Improving Network Measurement Efficiency through Multiadaptive Sampling

Sampling techniques play a key role in achieving efficient network measurements by reducing the amount of traffic processed while trying to maintain the accuracy of network statistical behavior estimation. Despite the evolution of current techniques regarding the correctness of network parameters estimation, the overhead associated with the volume of data involved in the sampling process is still considerable. In this context, this paper proposes a new technique for multiadaptive traffic sampling based on linear prediction, which allows to reduce significantly the traffic under analysis, keeping the representativeness of samples in capturing network behavior. A proof-of-concept, evaluating this technique for real traffic traces representing distinct traffic profiles, demonstrates the effectiveness of the proposal, outperforming classic techniques both in accuracy and data volumes processed.