eprintid: 15360
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/53/60
datestamp: 2023-11-10 03:29:58
lastmod: 2023-11-10 03:29:58
status_changed: 2023-11-10 01:59:18
type: conference_item
metadata_visibility: show
creators_name: Daha, M.Y.
creators_name: Zahid, M.S.M.
creators_name: Alashhab, A.
creators_name: Ul Hassan, S.
title: Comparative Analysis of Community Detection Methods for Link Failure Recovery in Software Defined Networks
ispublished: pub
keywords: Computer system recovery; Failure (mechanical); Internet protocols; Population dynamics, Community detection; Community detection method; Comparative analyzes; Detection methods; Failure recovery; IP-network; Link failures; Network infrastructure; Network resource; Software-defined networks, Software defined networking
note: cited By 2; Conference of 2021 International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2021 ; Conference Date: 1 December 2021 Through 2 December 2021; Conference Code:176965
abstract: The complexity of IP networks leads toward the minimum utilization of network resources. To address this problem the concept of SDN (Software Defined Network) has been introduced. SDN is a revolutionary networking paradigm that overcomes the limits of standard IP networks while also modernizing network infrastructures. SDN makes the IP networks into programable networks and upgrade the network infrastructure. Like traditional IP networks, SDN technology can experience network failures. Several research papers have investigated this issue utilizing several methods. One technique in SDN is to employ community detection methods for link failure recovery. Although a variety of comparing analyses have been given across community detection approaches, however, they have not considered the special comparative analysis for link failure recovery situations in SDN. This paper presents a comparative analysis of the most likely used community detection methods based on the Dijkstra algorithm for link failure recovery in SDN. Extensive simulations are performed to evaluate the performance of the community detection methods. The simulation results depict that the Infomap and Louvain community detection methods perform better and have more modularity by 0.12 and less average end-to-end latency by 27, avg data packet loss by 0.8 than the Girvan and Newman community detection methods. © 2021 IEEE.
date: 2021
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126633937&doi=10.1109%2fICICyTA53712.2021.9689089&partnerID=40&md5=d5abfb92cfb757c9a06b1413499ef76c
id_number: 10.1109/ICICyTA53712.2021.9689089
full_text_status: none
publication: 2021 International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2021
pagerange: 157-162
refereed: TRUE
isbn: 9781665417778
citation:   Daha, M.Y. and Zahid, M.S.M. and Alashhab, A. and Ul Hassan, S.  (2021) Comparative Analysis of Community Detection Methods for Link Failure Recovery in Software Defined Networks.  In: UNSPECIFIED.