ClusteringBased Algorithm for Services Deployment in Mobile Edge Computing Environment In the edge computing, the service is deployed to the edge server through virtualization technology. Most strategies of service deployment are singleness and ignore the diversity of usersu0027 requirements and the service deployment cost at the edge. This paper proposes a clusteringbased algorithm for service deployment, which considers the delay at the user side and the edgeside services deployment cost, and establishes a service deployment model based on multiobjective integer linear programming. Firstly, the Kmeans clustering algorithm is optimized to solve the problem of hotspot migration in the process of service deployment and reduce the deployment cost at the edge server side, then alternative enhanced heuristic algorithm is proposed to find the approximate optimal solution of services deployment. Experiments show that the algorithm can reasonably deploy services. Compared with traditional heuristic algorithms, the algorithm proposed in this paper has better performance in terms of user side and edge server side.

Redundant Virtual Machine Placement in Mobile Edge Computing Mobile edge computing (MEC), as an extension of the cloud computing paradigm to edge networks, overcomes some obstacles of traditional mobile cloud computing by offering ultrashort latency and less core network traffic. The edge resources are virtualized to be shared among multiple mobile users. Consolidated server systems using server virtualization involves serious risks of hosting server failures. Redundant virtual machine placement can be an effective countermeasure. This problem has been well solved in cloud computing. However, it is a more nontrivial task to efficiently deploy virtual machines redundantly in MEC networks. To address this issue, a redundant virtual machine placement method in MEC is proposed. Firstly, this method estimates the minimum number of virtual machines required based on the performance requirements of the applications. Then, the redundant virtual machine placement problem is formulated as a combinatorial optimization formulation. Finally, a optimal placement strategy searching algorithm is proposed to determine the optimal location of the virtual machines. The strategy can preserve the minimum configuration in the event of failure of any k edge servers. The experiment results illustrates the effectiveness of our algorithm.