Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks

Jun Wu, Mianxiong Dong*, Kaoru Ota, Jianhua Li, Zhitao Guan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

162 Citations (Scopus)

Abstract

In software-defined networks (SDNs), the abstracted control plane is its symbolic characteristic, whose core component is the software-based controller. The control plane is logically centralized, but the controllers can be physically distributed and composed of multiple nodes. To meet the service management requirements of large-scale network scenarios, the control plane is usually implemented in the form of distributed controller clusters. Cluster management technology monitors all types of events and must maintain a consistent global network status, which usually leads to big data in SDNs. Simultaneously, the cluster security is an open issue because of the programmable and dynamic features of SDNs. To address the above challenges, this paper proposes a big data analysis-based secure cluster management architecture for the optimized control plane. A security authentication scheme is proposed for cluster management. Moreover, we propose an ant colony optimization approach that enables big data analysis scheme and the implementation system that optimizes the control plane. Simulations and comparisons show the feasibility and efficiency of the proposed scheme. The proposed scheme is significant in improving the security and efficiency SDN control plane.

Original languageEnglish
Pages (from-to)27-38
Number of pages12
JournalIEEE Transactions on Network and Service Management
Volume15
Issue number1
DOIs
Publication statusPublished - 2018 Mar
Externally publishedYes

Keywords

  • Software-defined networks
  • big data
  • cluster management
  • security
  • swarm computing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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