Research Article
Representative Delay Measurements (RDM): Facing the Challenge of Modern Networks
@INPROCEEDINGS{10.4108/icst.valuetools.2014.258181, author={Joachim Fabini and Tanja Zseby and Michael Hirschbichler}, title={Representative Delay Measurements (RDM): Facing the Challenge of Modern Networks}, proceedings={8th International Conference on Performance Evaluation Methodologies and Tools}, publisher={EAI}, proceedings_a={VALUETOOLS}, year={2015}, month={2}, keywords={measurements networks delay one-way delay real-time anomaly repeatability timing hspa lte}, doi={10.4108/icst.valuetools.2014.258181} }- Joachim Fabini
Tanja Zseby
Michael Hirschbichler
Year: 2015
Representative Delay Measurements (RDM): Facing the Challenge of Modern Networks
VALUETOOLS
ICST
DOI: 10.4108/icst.valuetools.2014.258181
Abstract
Network access technologies have evolved significantly in the last years. They deploy novel mechanisms like reactive capacity allocation and time-slotted operation to optimize overall network capacity. From a single node's perspective, such optimizations decrease network determinism and measurement repeatability. Evolving application fields like machine to machine (M2M) communications or real-time gaming often have strict real-time requirements to operate correctly. Highly accurate delay measurements are necessary to monitor network compliance with application demands or to detect deviations of normal network behavior, which may be caused by network failures, misconfigurations or attacks.
This paper analyzes factors that challenge active delay measurements in modern networks. It introduces the Representative Delay Measurement tool (RDM) that addresses these factors and proposes solutions that conform to requirements of the recently published RFC7312. Delay measurement results acquired using RDM in live networks confirm that advanced measurement methods can significantly improve the quality of measurement samples by isolating systematic network behavior. The resulting high-quality samples are one prerequisite for accurate statistics that support proper operation of subsequent algorithms and applications.
