Round-Trip Time Anomaly Detection

Mobile text messages (SMS) are sometimes used for authentication, which requires short and reliable delivery times. The observed round-trip times when sending an SMS message provide valuable information on the quality of the connection. In this industry paper, we propose a method for detecting round-trip time anomalies, where the exact distribution is unknown, the variance is several orders of magnitude, and there are lots of shorter spikes that should be ignored. In particular, we show that using an adaption of Double Seasonal Exponential Smoothing to reduce the content dependent variations, followed by the Remedian to find short-term and long-term medians, successfully identifies larger groups of outliers. As training data for our method we use log files from a live SMS gateway. In order to verify the effectiveness of our approach, we utilize simulated data. Our contributions are a description on how to isolate content dependent variations, and the sequence of steps to find significant anomalies in big data.

Afzal, WasifMälardalens högskola,Inbyggda system(Swepub:mdh)wal01 (författare)
Causevic, Adnan,1981-Mälardalens högskola,Inbyggda system(Swepub:mdh)acc01 (författare)
Sundmark, DanielMälardalens högskola,Inbyggda system(Swepub:mdh)dsk01 (författare)
Björkman, MatsMälardalens högskola,Inbyggda system(Swepub:mdh)mbn07 (författare)
Infoflex Connect AB, Stockholm, SwedenInbyggda system (creator_code:org_t)

Ingår i:ICPE '18 Proceedings of the 2018 ACM/SPEC International Conference on Performance EngineeringNew York, NY, USA : ACM, s. 107-1149781450350952

Av författaren/redakt...: Brahneborg, Dani ...; Afzal, Wasif; Causevic, Adnan, ...; Sundmark, Daniel; Björkman, Mats

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Elektroteknik oc ...; och Datorsystem

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