TY - GEN
T1 - Flow-level Tail Latency Estimation and Verification based on Extreme Value Theory
AU - Helm, Max
AU - Wiedner, Florian
AU - Carle, Georg
N1 - Publisher Copyright:
© 2022 IFIP.
PY - 2022
Y1 - 2022
N2 - Modeling extreme latencies in communication net-works can contribute information to network planning and flow admission under service level agreements. Extreme Value Theory is such an approach that utilizes real-world measurement data. It is often applied without verifying the resulting model predictions on larger datasets. Here we show that such models can provide accurate predictions over larger datasets while being applied to 100 random network topologies and configurations. We found that applying derived models with a bounded tail to a twentyfold time period results in a prediction accuracy of 75% for extreme latency exceedances. Furthermore, we show that tail latency quantiles can be predicted on a flow level with median absolute percentage errors ranging from 0.7% to 16.8%. Therefore, we consider this approach to be useful for dimensioning networks under latency-constrained service level agreements.
AB - Modeling extreme latencies in communication net-works can contribute information to network planning and flow admission under service level agreements. Extreme Value Theory is such an approach that utilizes real-world measurement data. It is often applied without verifying the resulting model predictions on larger datasets. Here we show that such models can provide accurate predictions over larger datasets while being applied to 100 random network topologies and configurations. We found that applying derived models with a bounded tail to a twentyfold time period results in a prediction accuracy of 75% for extreme latency exceedances. Furthermore, we show that tail latency quantiles can be predicted on a flow level with median absolute percentage errors ranging from 0.7% to 16.8%. Therefore, we consider this approach to be useful for dimensioning networks under latency-constrained service level agreements.
KW - data analysis
KW - extreme value theory
KW - latency measurements
KW - network modeling
UR - https://www.scopus.com/pages/publications/85143891428
U2 - 10.23919/CNSM55787.2022.9964525
DO - 10.23919/CNSM55787.2022.9964525
M3 - Conference contribution
AN - SCOPUS:85143891428
T3 - Proceedings of the 2022 18th International Conference of Network and Service Management: Intelligent Management of Disruptive Network Technologies and Services, CNSM 2022
SP - 359
EP - 363
BT - Proceedings of the 2022 18th International Conference of Network and Service Management
A2 - Charalambides, Marinos
A2 - Papadimitriou, Panagiotis
A2 - Cerroni, Walter
A2 - Kanhere, Salil
A2 - Mamatas, Lefteris
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th International Conference of Network and Service Management, CNSM 2022
Y2 - 31 October 2022 through 4 November 2022
ER -