A Capacity Planning Framework for Multi-tier Enterprise Services with Real Workloads

With complexity of systems increasing and customer requirements for QoS growing, new methods and modeling techniques that explain large-systems´ behavior and help predict their future performance are required to effectively tackle the emerging performance issues. To accurately answer capacity planning questions for an existing production system with a real workload mix, we propose a new capacity planning framework that is based on the following three components: i) a Workload Profiler that dynamically builds the workload profile; ii) a Regression-based Solver that is used for deriving the CPU demand of client transactions on a given hardware; and iii) an Analytical model that is based on a network of queues representing the different tiers. To validate our approach, 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.