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Automated Capacity Planning and Performance Modeling for Enterprise Services with Live 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

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