Abstract
The increasing popularity of Software as a Service (SaaS) stresses the need of solutions to predict failures and avoid service interruptions, which invariably result in SLA violations and severe loss of revenue. A promising approach to continuously monitor the correct functioning of the system is to check the execution conformance to a set of invariants, i.e., properties that must hold when the system is deemed to run correctly. This paper proposes a technique to spot a true anomalies by the use of various data mining techniques like clustering, association rule and decision tree algorithms help in finding the hidden and previously unknown information from the database. We assess the techniques in two invariants’ applications, namely executions characterization and anomaly detection, using the metrics of coverage, recall and precision. In this work two real-world datasets have been used - the publicly available Google datacenter dataset and a dataset of a commercial SaaS utility computing platform - for detecting the anomalies.