A Modular Architecture for Deploying Self-adaptive Traffic Sampling

Abstract

Traffic sampling is seen as a mandatory solution to cope with the huge amount of traffic traversing network devices. Despite the substantial research work in the area, improving the versatility of adjusting sampling to the wide variety of foreseeable measurement scenarios has not been targeted so far. This motivates the development of an encompassing measurement model based on traffic sampling able to support a large range of network management activities, in a scalable way. The design of this model involves identifying sampling techniques through its components rather than a closed unit, allowing to address issues such as flexibility, estimation accuracy, data overhead and computational weight within a narrower and simpler scope. This paper concretises these ideas presenting a modular and self-configurable measurement architecture based on sampling, a framework implementing sampling inherent pieces, and provides first results when deploying the proposed concepts in real traffic scenarios.