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The ability to cap peak power consumption is a desirable feature in modern data centers for energy budgeting, cost management, and efficient power delivery. Dynamic voltage and frequency scaling (DVFS) is a traditional control knob in the tradeoff between server power and performance. Multi-core processors and the parallel applications that take advantage of them introduce new possibilities for control,...
The paper proposes an adaptive framework for modern data centers that jointly manages application- and system-level (dynamic voltage and frequency scaling) adaptation to improve energy efficiency of multicore servers. The results of applying this framework show that significant power savings and performance improvements are possible with respect to current data center management techniques.
Today's US power markets offer new opportunities for the energy consumers to reduce their energy costs by first promising an average consumption rate for the next hour and then by following a regulation signal broadcast by the independent system operators (ISOs), who need to match supply and demand in real time in presence of volatile and intermittent renewable energy generation. This paper leverages...
Power capping on server nodes has become an essential feature in data centers for controlling energy costs and peak power consumption. More than half of the server nodes are virtualized in today's data centers; thus, providing a practical power capping technique for consolidated virtual environments is a significant research problem. This paper proposes a power capping technique, vCap, which makes...
Cloud services have been actively used for transactional and batch workloads. Recently, multi-threaded high-performance computing (HPC) workloads have started to emerge on the cloud as well. Unlike most traditional data center loads, HPC workloads highly utilize the servers. The energy efficiency and performance of HPC loads, however, vary strongly as a function of the amount of allocated resources...
As today's computing trends are moving towards the cloud, meeting the increasing computational demand while minimizing the energy costs in data centers has become essential. This work introduces two adaptive techniques to reduce the energy consumption of the computing clusters through power and resource management on multi-core processors. We first present a novel power capping technique to constrain...
Future computing clusters will prevalently run parallel workloads to take advantage of the increasing number of cores on chips. In tandem, there is a growing need to reduce energy consumption of computing. One promising method for improving energy efficiency is co-scheduling applications on compute nodes. Efficient consolidation for parallel workloads is a challenging task as a number of factors,...
As the number of cores per processor grows, there is a strong incentive to develop parallel workloads to take advantage of the hardware parallelism. In comparison to single-threaded applications, parallel workloads are more complex to characterize due to thread interactions and resource stalls. This paper presents an accurate and scalable method for determining the optimal system operating points...
As an initial step in our Green Software research, this paper investigates whether software optimization at the application level can help achieve higher energy efficiency and better thermal behavior. We use both direct measurements and modeling to quantify power, energy and temperature for a given software method. The infrastructure includes a new power estimator for multicore systems developed by...
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