ew 15(6): e5

Research Article

Representative Delay Measurements (RDM): Facing the Challenge of Modern Networks

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  • @ARTICLE{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},
        journal={EAI Endorsed Transactions on Energy Web},
        keywords={measurements, networks, delay, one-way delay, real-time, anomaly, repeatability, timing, hspa, lte},
  • Joachim Fabini
    Tanja Zseby
    Michael Hirschbichler
    Year: 2015
    Representative Delay Measurements (RDM): Facing the Challenge of Modern Networks
    DOI: 10.4108/icst.valuetools.2014.258181
Joachim Fabini1,*, Tanja Zseby1, Michael Hirschbichler1
  • 1: Institute of Telecommunications, Vienna University of Technology
*Contact email: joachim.fabini@tuwien.ac.at


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.