Measurements and Models of One-Way Transit Time in IP Routers

Detta är en avhandling från Karlskrona : Blekinge Institute of Technology

Sammanfattning: The main goals of this thesis are towards an understanding of the delay process in best-effort Internet for both non-congested and congested networks. A novel measurement system is reported for delay measurements in IP routers, which follows specifications of the IETF RFC 2679. The system employs both passive measurements and active probing and offers the possibility to measure and analyze different delay components of a router, e.g., packet processing delay, packet transmission time and queueing delay at the output link. Dedicated application-layer software is used to generate UDP traffic with TCP-like characteristics. Pareto traffic models are used to generate self-similar traffic in the link. The reported results are in form of several important statistics regarding processing and queueing delays of a router, router delay for a single data flow, router delay for multiple data flows as well as end-to-end delay for a chain of routers. They confirm results reported earlier about the fact that the delay in IP routers is generally influenced by traffic characteristics, link conditions and, to some extent, details in hardware implementation and different IOS releases. The delay in IP routers may also occasionally show extreme values, which are due to improper functioning of the routers. Furthermore, new results have been obtained that indicate that the delay in IP routers shows heavy-tailed characteristics, which can be well modeled with the help of several distributions, either in the form of a single distribution or as a mixture of distributions. There are several components contributing to the OWTT in routers, i.e., processing delay, queueing delay and service time. The obtained results have shown that, e.g., the processing delay in a router can be well modeled with the Normal distribution, and the queueing delay is well modeled with a mixture of Normal distribution for the body probability mass and Weibull distribution for the tail probability mass. Furthermore, OWTT has several component delays and it has been observed that the component delay distribution that is most dominant and heavy-tailed has a decisive influence on OWTT.