Date of Completion

5-6-2020

Embargo Period

5-6-2023

Keywords

SDN, QoS, Fat-tree, Large-scale networks, Mininet, OpenDaylight, Congestion Control

Major Advisor

Chun-Hsi Huang

Associate Advisor

Reda A. Ammar

Associate Advisor

Sanguthevar Rajasekaran

Field of Study

Computer Science and Engineering

Degree

Doctor of Philosophy

Open Access

Open Access

Abstract

Recent research has shown that the Software-Defined Networking (SDN) technology is a promising architecture providing abstraction and programmability of modern networks and enables a more efficient solution to many of the security, performance, management, and QoS issues. This dissertation researches and enriched SDN with an added level of QoS for network applications. Starting by investigating the potential of an SDN-based large-scale networked system, two topologies widely used in modern data centers, namely, Fat-tree and BCube, are considered. Their behavior and performance under different network scales, traffic loads, and traffic patterns are studied. Experimental results indicate the superiority of a Fat-tree network as it scales up. The potential of SDN in supporting Big-Data applications is subsequently investigated, using a Hadoop cluster with the Fat-tree interconnection. Experimental results in terms of throughput and execution time for the read/write and sorting operations demonstrate the superiority of the SDN controller over the normal forwarding mechanism. In addition, a framework adopting externally developed modules to enrich SDN capabilities for forwarding, metric retrievals, and congestion control is proposed. A QoS level is guaranteed for traffic classification, metric-based route selection, or congestion detection and control. The behavior and the performance of different traffic types, namely, UDP, TCP, VOIP, and a Big-Data application, are investigated. Experimental results substantiate the advantage of having developed modules on top of the controller. The proposed framework reduces the overall average delay, jitter, and packet loss by 54%, 32%, and 51%, respectively. Moreover, the average utilization of ports is reduced by 22%.

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Available for download on Saturday, May 06, 2023

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