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Semantic Aware Online Detection of Resource Anomalies on the Cloud

As cloud based platforms become more popular, it becomes an essential task for the cloud administrator to efficiently manage the costly hardware resources in the cloud environment. Prompt action should be taken whenever hardware resources are faulty, or configured and utilized in a way that causes application performance degradation, hence poor quality of service. In this paper, we propose a semantic aware technique based on neural network learning and pattern recognition in order to provide automated, real-time support for resource anomaly detection. We incorporate application semantics to narrow down the scope of the learning and detection phase, thus enabling our machine learning technique to work at a very low overhead when executed online. As our method runs "life-long" on monitored resource usage on the cloud, in case of wrong prediction, we can leverage administrator feedback to improve prediction on future runs. This feedback directed scheme with the attached context helps us to achieve an anomaly detection accuracy of as high as 98.3% in our experimental evaluation, and can be easily used in conjunction with other anomaly detection techniques for the cloud.