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In modern data centers, energy usage represents one of the major factorsaffecting operational costs. Power capping is a technique that limits the powerconsumption of individual systems, which allows reducing the overall powerdemand at both cluster and data center levels. However, literature powercapping approaches do not fit well the nature of important applications basedon first-class multi-thread technology. For these applications performance maynot grow linearly as a function of the thread-level parallelism because of theneed for thread synchronization while accessing shared resources, such asshared data. In this paper we consider the problem of maximizing theapplication performance under a power cap by dynamically tuning thethread-level parallelism and the power state of the CPU-cores. Based onexperimental observations, we design an adaptive technique that selects inlinear time the optimal combination of thread-level parallelism and CPU-corepower state for the specific workload profile of the multi-threadedapplication. We evaluate our proposal by relying on different benchmarks,configured to use different thread synchronization methods, and compare itseffectiveness to different state-of-the-art techniques.

Adaptive Performance Optimization under Power Constraint in Multi-thread Applications with Diverse Scalability