi-user environment,a resource scheduling strategy based on multi-objective priority particle swarm optimization(MPPSO)was proposed by considering the user's physical location,user size,data transmission rate,task energy consumption,task priority and edge base station performance.The strategy design for two fitness function and a method of particle codec introduced Pareto control mechanism,in multi-user concurrent multi-tasking,assist search multi-objective optimal solution priority strategy,to provide the optimal resource scheduling strategy for edge cloud,real-time resources need to satisfy different users different tasks,more fully use the edge of cloud resources up,at the same time,improve the user experience.Experimental results show the effectiveness of this strategy.In summary,the three mobile edge computing strategies designed in this paper provide new ideas and directions for the promotion and application of 5G communication technology.In various cases,they can achieve reasonable utilization of base station resources,improve user experience,balance network load,and meet the application requirements of mobile edge computing,such as high rate and low delay. |
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