Blog
The latest news from Google AI
Google North American Faculty Summit - cloud computing
Tuesday, August 3, 2010
Posted by Brian Bershad, Director of Engineering, Site Director, Google Seattle
Of the three themes of our 2010 Faculty Summit, cloud computing was the one that pervaded all others, from
security in the cloud
to the presumption of cloud infrastructure behind the social web. But in our more focused discussion on cloud computing last Thursday, we started with the premise of “prodigiousness,” a concept introduced by Afred Spector, VP of Research and Special Initiatives.
While we all know that systems are huge and will get even huger, the implications of this size on programmability, manageability, power, etc. is hard to comprehend. Alfred noted that the Internet is predicted to be carrying a zetta-byte (10
21
bytes) per year in just a few years. And growth in the number of processing elements per chip may give rise to warehouse computers of having 10
10
or more processing elements. To use systems at this scale, we need new solutions for storage and computation. It was these solutions we focused on throughout our discussions.
In the plenary talk, Andrew Fikes spoke on storage system opportunities. Among many topics, he talked about shifting engineering foci to storage management and optimization not just on an individual cluster of co-located systems, but across geographically distributed clusters. The goal is so-called planetary-scale systems. This brings up all manner of diverse challenges ranging from the need to continually balance storage vs. transmission costs, the need to account for variable network latency characteristics, and the desire to optimize storage (e.g., by physically storing only one copy of a file that many feel they have rights to, or own).
We had a few roundtables in the afternoon for deeper discussions. In the table I led, we discussed two systems for “programming the data center” developed by systems researchers at Google Seattle/Kirkland. The first, Dremel, is a scalable, interactive ad-hoc query system for analysis of read-only nested databases. Dremel was recently presented in a paper at VLDB (
Dremel: Interactive Analysis of Web-Scale Datasets
, Sergey Melnik, Andrey Gubarev, Jing Jing Long, Geoffrey Romer, Shiva Shivakumar, Matt Tolton, Theo Vassilakis. In
Proceedings of the 36th Int'l Conf on Very Large Data Bases
, 2010). The system serves as the foundational technology behind
BigQuery
, a product
launched
in limited preview mode at Google I/O in May.
We also discussed FlumeJava, a Java library that makes it easy to develop, test and run efficient data-parallel pipelines at data center scale. FlumeJava was developed by programming languages researchers at Google Seattle, and is currently in widespread use within Google. It was presented at the recent PLDI conference (
FlumeJava: easy, efficient data-parallel pipelines
, Craig Chambers, Ashish Raniwala, Frances Perry, Stephen Adams, Robert R. Henry, Robert Bradshaw, Nathan Weizenbaum. In
Proceedings of the 2010 ACM SIGPLAN conference on Programming language design and implementation
). The work reflects Google’s commitment to programming language and compiler technologies at scale.
The field of data center programming has progressed substantially in the last 10 years. Dremel and FlumeJava systems represent abstractions of a higher level than the
MapReduce
construct we previously introduced, and we think they are easier to use (within their domain of applicability) and more automatically optimizable. With time, the field will discover new “instructions” and even better abstractions leading us to a point where computations which run on nearly unlimited processors can be expressed as easily as sequential programs. We are working hard to make progress here, and I look forward to reporting on our progress in the future.
Labels
2018
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
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
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
2019
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
.
