Fault Tolerance Management Implementation from Medium-to-Large-Scale Networks

As network infrastructures grow in complexity, ensuring high availability and resilience becomes critical, especially for medium-to-large scale networks. This study focuses on the development and implementation of fault tolerance management within Software-defined networking (SDN) environments, aimed at minimizing downtime and enhancing network reliability. SDN’s centralized control and dynamic programmability provide an ideal framework for implementing efficient fault detection and recovery mechanisms. The proposed model leverages real-time monitoring, redundancy protocols, and adaptive rerouting strategies to mitigate the impact of node or link failures. Key components of the model include failover mechanisms, load balancing, and traffic rerouting algorithms, designed to maintain seamless network operations during failures. Through simulation and testing, the model demonstrates significant improvements in network resilience, reducing recovery time and ensuring uninterrupted service delivery. This research provides a comprehensive guide to implementing fault-tolerant networks using SDN, offering scalable solutions that can be adapted to various network sizes and configurations. The findings emphasize the potential of SDN to revolutionize fault management in modern network infrastructures, making it a crucial consideration for future network design and operations.

Institutions

Publication Details

Journal
Indonesian Journal of Computer Science
Published
2024-12-03
DOI
https://doi.org/10.33022/ijcs.v13i6.4417
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fault Tolerance Management Implementation from Medium-to-Large-Scale Networks

Indonesian Journal of Computer Science
Software-Defined Networks and 5G
article

Fault Tolerance Management Implementation from Medium-to-Large-Scale Networks

article en

Abstract

As network infrastructures grow in complexity, ensuring high availability and resilience becomes critical, especially for medium-to-large scale networks. This study focuses on the development and implementation of fault tolerance management within Software-defined networking (SDN) environments, aimed at minimizing downtime and enhancing network reliability. SDN’s centralized control and dynamic programmability provide an ideal framework for implementing efficient fault detection and recovery mechanisms. The proposed model leverages real-time monitoring, redundancy protocols, and adaptive rerouting strategies to mitigate the impact of node or link failures. Key components of the model include failover mechanisms, load balancing, and traffic rerouting algorithms, designed to maintain seamless network operations during failures. Through simulation and testing, the model demonstrates significant improvements in network resilience, reducing recovery time and ensuring uninterrupted service delivery. This research provides a comprehensive guide to implementing fault-tolerant networks using SDN, offering scalable solutions that can be adapted to various network sizes and configurations. The findings emphasize the potential of SDN to revolutionize fault management in modern network infrastructures, making it a crucial consideration for future network design and operations.

Indonesian Journal of Computer ScienceVol. 13(6)
Walter Sisulu University (ZA)
Industry, innovation and infrastructure
Openalex Percentile: Top 75%
Software-Defined Networks and 5G
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.