Traffic Analyses and Measurements

Abstract

Resource management schemes in current data centers, including cloud environments, are not well equipped to handle the dynamic variation in traffic caused by the large diversity of traffic sources, source mobility patterns, and underlying network characteristics. Part of the problem is lacking knowledge on the traffic source behaviour and its proper representation for development and operation. Inaccurate, static traffic models lead to incorrect estimation of traffic characteristics, making resource allocation, migration, and release schemes inefficient, and limit scalability. The end result is unsatisfied customers (due to service degradation) and operators (due to costly inefficient infrastructure use). The authors argue that developing appropriate methods and tools for traffic predictability requires carefully conducted and analysed traffic experiments. This chapter presents their measurements and statistical analyses on various traffic sources for two network settings, namely local Area Network (LAN) and 3G mobile network. LAN traffic is organised in DiffServ categories supported by MPLS to ensure Quality of Service (QoS) provisioning. 3G measurements are taken from a live network upon entering the IP domain. Passive monitoring was used to collect the measurements in order to be non-obtrusive for the networks. The analyses indicate that the gamma distribution has general applicability to represent various traffic sources by proper setting of the parameters. The findings allow the construction of traffic models and simulation tools to be used in the development and evaluation of flexible resource management schemes that meet the real-time needs of the users.