Enabling Network Stability in a Scalable Software-Defined Data Center Network
type: CreativeWork
description: In the recent past, Data Centre Network (DCN) has received a lot of research attention because of the indispensable role that DCNs play in modern-day network communication as; the data storage container for on-demand service provisioning. One of the major responsibilities of the DCN is the management of the network’s need for optimization, thereby addressing request services. One consequence of the increasing requests is network flow interference that incurs network instability. Therefore, the task of addressing flow interference within a network becomes very important because it plays a vital role in restoring network stability. Network scalability continues to be the concern of many networks management engineers while less consideration is given to the effectiveness and efficiency of their adopted network stability solutions. Furthermore, very few approaches to network stability gave significant attention to achieving an optimal solution. Several related works notably still left unaddressed slow convergence, computational complexity, complex memory requirements and lack of adaptive stability mechanisms among others. Therefore, the need to enhance network stability in a scalable software-defined and data-oriented DCN remains an open issue for the research community to contend with. This research work addressed this knowledge gap by conducting a comprehensive literature review on relevant existing approaches used by other researchers to address network stability. This provided useful insight and motivation to launch a search for a more robust and appropriate approach to fast network adaptation, and economical utilization of network resources with no negative impairment on the expected optimal performance. The literature review revealed that the concept of Adaptive Rendering Technique (ART) in Computer Graphics was generic and analytically simple enough to adopt for high-level applicability to network flow. The methodology for this study emerged from using ART as the…
abstract: In the recent past, Data Centre Network (DCN) has received a lot of research attention because of the indispensable role that DCNs play in modern-day network communication as; the data storage container for on-demand service provisioning. One of the major responsibilities of the DCN is the management of the network’s need for optimization, thereby addressing request services. One consequence of the increasing requests is network flow interference that incurs network instability. Therefore, the task of addressing flow interference within a network becomes very important because it plays a vital role in restoring network stability. Network scalability continues to be the concern of many networks management engineers while less consideration is given to the effectiveness and efficiency of their adopted network stability solutions. Furthermore, very few approaches to network stability gave significant attention to achieving an optimal solution. Several related works notably still left unaddressed slow convergence, computational complexity, complex memory requirements and lack of adaptive stability mechanisms among others. Therefore, the need to enhance network stability in a scalable software-defined and data-oriented DCN remains an open issue for the research community to contend with. This research work addressed this knowledge gap by conducting a comprehensive literature review on relevant existing approaches used by other researchers to address network stability. This provided useful insight and motivation to launch a search for a more robust and appropriate approach to fast network adaptation, and economical utilization of network resources with no negative impairment on the expected optimal performance. The literature review revealed that the concept of Adaptive Rendering Technique (ART) in Computer Graphics was generic and analytically simple enough to adopt for high-level applicability to network flow. The methodology for this study emerged from using ART as the…
author: Anikola, Ayotuyi Tosin
keywords: Data Centre Network, Network scalability, Adaptive Rendering Technique
license: https://devspace.unizulu.ac.za/bitstreams/f1b9cfb0-207f-4741-8c2b-d3a5210e5a75/download
date published: 2022
date modified: 2026-07-24T06:17:03Z
provider: University of Zululand
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Zululand