Self-Configuration and Monitoring of Service Specific Overlay Networks

• Main Author: Abdeljaouad, Imad
• Language: en
• Published: 2013
• Subjects: Monitoring; Management; SSON; Autonomic; IPTV; Overlay Networks; Quality of Experience
• Online Access: http://hdl.handle.net/10393/23960

The constant growth in network communications technologies and the emergence of Service Specific Overlay Networks (SSONs), coupled with the rapid development of multimedia applications make the management of such technologies a major challenge. This thesis investigates the SSONs management problem and proposes an autonomic architecture, a self-organizing and self-adapting algorithm, and a utility function for monitoring the Quality of Experience (QoE) of IPTV streams in SSONs. First, we examine the different issues stemming from the autonomic management of SSONs and identify the limitations of existing approaches. We then propose an architecture to ease the management of SSONs by incorporating autonomic computing principles to make SSONs acquire self-management capabilities. The proposed architecture introduces autonomic control loops that continuously monitor network components and analyze the gathered data. An Autonomic System (AS) is comprised of one or more Autonomic Managers (AM) which take control of managing other elements in the network. The proposed architecture highlights the different components of an AM and identifies its purpose. The distributed nature of the proposed architecture avoids limitations of centralized management solutions. We then propose a scheme to allow AMs to emerge among the set of nodes in the network as the most powerful ones in terms of different factors, including processing capabilities and stability. Using a self-organizing and self-adapting distributed protocol, each node in the overlay selects an appropriate AM to report to so that sensed data is delivered error-free, and in a timely manner, while the load is distributed over the AMs. Finally, we propose a utility function to monitor the quality of IPTV streams by predicting QoE based on statistical Quality of Service (QoS) information. The proposed function is simple and does not require high processing power. It allows the QoE of IPTV users to be monitored in real-time by the AMs, so that quality degradations are accurately identified and adaptation mechanisms are triggered at the right moment to correct issues causing degradations. Theoretical analysis and simulations studies are presented to demonstrate the performance of the proposed schemes.