Abstract
AbstractThe software-defined networks-enable mobile edge computing (SDN-enable MEC) architecture, which integrates SDN and MEC technologies, realizes the flexibility and dynamic management of the underlying network resources by the MEC, reduces the distance between the access terminal and computing resources and network resources, and increases the terminal's access to resources. However, the static distribution relationship between MEC servers (MECSs) and controllers in the multi-controller architecture may result in unbalanced load distribution among the controllers, which would degrade network performance. In this paper, a multi-objective optimization MECS redistribution algorithm (MOSRA) is proposed to decrease the response time and overhead. A controller response time model and link transmit overhead model are introduced as basis of an evolutionary algorithm which is proposed to optimize MECS redistribution. The proposed algorithm aims to select an available sub-optimizes individual by using a strategy based coordination transformation from Pareto Front. That is, when the master controller of the MECS is redistributed, both of the network overhead of the MECS to the controller and the response time of the controller to the MECS processing request are optimized. Finally, the simulation results demonstrate that the MOSRA can solve the redistribution problem in different network load levels and different network sizes within the effective time, and has a lower control plane response time, while making the edge network plane transmission overhead lower, compared with other algorithms
.
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Reference30 articles.
1. Gu, X. H., Jin, L., Zhao, N., & Zhang, G. A. (2019). Energy-efficient computation offloading and transmit power allocation scheme for mobile edge Computing. Mobile Information Systems, 2019, 9.
2. Mavromatis, A., Colman-Meixner, C., Silva, A. P., Vasilakos, X., Nejabati, R., & Simeonidou, D. (2020). A software-defined IoT device management framework for edge and cloud computing. IEEE Internet of Things Journal, 7(3), 1718–1735.
3. Krishnan, P., Duttagupta, S., & Achuthan, K. (2019). SDNFV based threat monitoring and security framework for multi-access edge computing infrastructure. Mobile Networks & Applications, 24(6), 1896–1923.
4. Yan, Q., Yu, F. R., Gong, Q., & Li, J. (2016). Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: A survey some research issues, and challenges. IEEE Communications Surveys & Tutorials, 18(1), 602–622. https://doi.org/10.1109/COMST.2015.2487361.
5. Linthicum, D., As cloud use grows so will rate of DDoS attacks, Feb. 2013, [online] Available: http://www.infoworld.com/d/cloud-computing/cloud-use-grows-so-will-rate-of-ddos-attacks-211876.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献