Detecting and localizing end-to-end performance degradation for cellular data services

摘要

Providing high end-to-end (E2E) performance is critical for cellular service providers to best serve their customers. Detecting and localizing E2E performance degradation is crucial for cellular service providers, content providers, device manufactures, and application developers to jointly troubleshoot root causes. To the best of our knowledge, detection and localization of E2E performance degradation at cellular service providers has not been previously studied. In this paper, we propose a holistic approach to detecting and localizing E2E performance degradation at cellular service providers across the four dimensions of user locations, content providers, device types, and application types. First, we use training data to build models that can capture the normal performance of every E2E-instance, which means flows corresponding to a specific location, content provider, device type, and application type. Second, we use our models to detect performance degradation for each E2E-instance on an hourly basis. Third, after each E2E-instance has been labeled as non-degrading or degrading, we use association rule mining techniques to localize the source of performance degradation. Our system detected performance degradation instances over a period of one week. In 80% of the detected degraded instances, content providers, device types, and application types were the only factors of performance degradation.