Blog
The latest from Google Research
Towards Energy-Proportional Datacenters
miércoles, 1 de septiembre de 2010
Posted by Dennis Abts, Michael R. Marty, Philip M. Wells, Peter Klausler, and Hong Liu
This is part of the
series
highlighting some notable publications by Googlers.
At Google, we operate large datacenters containing clusters of servers, networking switches, and more. While this gear costs a lot of money, an increasingly important cost -- both in terms of dollars and environmental impact -- is the electricity that drives the computing clusters and the cooling infrastructure. Since our clusters often do not run at full utilization, Google recently put forth a call to industry and researchers to develop energy proportional computer systems. With such systems, the power consumed by our clusters would be directly proportional to utilization. Servers consume the most electricity, and therefore researchers have responded to Google’s call by focusing their attention towards servers. As the servers become increasingly energy proportional, however, the “always on” network fabric that connects servers together will consume an increasing fraction of datacenter power unless it too becomes energy proportional.
In a
paper
recently published at the International Symposium on Computer Architecture (ISCA), we push further towards the goal of energy-proportional computing by focusing on the energy usage of high-bandwidth, highly-scalable cluster networking fabrics. This research considers a broad set of architectural and technological solutions to optimize energy usage without sacrificing performance. First, we show how the Flattened Butterfly network topology uses less power since it uses less switching chips and fewer links than a comparable-performance network built using the more conventional Fat Tree topology. Second, our approach takes advantage of the observation that when network demand is low, we can reduce the speed at which links transmit data. We show via simulation, that by tuning the speeds of the links very rapidly, we can reduce power consumption with little impact on performance. Finally, our research is a further call to action for the academic and industry research communities to make energy efficiency, and energy proportionality in particular, a first-class citizen in networking research. Put together, our proposed techniques can reduce energy cost for typical Google workloads seen in our production datacenters by millions of dollars!
Etiquetas
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2021
sep
ago
jul
jun
may
abr
mar
feb
ene
2020
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2019
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2018
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2017
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2016
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2015
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2014
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2013
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2012
dic
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2011
dic
nov
sep
ago
jul
jun
may
abr
mar
feb
ene
2010
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2009
dic
nov
ago
jul
jun
may
abr
mar
feb
ene
2008
dic
nov
oct
sep
jul
may
abr
mar
feb
2007
oct
sep
ago
jul
jun
feb
2006
dic
nov
sep
ago
jul
jun
abr
mar
feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
