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A physically-aware architecture for self-organizing peer-to-peer overlay networks.

University of Technology, Sydney. Faculty of Information Technology. / Over the last few years Peer-to-Peer (P2P) systems have emerged as highly attractive systems supporting many useful large-scale applications and services. They allow the exploitation of enormous untapped resources (such as idle processing cycles, storage, and bandwidth) available at Internet-connected devices, which were previously considered incapable of providing services to others. Participating nodes (peers) form an overlay network and communicate with each other without being controlled by a central authority. The structures and routing decisions of the most current P2P networks often do not correlate with the Internet infrastructure. In doing so, the tasks of overlay construction and routing become less complicated however, this results in high end-to-end delay for the P2P applications. As a consequence, the P2P networks may not be able to provide stringent Quality of Service (QoS) requirements for a new generation of P2P applications, and thus limit their benefits for the end users. Moreover, the infrastructure ignorance means P2P systems waste Internet resources by adding more than they should to the Internet traffic. This leads to the increase in Internet access costs substantially, and in turn the P2P systems do not scale well. The thesis presents a novel architecture for developing efficient P2P systems, and new schemes for constructing infrastructure-aware overlay networks. The main objective is first, to overcome the disparity between the overlay and Internet structures in order to maximize the use of network resources and reduce the overlay delay to the P2P applications; second, to provide efficient communication for P2P systems enabling deployment of any P2P applications while preserving decentralized, self-organizing and self-maintaining characteristics for the systems. To achieve these goals, we firstly developed Geographically Longest Prefix Matching (Geo-LPM) and Geographical Partitioning (Geo-Partitioning) schemes to cluster nodes that are close to each other in terms of network latency and network membership, and to determine links between neighboring clusters respectively. The developed schemes are efficient, generate low overhead, and help to produce excellent physically/infrastructure-aware overlay networks. Their distinctive features are self-organization, self-maintenance, and decentralization, which make them suitable to work in a P2P environment. Secondly we propose a novel architecture, called a physically-aware reference model (PARM) that captures desirable features for P2P systems by resolving major functional P2P system problems efficiently in a layered structure. For example, the application routing layer of PARM deals with routing inefficiency, meanwhile the infrastructure unawareness is resolved at the overlay network layer. We develop a useful P2P application, called a Peer Name Service (PNS) that interprets node names into their current IP addresses for any Internet-connected devices. Using the overlay networks, the PNS can support devices, which could be unreachable via the Domain Name Server (DNS), and mobile devices on-the-move without prior setup requirement in a distributed and timely fashion. Finally, to validate the whole concept of PARM, we simulate the PNS and a file transfer to a mobile node at the top layer of PARM, the P2P application layer. Since the PNS is sensitive to delay, it would be useful to evaluate the impacts of overlay delay factor and PARM on the performance of P2P applications. The simulation results show that the performance of the PARM-based applications is significantly improved while achieving decentralized and self-organizing features. The results also indicate that PARM can be a recommended reference model for developing scalable and efficient P2P systems.

  1. http://hdl.handle.net/2100/611
Identiferoai:union.ndltd.org:ADTP/269752
Date January 2006
CreatorsLe, Thi Hong Hanh
Source SetsAustraliasian Digital Theses Program
LanguageEnglish
Detected LanguageEnglish

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