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MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Experimental evaluation of loss perception in continuous media
Multimedia Systems
Measurement study of low-bitrate internet video streaming
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Building a Performance Model of Streaming Media Applications in Utility Data Center Environment
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
An analysis of live streaming workloads on the internet
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Autonomous Fault Recovery Technology for Achieving Fault-Tolerance in Video on Demand System
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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USITS'03 Proceedings of the 4th conference on USENIX Symposium on Internet Technologies and Systems - Volume 4
Detecting performance anomalies in global applications
WORLDS'05 Proceedings of the 2nd conference on Real, Large Distributed Systems - Volume 2
Youtube traffic characterization: a view from the edge
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Anomaly detection and diagnosis in grid environments
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Online Anomaly Prediction for Robust Cluster Systems
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Human perception of jitter and media synchronization
IEEE Journal on Selected Areas in Communications
Streaming video over the Internet: approaches and directions
IEEE Transactions on Circuits and Systems for Video Technology
Wi-Fi/ZigBee coexistence for fault-tolerant building automation system
International Journal of Systems, Control and Communications
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Video-streaming services are dominating the Internet, delivering content for video-on-demand, TV, education and collaborative work. Service parameters addressing quality and continuity of video content have a special importance due to the human sensitiveness to variations on video quality and decades of quality patterns absorbed by traditional TV users. Thus, the performance analysis and repair lifecycle at server and network levels is mandatory to avoid degradation of user experience. At the network level, there are several effective techniques based on temporal and spatial data redundancy, though they deeply depend on healthy servers with enough resources to afford both the client and recovery workloads. Excess of streaming workloads and performance anomalies (i.e., server resources exhaustion not explained by client requests) are typical causes of server performance failures. The former is often caused by memory caching of popular videos, which impacts the number of requests accepted by the server and consequently blurs load admittance mechanisms when the workload changes. The latter is caused by server internal factors independent of client workloads (e.g., memory leaks and maintenance activities). Separating client workload related failures from performance anomalies is mandatory for selection of immediate repair actions, capacity planning and to support fault repair. We evaluated the performance of Naive Bayes and C4.5 Trees algorithms for classification of these failure states using client and server performance metrics. Results shown that it is possible to predict the type of failure with levels of recall and accuracy higher than 90% for workload types with different popularity levels.