Applying Machine Learning to Study Infrastructure Anomalies in a Mid-size Data Center -- Preliminary Considerations
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Date
2021-01-05
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218
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Today, data centers deal with fast growing data volumes. To deliver services, they deploy growing amount of heterogeneous hardware. As a result, it becomes practically impossible to apply human-based data center management. For instance, in a real-world data center, with 500+ computers, delivering data, computational, and network services, it becomes impossible to visualize, and understand, causal relationships among variables describing performance of monitored resources. However, it is possible to collect data describing behavior of individual nodes. Hence, such data may be used to analyze/model system performance. In particular, it may be applied to recognize and predict anomalies in system behavior. Furthermore, collected data should allow finding the cause(s) of anomalies. Therefore, “data-driven approaches” have been applied to the real-world data, to find, so called, Root Cause of anomalies.
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Business Intelligence and Big Data for Innovative and Sustainable Development of Organizations, anomaly detection, causality, data center, root cause analysis, time series
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10 pages
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Proceedings of the 54th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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