Affiliation:
1. University of California at Santa Barbara
2. Rutgers University
Abstract
Current resource provisioning schemes in Internet services leave servers less than 50% utilized almost all the time. At this level of utilization, the servers' energy efficiency is substantially lower than at peak utilization. A solution to this problem could be dynamically consolidating workloads into fewer servers and turning others off. However, services typically resist doing so, because of high response times during reactivation in handling traffic spikes. Moreover, services often want the memory and/or storage of all servers to be readily available at all times.
In this article, we propose a family of
barely alive
active low-power server states that facilitates both fast reactivation and access to memory while in a low-power state. We compare these states to previously proposed active and idle states. In particular, we investigate the impact of load bursts in each energy-saving scheme. We also evaluate the additional benefits of memory access under low-power states with a study of a search service using a cooperative main-memory cache. Finally, we propose a system that combines a barely-alive state with the off state. We find that the barely alive states can reduce service energy consumption by up to 38%, compared to an energy-oblivious system. We also find that these energy savings are consistent across a large parameter space.
Funder
Energy-Proportional Computing
Publisher
Association for Computing Machinery (ACM)
Subject
Electrical and Electronic Engineering,Hardware and Architecture,Software
Reference31 articles.
1. Adamic L. 2000. Zipf power-laws and pareto -- A ranking tutorial. Tech. rep. HP Labs. Adamic L. 2000. Zipf power-laws and pareto -- A ranking tutorial. Tech. rep. HP Labs.
2. FAWN
3. The Case for Energy-Proportional Computing
4. PRESS: a clustered server based on user-level communication
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