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As the complexity of IT systems increases, performance management and capacity planning become the largest and most difficult expenses to control. New methodologies and modeling techniques that explain large-system behavior and help predict their future performance are now needed to effectively tackle the emerging performance issues. With the multi-tier architecture paradigm becoming an industry standard for developing scalable client-server applications, it is important to design effective and accurate performance prediction models of multi-tier applications under an enterprise production environment and a real workload mix. To accurately answer performance questions for an existing production system with a real workload mix, we design and implement a new capacity planning and anomaly detection tool, called R-Capriccio, that is based on the following three components: i) a Workload Profiler that exploits locality in existing enterprise web workloads and extracts a small set of most popular, core client transactions responsible for the majority of client requests in the system; ii) a Regression-based Solver that is used for deriving the CPU demand of each core transaction on a given hardware; and iii) an Analytical Model that is based on a network of queues that models a multi-tier system. To validate R-Capriccio, we conduct a detailed case study using the access logs from two heterogeneous production servers that represent customized client accesses to a popular and actively used HP Open View Service Desk application.

R-Capriccio: A Capacity Planning and Anomaly Detection Tool for Enterprise Services with Live Workloads