Research Article
Mobile Edge Server Placement Based on Bionic Swarm Intelligent Optimization Algorithm
@INPROCEEDINGS{10.1007/978-3-030-67540-0_6, author={Feiyan Guo and Bing Tang and Linyao Kang and Li Zhang}, title={Mobile Edge Server Placement Based on Bionic Swarm Intelligent Optimization Algorithm}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={Mobile edge computing Edge server placement Artificial firefly algorithm Performance optimization}, doi={10.1007/978-3-030-67540-0_6} }- Feiyan Guo
Bing Tang
Linyao Kang
Li Zhang
Year: 2021
Mobile Edge Server Placement Based on Bionic Swarm Intelligent Optimization Algorithm
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_6
Abstract
By offloading computing tasks from mobile devices to edge servers with sufficient computing resources, network congestion and data propagation delays can be effectively reduced. The placement of edge servers is the core of task offloading and is a multi-objective optimization problem with multiple resource constraints. An optimization model of edge server placement has been established in this paper by minimizing both access delay and workload difference as the optimization goal. Then, based on Glowworm Swarm algorithm, it proposes a mobile edge server placement approach called GSOESP to achieve a multi-objective optimization goal. In this study, we use the improved Glowworm Swarm Optimization (GSO) algorithm to find the optimal places as the clustering center which is the edge server placement address, and every base station in edge server’s neighbor list is allocated to the edge server. After many iterations, we gradually approach the optimal target. So, the optimal placement scheme is obtained to achieve the goals of minimizing the distance for users to access the edge server and balancing the workload. The GSOESP algorithm is similar to a fast clustering algorithm with good time performance. Experimental results using Shanghai Telecom’s real dataset show that the proposed approach achieves an optimal balance between low latency and workload balancing, while guaranteeing service quality, which outperforms several existing representative approaches.
