Performance improvement of quality of service routing under inaccurate link-state information
It has been observed that the current best-effort IP packet delivery service in the global Internet is sometimes not good enough for emerging real-time multimedia applications. These resource intensive applications normally have more stringent requirements on bandwidth, delay, delay jitter etc. The quality of service (QoS) requirements of these applications raise new challenges for the development of new routing mechanisms. QoS routing can provide increased network utilisation compared to best-effort routing by efficiently regulating and managing resource sharing across a network. However, the benefit of QoS routing comes with complex routing computation costs and increased routing protocol overhead. It is impractical to collect detailed global state information and keep it up-to-date in large-scale dynamic networks, such as the Internet. As a result, inaccurate link-state information increases the flow blocking probability and makes source nodes select non-optimal paths. To maximise the link utilisation and meet application QoS requirements, routing algorithms need accurate link-state information to make routing decisions. This thesis investigates the statistical properties of time series of link utilisation. In particular, the evaluation focuses on the presence of autocorrelation in the time series. Further study under various link-state update policies, network and traffic configurations identifies the factors that may affect the statistical properties of the time series. Based on this analysis, a prediction-based link-state update policy is proposed to reduce the effect of inaccurate link-state information. The approach predicts the link-state utilisation trend based on past values. By advertising trend rather than instantaneous link utilisation, the routing algorithms may have more valuable information to make routing decisions instead of being affected by short lived sudden changes. An appropriate model that can satisfactorily fit the actual model is identified, estimated and validated. Finally, the performance of the proposed prediction-based link-state update policy is validated by simulation and compared with conventional update policies under a variety of network configurations. the results show that this approach is effective in improving routing performance.