Abstract
With the development of Mobile Edge Computing (MEC), it has become a key technology to realize the vision of the Internet of Things. In MEC, users can upload tasks to edge nodes for faster processing speed and lower local energy consumption. However, as the mobility of users and the limited resources of the edge nodes, some edge nodes cannot provide high-quality services. In this case, we study service migration strategy in the MEC system to migrate services from the initial nodes to other edge nodes that can provide services to meet the needs of users. By making service migration decision and allocating computation resource, our work minimizes the delay and the energy consumption caused by finishing tasks. Specifically, we set up an efficient service migration model and formulate the service migration problem as a non-linear 0-1 programming problem. To solve this problem, we design a Particle Swarm based Service Migration scheme (PSSM) which includes Queuing Delay Prediction algorithm (QDP), Delay-aware Computation Resource Allocation algorithm (DCRA), and Modified Quantum Particle Swarm algorithm (MQPS). For evaluating the performance of the proposed PSSM, we conduct simulation in a practical scenario. The results demonstrate that our scheme not only can effectively reduce delay and energy consumption, but also improve the processing capability of servers.
Highlights
Mobile Cloud Computing (MCC), as the integration of cloud computing and mobile computing, has been widely used in the past few years
We design Queuing Delay Prediction algorithm (QDP) to predict the queuing delay of tasks and design Delay-aware Computation Resource Allocation algorithm (DCRA) to allocate computation resource dynamically to improve the efficiency of servers
In this figure, ‘‘U-before migration’’ represents the delay of uLLRC tasks before migration; ‘‘U-Particle Swarm based Service Migration scheme (PSSM)’’ represents the delay of Ultra Reliable and Low Latency Communication (uRLLC) tasks migrated by using PSSM; ‘‘U-Quantum Particle Swarm algorithm (QPS)’’ represents the delay of uRLLC tasks migrated by using QPS; ‘‘U-GA’’ represents the delay of uRLLC tasks migrated by using GA
Summary
Mobile Cloud Computing (MCC), as the integration of cloud computing and mobile computing, has been widely used in the past few years. MEC scenario deploys a part of resource (such as storage resource and computing resource) to the edge of the network to provide service to users [2], [3], so that the data generated by users would not need to be processed in the data center In this way, the congestion of the core network can be alleviated, and the response time of users can be reduced significantly. We assume the user status, network status and edge server status are collected in real time, so that the migration model can VOLUME 8, 2020 effectively reflect the real situation On this basis, we design QDP to predict the queuing delay of tasks and design DCRA to allocate computation resource dynamically to improve the efficiency of servers.
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