Discovery of Flow Splitting Ratios in ISP Networks with Measurement Noise

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Abstract

Network telemetry and analytics is essential for providing highly dependable services in modern computer networks. In particular, network flow analytics for ISP networks allows operators to inspect and reason about traffic patterns in their networks in order to react to anomalies. High performance network analytics systems are designed with scalability in mind, and can consequently only observe partial information about the network traffic. Still, they need to provide a holistic view of the traffic, including the distribution of different traffic flows on each link. It is impractical to monitor such fine-grained telemetry, and in large, heterogeneous networks it is often too complex and error-prone, if not impossible, to access and maintain all technical specifications and router-specific configurations needed to determine e.g. the load balancing weights used when traffic is split onto multiple paths. The ratios by which flows are split on the possible paths must be derived indirectly from the measured flow demands and link utilizations. Motivated by a case study provided by a major European ISP, we suggest an efficient method to estimate the flow splitting ratios. Our approach, based on quadratic linear programming, is scalable and robust to the measurement noise found in a typical network analytics deployment. Finally, we implement an automated tool for estimating the flow splitting ratios and document its applicability on real data from the ISP.
OriginalsprogEngelsk
TitelProceedings - 2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing, PRDC 2023
Antal sider7
ForlagIEEE (Institute of Electrical and Electronics Engineers)
Publikationsdato2023
Sider64-70
ISBN (Trykt)979-8-3503-5877-3
ISBN (Elektronisk)979-8-3503-5876-6
DOI
StatusUdgivet - 2023
Begivenhed28th IEEE Pacific Rim International Symposium on Dependable Computing - Singapore Management University, Singapore, Singapore
Varighed: 24 okt. 202327 okt. 2023
https://prdc.dependability.org/PRDC2023/index.html

Konference

Konference28th IEEE Pacific Rim International Symposium on Dependable Computing
LokationSingapore Management University
Land/OmrådeSingapore
BySingapore
Periode24/10/202327/10/2023
Internetadresse
NavnIEEE Pacific Rim International Symposium on Dependable Computing (PRDC)
ISSN2473-3105

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