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In 2013, U.S. data centers accounted for 2.2% of the country's total electricity consumption, a figure that is projected to increase rapidly over the next decade. Many important workloads are interactive, and they demand strict levels of quality-of-service (QoS) to meet user expectations, making it challenging to reduce power consumption due to increasing performance demands.This paper introduces Hipster, a technique that combines heuristics and reinforcement learning to manage latency-critical workloads. Hipster's goal is to improve resource efficiency in data centers while respecting the QoS of the latency-critical workloads. Hipster achieves its goal by exploring heterogeneous multicores and dynamic voltage and frequency scaling (DVFS). To improve data center utilization and make best usage of the available resources, Hipster can dynamically assign remaining cores to batch workloads without violating the QoS constraints for the latency-critical workloads. We perform experiments using a 64-bit ARM big.LITTLE platform, and show that, compared to prior work, Hipster improves the QoS guarantee for Web-Search from 80% to 96%, and for Memcached from 92% to 99%, while reducing the energy consumption by up to 18%.

The current trend to move from homogeneous to heterogeneous multicore systems provides compelling opportunities for achieving performance and energy efficiency goals. Running multiple threads in multicore systems poses challenges on meeting limited shared resources, such as memory bandwidth. We propose an optimization approach that includes an Integer Linear Programming (ILP) optimization model and a scheme to dynamically determine thread-to-core assignment. We present simulation analysis that shows energy savings and performance gains for a variety of workloads compared to state-of-the-art schemes. We implemented and evaluated a prototype of our thread assignment approach at user level, leveraging Linux scheduling and performance-monitoring capabilities.

This paper proposes and evaluates an approach for power and performance management in virtualized server clusters. The major goal of our approach is to reduce power consumption in the cluster while meeting performance requirements. The contributions of this paper are: (1) a simple but effective way of modeling power consumption and capacity of servers even under heterogeneous and changing workloads, and (2) an optimization strategy based on a mixed integer programming model for achieving improvements on power-efficiency while providing performance guarantees in the virtualized cluster. In the optimization model, we address application workload balancing and the often ignored switching costs due to frequent and undesirable turning servers on/off and VM relocations. We show the effectiveness of the approach applied to a server cluster testbed. Our experiments show that our approach conserves about 50% of the energy required by a system designed for peak workload scenario, with little impact on the applications' performance goals. Also, by using prediction in our optimization strategy, further QoS improvement was achieved.

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In this paper we present an optimization solution for power and performance management in a platform running multiple independent applications. Our approach assumes a virtualized server environment and includes an optimization model and strategy to dynamically control the cluster power consumption, while meeting the application's workload demands.

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