Due to the imperative need to reduce the management costs, operators multiplex several concurrent applications in large datacenters. However, uncontrolled resource sharing between co-hosted applications often results in performance degradation problems, thus creating violations of service level agreements (SLAs) for service providers. Therefore, in order to meet per-application SLAs, per-application performance modeling for dynamic resource allocation in shared resource environments has recently become promising. We introduce Chorus, an interactive performance modeling framework for building application performance models incrementally and on the fly. It can be used to support complex, multi-tier resource allocation, and/or what-if performance inquiry in modern datacenters, such as Clouds. Chorus consists of (i) a declarative high-level language for providing semantic model guidelines, such as model templates, model functions, or sampling guidelines, from a sysadmin or a performance analyst, as model approximations to be learned or refined experimentally, (ii) a runtime engine for iteratively collecting experimental performance samples, validating and refining performance models. Chorus efficiently builds accurate models online, reuses and adjusts archival models over time, and combines them into an ensemble of models. We perform an experimental evaluation on a multi-tier server platform, using several industry- standard benchmarks. Our results show that Chorus is a flexible modeling framework and knowledge base for validating, extending and reusing existing models while adapting to new situations.

Due to the imperative need to reduce the management costs, operators multiplex several concurrent applications in large datacenters. However, uncontrolled resource sharing between co-hosted applications often results in performance degradation problems, thus creating violations of service level agreements (SLAs) for service providers. Therefore, in order to meet per-application SLAs, per-application performance modeling for dynamic resource allocation in shared resource environments has recently become promising. We introduce Chorus, an interactive performance modeling framework for building application performance models incrementally and on the fly. It can be used to support complex, multi-tier resource allocation, and/or what-if performance inquiry in modern datacenters, such as Clouds. Chorus consists of (i) a declarative high-level language for providing semantic model guidelines, such as model templates, model functions, or sampling guidelines, from a sysadmin or a performance analyst, as model approximations to be learned or refined experimentally, (ii) a runtime engine for iteratively collecting experimental performance samples, validating and refining performance models. Chorus efficiently builds accurate models online, reuses and adjusts archival models over time, and combines them into an ensemble of models. We perform an experimental evaluation on a multi-tier server platform, using several industry- standard benchmarks. Our results show that Chorus is a flexible modeling framework and knowledge base for validating, extending and reusing existing models while adapting to new situations.