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Detecting and Localizing End-to-End Performance Degradation for Cellular Data Services Based on TCP Loss Ratio and Round Trip Time.

, , , , , and . IEEE/ACM Trans. Netw., 25 (6): 3709-3722 (2017)

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Detecting and localizing end-to-end performance degradation for cellular data services., , , , , and . INFOCOM, page 1-9. IEEE, (2016)Monitoring quality-of-experience for operational cellular networks using machine-to-machine traffic., , , , , and . INFOCOM, page 1-9. IEEE, (2017)Offline/realtime traffic classification using semi-supervised learning., , , , and . Perform. Evaluation, 64 (9-12): 1194-1213 (2007)Internet Traffic Identification using Machine Learning., , and . GLOBECOM, IEEE, (2006)HTTP in the home: it is not just about PCs., , and . Comput. Commun. Rev., 41 (1): 90-95 (2011)Identifying diverse usage behaviors of smartphone apps., , , , , and . Internet Measurement Conference, page 329-344. ACM, (2011)Detecting and Localizing End-to-End Performance Degradation for Cellular Data Services., , , , , and . SIGMETRICS, page 459-460. ACM, (2015)Traffic classification using clustering algorithms., , and . MineNet, page 281-286. ACM, (2006)How to Reduce Smartphone Traffic Volume by 30%?, , , , , and . PAM, volume 7799 of Lecture Notes in Computer Science, page 42-52. Springer, (2013)HTTP in the home: it is not just about PCs., , and . HomeNets@SIGCOMM, page 43-48. ACM, (2010)