Enabling Network Stability in a Scalable Software-Defined Data Center Network
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 analogy for modelling the traffic flow interference which causes network instability. A Multi-Objective Optimization Crosspoint Queue (MOCQ) was modelled after the primary stages of the ART’s adaptive pipeline processes. The MOCQ process consisted of a control flow list, sorting entry matching, matching flow table and matching operation. This process enabled the achievement of stable controller-switch states. vi The efficacy of the proposed ART-based MOCQ approach has been extensively validated using numerical analysis, miniature testbed and computer simulations. The approach resulted in improved performance over existing approaches as demonstrated by faster stability, improved response time and network throughput, and speedy rate of convergence. Furthermore, a balanced perspective of individual interests on the sides of both service providers and end-users was attained. The emerging facts were confirmed by a measure of a 42.9% reduction in the rate of network response delays, and over 150% point improvement in correcting the existing rate of switch failures when compared to the ordinary Crosspoint Queue approach. The approach also assisted in determining the level of stability attained and in the case of the setup in this thesis, a level of 0.7 was achieved for the hierarchical data centre network we are considering. This determination is an improvement over existing systems as they do not go all the way to achieve this goal.
Data Centre Network, Network scalability, Adaptive Rendering Technique
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