Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
Diagnosing network disruptions with network-wide analysis
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Practical guide to controlled experiments on the web: listen to your customers not to the hippo
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Spatio-temporal compressive sensing and internet traffic matrices
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
ANTIDOTE: understanding and defending against poisoning of anomaly detectors
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Detecting the performance impact of upgrades in large operational networks
Proceedings of the ACM SIGCOMM 2010 conference
Diagnosing performance changes by comparing request flows
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Rapid detection of maintenance induced changes in service performance
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
Self-Organizing Networks (SON): Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE
A NICE way to test openflow applications
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Trustworthy online controlled experiments: five puzzling outcomes explained
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Abstractions for network update
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
X-ray: automating root-cause diagnosis of performance anomalies in production software
OSDI'12 Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation
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Cellular network service providers often have to conduct small scale testing in the operational network before a change (e.g., a new feature) is fully rolled out across the entire network. This is referred to as the First Field Application (FFA). However, assessing the effectiveness of FFA changes is challenging because of overlapping external factors: seasonality (foliage, leaves budding), weather (rain, snow, hurricanes, storms), traffic pattern changes due to big events (e.g., games at stadiums, students returning to school after holidays), and network events such as outages or other maintenance activities in different regions. In this paper, we first highlight the technical challenges in assessing the service performance impact of changes in operational cellular networks. We then propose Litmus, a new approach based on a spatial dependency model for robust assessment of changes. We evaluate the effectiveness of Litmus using real-world data from operational cellular networks (GSM, UMTS and LTE). Our operational experiences demonstrate accurate inferences of the service performance impact of changes in the field.