Network traffic is said to be bursty in the sense that a large fraction of the workload or volume of the data transferred is due to rare but large transfer volumes. In general, burstiness is dominant in data flows generated by a single user, but traffic often appears to be bursty at both single-user and multiple-user levels. More formally, a bursty time series has an empirical complementary distribution function with very slow decay. Burstiness is also referred to as impulsiveness and extreme variability.
Published in Chapter:
Performance Measurement of Computer Networks
Federico Montesino Pouzols (University of Seville, Spain), Angel Barriga Barros (University of Seville, Spain), Diego R. Lopez (RedIRIS, Spain), and Santiago Sánchez-Solano (CSIC - Scientific Research Council, Spain)
Copyright: © 2008
|Pages: 7
DOI: 10.4018/978-1-59904-885-7.ch160
Abstract
In this article, general findings about Internet traffic models are first reviewed, with emphasis on two important invariants or characteristics that are observed with some reproducibility and independently of the precise settings of the network under consideration: self-similarity and heavy-tail marginal distributions. Then metrics and measurement techniques and tools will be discussed. This article deals with generic network performance measurement systems and outlines models, measurement techniques and tools that measure performance at the network and transport layers and can thus be applied regardless of the application layer protocols being employed. These systems are useful for analyzing performance of any network application and are an important foundational tool for enabling advanced virtual organizations (Foster, Kesselman, & Tuecke, 2001). Note, however, that application-level (or specific application details aware) measurements are commonly needed to complement generic tools so as to achieve a clear understanding of overall applications performance, which cannot be synthesized from lower level data with ease (Andrews, Cao, & McGowan, 2006).