Blog
The latest news from Google AI
Pulling Back the Curtain on Google’s Network Infrastructure
Tuesday, August 18, 2015
Posted by Amin Vahdat, Google Fellow
Pursuing Google’s mission of organizing the world’s information to make it universally accessible and useful takes an enormous amount of computing and storage. In fact, it requires coordination across a
warehouse-scale computer
. Ten years ago, we realized that we could not purchase, at any price, a datacenter network that could meet the combination of our scale and speed requirements. So, we set out to build our own datacenter network hardware and software infrastructure. Today, at the
ACM SIGCOMM conference
, we are presenting a
paper
with the technical details on five generations of our in-house data center network architecture. This paper presents the technical details behind a
talk
we presented at
Open Network Summit
a few months ago.
From relatively humble beginnings, and after a misstep or two, we’ve built and deployed five generations of datacenter network infrastructure. Our latest-generation Jupiter network has improved capacity by more than 100x relative to our first generation network, delivering more than 1 petabit/sec of total
bisection bandwidth
. This means that each of 100,000 servers can communicate with one another in an arbitrary pattern at 10Gb/s.
Such network performance has been tremendously empowering for Google services. Engineers were liberated from optimizing their code for various levels of bandwidth hierarchy. For example, initially there were painful tradeoffs with careful data locality and placement of servers connected to the same top of rack switch versus correlated failures caused by a single switch failure. A high performance network supporting tens of thousands of servers with flat bandwidth also enabled individual applications to scale far beyond what was otherwise possible and enabled tight coupling among multiple federated services. Finally, we were able to substantially improve the efficiency of our compute and storage infrastructure. As quantified in this
recent paper
, scheduling a set of jobs over a single larger domain supports much higher utilization than scheduling the same jobs over multiple smaller domains.
Delivering such a network meant we had to solve some fundamental problems in networking. Ten years ago, networking was defined by the interaction of individual hardware elements, e.g.,
switches
, speaking standardized protocols to dynamically learn what the network looks like. Based on this dynamically learned information, switches would set their forwarding behavior. While robust, these protocols targeted deployment environments with perhaps tens of switches communicating between between multiple organizations. Configuring and managing switches in such an environment was manual and error prone. Changes in network state would spread slowly through the network using a high-overhead broadcast protocol. Most challenging of all, the system could not scale to meet our needs.
We adopted a set of principles to organize our networks that is now the primary driver for networking research and industrial innovation,
Software Defined Networking (SDN)
. We observed that we could arrange emerging merchant switch silicon around a Clos topology to scale to the bandwidth requirements of a data center building. The topology of all five generations of our data center networks follow the blueprint below. Unfortunately, this meant that we would potentially require 10,000+ individual switching elements. Even if we could overcome the scalability challenges of existing network protocols, managing and configuring such a vast number of switching elements would be impossible.
So, our next observation was that we knew the exact configuration of the network we were trying to build. Every switch would play a well-defined role in a larger-ensemble. That is, from the perspective of a datacenter building, we wished to build a logical building-scale network. For network management, we built a single configuration for the entire network and pushed the single global configuration to all switches. Each switch would simply pull out its role in the larger whole. Finally, we transformed routing from a pair-wise, fully distributed (but somewhat slow and high overhead) scheme to a logically-centralized scheme under the control of a single dynamically-elected master as illustrated in the figure below from our paper.
Our SIGCOMM paper on Jupiter is one of four Google-authored papers being presented at that conference. Taken together, these papers show the benefits of embedding research within production teams, reflecting both collaborations with PhD students carrying out extended research efforts with Google engineers during internships as well as key insights from deployed production systems:
Our work on
Bandwidth Enforcer
shows how we can allocate wide area bandwidth among tens of thousands of individual applications based on centrally configured policy, substantially improving network utilization while simultaneously isolating services from one another.
Condor
addresses the challenges of designing data center network topologies. Network designers can specify constraints for data center networks; Condor efficiently generates candidate network designs that meet these constraints, and evaluates these candidates against a variety of target metrics.
Congestion control in datacenter networks is challenging because of tiny buffers and very small round trip times.
TIMELY
shows how to manage datacenter bandwidth allocation while maintaining highly responsive and low latency network roundtrips in the data center.
These efforts reflect the latest in a long series of substantial Google contributions in networking. We are excited about being increasingly open about results of our research work: to solicit feedback on our approach, to influence future research and development directions so that we can benefit from community-wide efforts to improve networking, and to attract the next-generation of great networking thinkers and builders to Google. Our focus on
Google Cloud Platform
further increases the importance of being open about our infrastructure. Since the same network powering Google infrastructure for a decade is also the underpinnings of our Cloud Platform, all developers can leverage the network to build highly robust, manageable, and globally scalable services.
Labels
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
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
