Efficient service deployment in mobile edge computing environment

被引：0

作者：

Lu J. ^{[1
]}

Li J. ^{[1
]}

Liu W. ^{[2
]}

Sun Q. ^{[1
]}

Zhou A. ^{[1
]}

机构：

[1] Institute of Network Technology, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing

[2] School of Software, Beijing University of Posts and Telecommunications, Beijing

来源：

Liu, Wei (liuw@bupt.edu.cn) | 1600年 / Inderscience Publishers卷 / 16期

关键词：

Cloudlet; Delay; Edge computing; Service deployment;

D O I：

10.1504/IJWGS.2020.107917

中图分类号：

学科分类号：

摘要：

Mobile applications' requirements for compute capability grow up daily. Providing mobile service in remote cloud is one of the solutions to this issue. However, due to the long geographical distance of clouds, it is difficult to ensure the claimed performance of real-time service. As a result, mobile edge computing has become the main solution to solve this problem. The present researches are concerned with cloudlet placement, and computation offloading, while the problem about how to deploy services on cloudlets based on the user's geographical distribution is overlooked. We address this issue in our paper. With the consideration of minimising the number of services and guaranteeing access delay constraint at the same time, this problem is formulated into an optimisation problem. For the NP-hardness of the problem, an efficient heuristic algorithm is proposed to resolve it. The simulation experiment at the end of this paper evaluates the performance of this algorithm. Experiment results demonstrate that the proposed heuristic algorithm is effective. Copyright © 2020 Inderscience Enterprises Ltd.

引用

页码：126 / 146

页数：20

共 24 条

[1]

Ascigil O., Phan T.K., Tasiopoulos A.G., Sourlas V., Psaras I., Pavlou G., On uncoordinated service placement in edge-clouds, IEEE International Conference on Cloud Computing Technology & Science, (2017)

[2]

Cao H., Cai J., Distributed multiuser computation offloading for cloudlet-based mobile cloud computing: A game-theoretic machine learning approach, IEEE Transactions on Vehicular Technology, 67, 1, pp. 752-764, (2018)

[3]

Crawford B., Soto R., Berrios N., Johnson F., Paredes F., Solving the set covering problem with binary cat swarm optimization, Advances in Swarm and Computational Intelligence, pp. 41-48, (2015)

[4]

Du J., Zhao L., Feng J., Chu X., Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee, IEEE Transactions on Communications, 66, 4, pp. 1594-1608, (2018)

[5]

Han D., Chen W., Bai B., Fang Y., Offloading optimization and bottleneck analysis for mobile cloud computing, IEEE Transactions on Communications, 67, 9, pp. 6153-6167, (2019)

[6]

Hong S., Kim H., QOE-aware computation offloading to capture energy-latency-pricing tradeoff in mobile clouds, IEEE Transactions on Mobile Computing, 18, 9, pp. 2174-2189, (2019)

[7]

Hong Y., Bai C., Xiong M., Zeng D., Fu Z., Heterogeneous cloudlet deployment and user-cloudlet association toward cost effective fog computing, Concurrency & Computation Practice & Experience, 29, 16, (2017)

[8]

Huang P., Wang Y., Wang K., Liu Z., A bilevel optimization approach for joint offloading decision and resource allocation in cooperative mobile edge computing, IEEE Transactions on Cybernetics, pp. 196-203, (2019)

[9]

Jia M., Cao J., Liang W., Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks, IEEE Transactions on Cloud Computing, 5, 4, pp. 725-737, (2017)

[10]

Ke Z., Mao Y., Leng S., Zhao Q., Li L., Xin P., Li P., Maharjan S., Yan Z., Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks, IEEE Access, 4, 99, pp. 5896-5907, (2017)

← 1 2 3 →