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The effect of server energy proportionality on data center ...
We develop a theoretical framework to characterize and predict the relationship between server energy proportionality and power oversubscription. We verify our framework through an extensive empirical study using publicly available SPECpower benchmark data for over 500 server models and publicly available Google cluster utilization data.
Measuring Server Energy Proportionality | Proceedings of ...
D. Wong and M. Annavaram. Scaling the energy proportionality wall with KnightShift. IEEE Micro, 33(3):28--37, May/June 2013. Google Scholar Digital Library; D. Wong and M. Annavaram. Implications of high energy proportional servers on cluster-wide energy proportionality. In International Symposium on High-Performance Computer Architecture, Feb ......
The Case for Energy-Proportional Computing – Google Research
Achieving energy proportionality will require significant improvements in the energy usage profile of every system component, particularly the memory and disk subsystems. Although our experience in the server space motivates these observations, we believe that energy-proportional computing also will benefit other types of computing devices....
 The Case for Energy-Proportional Computing
Energy-proportional designs would enable large energy savings in servers,potentially doubling their efficiency in real-life use.Achieving energy proportionality will require significant improvements in the energy usage profile of every system component, particularly the memory and disk subsystems. Luiz André Barroso and Urs Hölzle Google E...
Efforts Made Towards Achieving Energy Efficiency in Cloud ...
the need of achieving energy efficiency in data centers and survey various techniques used to achieve energy efficiency. 3. ENERGY EFFICIENT TECHNIQUES ... 2005 Managing Server Energy and Operational Costs in Hosting Centers ... traces collected over a 29-day period from a Google cluster containing over 12,500 PMs. Ganesh et al [14] proposed an ......
Energy proportional datacenter networks
tween common server workload profiles and server energy effi-ciency [3]. In particular, they show that a typical Google cluster spends most of its time within the 10-50% CPU utilization range, but that servers are inefficient at these levels. They therefore make the call for energy proportional computing systems that ideally con-...
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