On the combined effects of Bit Error Rate and delay-distribution tail on TCP performance

The original design of the TCP retransmission timeout was implemented ignoring the recent measurement studies on the dynamics and features of network traffic and delay. Such studies have reported the highly variable characteristics of network delay, considered to be heavy-tailed distributed. Accordingly, depending on the heavy characteristics of the tail of the delay distribution, the actual implementation of TCP's retransmission timeout might be too conservative, or rather insufficient. This work aims to assess the optimal design of the retransmission timeout when heavy-tailed delay profiles are present. In our experiments, we have considered the case of low-bit error rate scenarios typical from wired networks as well as the high bit-error rates, typical from wireless networks. We show that the current implementation of the retransmission timeout is in broad terms very conservative, except in cases with extremely heavy tails

Weibull mixture model to characterise end-to-end Internet delay at coarse time-scales

Traces collected at monitored points around the Internet contain representative performance information about the paths their probes traverse. Basic measurement attributes, such as delay and loss, are easy to collect and provide a means to both build and validate empirical performance models. However, the task of analysis and extracting performance conclusions from measurements remains challenging. Ideally, performance modelling aims to find a set of self-contained parameters to describe, summarise, profile and easy display network performance status at a time. This can result in the provision of meaningful information to address applications in fault and performance management, hence providing input to network provisioning, traffic engineering and performance prediction. In this work we present the Weibull Mixture Model, a method to characterise endto- end network delay measurements within a few simple, accurate, representative and handleable parameters using a finite combination of Weibull distributions, with all the aforementioned benefits. The model parameters are related tomeaningful delay characteristics, such as average peak and tail behaviour in a daily profile, and can be optimally found using an iterative algorithm known as Expectation Maximisation. Studies on such parameter evolution can reflect current workload status and all possible network events impacting packet dynamics, with further applications in network management. In addition, a self-sufficient procedure to implement the Weibull Mixture Model is presented, along with a set of matching examples to real GPS synchronised measurements taken across the Internet, donated by RIPE NCC

Discrete-time heavy-tailed chains, and their properties in modelling network traffic

The particular statistical properties found in network measurements, namely self-similarity and long-range dependence, cannot be ignored in modelling network and Internet traffic. Thus, despite their mathematical tractability, traditional Markov models are not appropriate for this purpose, since their memoryless nature contradicts the burstiness of transmitted packets. However, it is desirable to find a similarly tractable model which is, at the same time, rigorous at capturing the features of network traffic. This work presents the discrete-time heavy-tailed chains, a tractable approach to characterise network traffic as a superposition of discrete-time “on/off” sources. This is a particular case of the generic “on/off” heavy-tailed model, thus showing the same statistical features as the former; particularly, self-similarity and long-range dependence, when the number of aggregated sources approaches infinity. The model is then applicable to characterise a number of discrete-time communication systems, for instance ATM and Optical Packet Switching, and further derive meaningful performance met- rics, such as the average burst duration and the number of active sources in a random instant