Thesis (PhD)--University of South Australia, 1994

Master of Engineering by Research / Service-level-agreement (SLA) monitoring measures network Quality-of-Service (QoS) parameters to evaluate whether the service performance complies with the SLAs. It is becoming increasingly important for both Internet service providers (ISPs) and their customers. However, the rapid expansion of the Internet makes SLA monitoring a challenging task. As an efficient method to reduce both complexity and overheads for QoS measurements, sampling techniques have been used in SLA monitoring systems. In this thesis, I conduct a comprehensive study of sampling methods for network QoS measurements. I develop an efficient sampling strategy, which makes the measurements less intrusive and more efficient, and I design a network performance monitoring software, which monitors such QoS parameters as packet delay, packet loss and jitter for SLA monitoring and verification. The thesis starts with a discussion on the characteristics of QoS metrics related to the design of the monitoring system and the challenges in monitoring these metrics. Major measurement methodologies for monitoring these metrics are introduced. Existing monitoring systems can be broadly classified into two categories: active and passive measurements. The advantages and disadvantages of both methodologies are discussed and an active measurement methodology is chosen to realise the monitoring system. Secondly, the thesis describes the most common sampling techniques, such as systematic sampling, Poisson sampling and stratified random sampling. Theoretical analysis is performed on the fundamental limits of sampling accuracy. Theoretical analysis is also conducted on the performance of the sampling techniques, which is validated using simulation with real traffic. Both theoretical analysis and simulation results show that the stratified random sampling with optimum allocation achieves the best performance, compared with the other sampling methods. However, stratified sampling with optimum allocation requires extra statistics from the parent traffic traces, which cannot be obtained in real applications. In order to overcome this shortcoming, a novel adaptive stratified sampling strategy is proposed, based on stratified sampling with optimum allocation. A least-mean-square (LMS) linear prediction algorithm is employed to predict the required statistics from the past observations. Simulation results show that the proposed adaptive stratified sampling method closely approaches the performance of the stratified sampling with optimum allocation. Finally, a detailed introduction to the SLA monitoring software design is presented. Measurement results are displayed which calibrate systematic error in the measurements. Measurements between various remote sites have demonstrated impressively good QoS provided by Australian ISPs for premium services.

Thesis (M. Eng. Sc.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1992. / Includes bibliographical references (leaves 119-120).

Thesis (M.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 49-50). Also available on the World Wide Web.

Thesis (M.S.)--Rochester Institute of Technology, 1987. / Typescript. Includes bibliographical references (leaves 160-166).

Thesis (M. Phil.)--University of Hong Kong, 1991.

Thesis (M.Eng.)--Memorial University of Newfoundland, 2003. / Bibliography: leaves 114-117. Also available online.

Thesis (M.Eng.)--Memorial University of Newfoundland, 2004. / Bibliography: leaves 93-97.

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references.

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