Research On Cloud Deployment Location Optimization Method In Edge Computing Environment

As the era rapidly advances,the emergence of the metaverse concept and the application of Web 3.0,coupled with the continuous increase in the types and numbers of user devices,will face immense data computing power.Therefore,traditional cloud computing is unable to meet the low-latency requirements of user devices,and the low computing power of mobile devices cannot handle large amounts of application data in a short time.Mobile edge computing is an important approach that can completely solve the above problems.It can sink various resources in cloud computing to the network edge,eliminating the need for computation through the central server,thereby reducing the overall computing cost and the resulting high latency.The reason it can solve these problems is that it has a reasonable cloud deployment and virtual machine layout.Through effective cloud deployment,user devices can efficiently communicate with the server.By selecting the appropriate virtual machine location,it can better solve constraints such as cost,latency,energy consumption,and coverage.Firstly,the problem of determining the cloud deployment location is studied.In response to the inefficiency of edge server layout,which can lead to high latency and a significantly imbalanced workload distribution across edge servers,this paper proposes a cloud deployment location selection method based on a dual-effect approximation algorithm(DEAA).By avoiding the problem of scattered edge users not being covered in specific areas,it solves constraints such as cost,latency,coverage,and coverage radius,thus adopting the standard of full coverage.Experimental results show that this method achieves lower cost and less latency.Secondly,the relevant issues of virtual machine location selection are studied.In response to the problems of network load and low device utilization,this paper proposes a virtual machine location selection method based on ant colony algorithm and sample average approximation method(AS).By further explaining the traveling salesman problem and proposing a sample mean approximation method based on ant colony algorithm,it improves the balanced load capacity of virtual machines and obtains a better solution.Experiments show that this method can achieve the goal of maximizing resource utilization and minimizing transmission costs.Finally,the proposed solutions are experimentally verified.For the cloud deployment location selection method based on the dual-effect approximation algorithm,the optimization performance of the server in specific conditions and areas is analyzed.The experimental results show that compared with the control algorithm,the proposed selection algorithm has lower cost and less latency.Regarding the problem of virtual machine location selection,the effectiveness of the virtual machine location selection method based on AS algorithm is verified,achieving the goal of maximizing resource utilization and minimizing transmission costs.