Elastic and Real-Time Capacity Planning for Web Search Engines

Web search engines are complex systems deployed on large clusters of processors. With a constant growing number of users, Web search engines capacity must be frequently evaluated. Among other techniques, capacity planning methods are a good approach to estimate the amount of processors in cluster based systems, allowing data-center engineers to ensure enough computational resources are available to efficiently deal with the ever changing streams of user queries. In this paper, we present a novel capacity planning methodology that combines discrete-event simulation and classical operational analysis for open systems formulae. With this methodology, just a small fraction of deployments are evaluated to find an optimal assignment of resources given a target workload. We experimentally evaluate a simulation-based capacity planning module in combination with an OpenMP+MPI implementation of a Web search engine, that periodically evaluates its performance metrics in order to elastically adjust the amount of computational resources needed to support the current workload. Results show that the proposed methodology is able to quickly provide a new deployment configuration upon performance changes, ensuring that an strictly sufficient amount of computational resources are available to efficiently deal with users demands. © 2020 IEEE.