SaaS software performance issue identification using HMRF-MAP framework: SaaS software performance issue identification

The performance of the software-as-a-service (SaaS) software is often characterized by combinations of performance metrics monitored in a cloud computing platform. Due to the complexity of the application software and the dynamic nature of the deployment environment, manual diagnosis for performance issues based on metric data is typically expensive and laborious. In order to solve the above problems, we propose an automatic performance issue identification method. This approach constructs the hidden Markov random field maximum a posteriori (HMRF-MAP) model based on the monitored metric values. The model calculates the current performance state of the system by analyzing the historical states of the system. In this paper, we evaluate our approach in a case study of a production system deployed on the cloud computing platform. The evaluation results show that our approach (1) has small system overhead, (2) is accurate in identifying the time frame during which a performance issue occurs, (3) is indeed useful and assists an operation and maintenance manager in recovering the service capability of SaaS software, and (4) is better than other approaches for identifying the performance issues in the system.