On the self-similar nature of Ethernet traffic
SIGCOMM '93 Conference proceedings on Communications architectures, protocols and applications
A unifying review of linear Gaussian models
Neural Computation
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Critical event prediction for proactive management in large-scale computer clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
InteMon: continuous mining of sensor data in large-scale self-infrastructures
ACM SIGOPS Operating Systems Review
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Workload Analysis and Demand Prediction of Enterprise Data Center Applications
IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
ACM Computing Surveys (CSUR)
New Introduction to Multiple Time Series Analysis
New Introduction to Multiple Time Series Analysis
DynaMMo: mining and summarization of coevolving sequences with missing values
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Network anomaly detection based on Eigen equation compression
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Sustainable operation and management of data center chillers using temporal data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
Managing massive time series streams with multi-scale compressed trickles
Proceedings of the VLDB Endowment
Black-box problem diagnosis in parallel file systems
FAST'10 Proceedings of the 8th USENIX conference on File and storage technologies
Parsimonious linear fingerprinting for time series
Proceedings of the VLDB Endowment
DataGarage: warehousing massive performance data on commodity servers
Proceedings of the VLDB Endowment
ThermoCast: a cyber-physical forecasting model for datacenters
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
PAL: Propagation-aware Anomaly Localization for cloud hosted distributed applications
SLAML '11 Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques
Distributed pattern discovery in multiple streams
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Detecting Abnormal Machine Characteristics in Cloud Infrastructures
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Toward Automated Anomaly Identification in Large-Scale Systems
IEEE Transactions on Parallel and Distributed Systems
Finding anomalies in time-series using visual correlation for interactive root cause analysis
Proceedings of the Tenth Workshop on Visualization for Cyber Security
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Metrics like disk activity and network traffic are widespread sources of diagnosis and monitoring information in datacenters and networks. However, as the scale of these systems increases, examining the raw data yields diminishing insight. We present RainMon, a novel end-to-end approach for mining timeseries monitoring data designed to handle its size and unique characteristics. Our system is able to (a) mine large, bursty, real-world monitoring data, (b) find significant trends and anomalies in the data, (c) compress the raw data effectively, and (d) estimate trends to make forecasts. Furthermore, RainMon integrates the full analysis process from data storage to the user interface to provide accessible long-term diagnosis. We apply RainMon to three real-world datasets from production systems and show its utility in discovering anomalous machines and time periods.