Identifying Most Relevant Performance Measures for Root Cause Analysis of Performance Degradation Events on a Private Cloud Computing Application: Experiment in an Industry Environment

Cloud computing applications (CCA) are defined by their elasticity, on-demand provisioning and ability to address, cost-effectively, volatile workloads. These new cloud computing (CC) applications are being increasingly deployed by organizations but without a means of managing their performance proactively. While CCA provide advantages and disadvantages over traditional client-server applications, their unreliable application performance due to the intricacy and the high number of multi connected moving parts of its underlying infrastructure, has become a major challenge for software engineers and system administrators. For example, capturing how the end-users perceive the application performance as they complete their daily tasks has not been addressed satisfactorily. One possible approach for identifying the most relevant performance measures for Root Cause Analysis (RCA) of performance degradation events on CCA, from an end-user perspective, is to leverage the information captured in performance logs, a source of data that is widely available in today’s datacenters, and where detailed records of resource consumption and performance logs is captured from numerous systems, servers and network components used by the CCA. This paper builds on a model proposed for measuring CC application performance and extends it with the addition of the end-user perspective, exploring how it can be used in identifying root causes (RC) for performance degradation events in a large-scale industrial scenario. The experimentation required adjustments to the original proposal in order to determine, with the help of a multivariate statistical technique, the performance of a CCA from the perspective of an end-user. An experiment with a corporate email CCA is also presented and illustrates how the performance model can identify most relevant performance measures and help predict future performance issues.