Link Criticality Characterization for Network Optimization : An approach to reduce packet loss rate in packet-switched networks

Sammanfattning: Network technologies are continuously advancing and attracting ever-growing interests from the industry and society. Network users expect better experience and performance every day. Consequently, network operators need to improve the quality of their services. One way to achieve this goal entails over-provisioning the network resources, which is not economically efficient as it imposes unnecessary costs. Another way is to employ Traffic Engineering (TE) solutions to optimally utilize the current underlying resources by managing traffic distribution in the network. In this thesis, we consider packet-switched Networks (PSN), which allows messages to be split across multiple packets as in today’s Internet. Traffic engineering in PSN is a well-known topic yet current solutions fail to make efficient utilization of the network resources. The goal of the TE process is to compute a traffic distribution in the network that optimizes a given objective function while satisfying the network capacity constraints (e.g., do not overflow the link capacity with an excessive amount of traffic). A critical aspect of TE tools is the ability to capture the impact of routing a certain amount of traffic through a certain link, also referred as the link criticality function. Today’s TE tools rely on simplistic link criticality functions that are inaccurate in capturing the network-wide performance of the computed traffic distribution. A good link criticality function allows the TE tools to distribute the traffic in a way that it achieves close-to-optimal network performance, e.g., in terms of packet loss and possibly packet latencies. In this thesis, we embark upon the study of link criticality functions and introduce four different criticality functions called: 1) LeakyCap, 2) LeakyReLU, 3) SoftCap, and 4) Softplus. We compare and evaluate these four functions with the traditional link criticality function defined by Fortz and Thorup, which aims at capturing the performance degradation of a link given its utilization. To assess the proposed link criticality functions, we designed 57 network scenarios and showed how the link criticality functions affect network performance in terms of packet loss. We used different topologies and considered both constant and bursty types of traffic. Based on our results, the most reliable and effective link criticality function for determining traffic distribution rates is Softplus. Softplus outperformed Fortz function in 79% of experiments and was comparable in the remaining 21% of the cases.