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Round-Trip Time Anomaly Detection


Publication Type:

Conference/Workshop Paper


International Conference on Performance Engineering


Mobile text messages (SMS) are sometimes used for authorization, a service that requires short and reliable delivery times. The round- trip times required for sending an SMS message provide valuable information on the quality of the connection, giving the sender a possibility to send the messages via another operator. To measure the reliability of the connection, we thus want to find anomalies in the round-trip times, 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 this industry paper, we propose a method for detecting round- trip time anomalies. In particular, we show that using exponential smoothing and a variation of the Holt-Winters method for seasonal forecasting 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 anomalies in this type of data.


author = {Daniel Brahneborg and Wasif Afzal and Adnan Causevic and Daniel Sundmark and Mats Bj{\"o}rkman},
title = {Round-Trip Time Anomaly Detection},
number = {9},
month = {April},
year = {2018},
booktitle = {International Conference on Performance Engineering},
url = {}