Campus Event Calendar
Event Entry
New for: D1, D2, D3, D4, D5
What and Who
Data in the Cloud: New challenges or more of the same?
Divyakant Agrawal
Department of Computer Science, University of California at Santa Barbara
&
NUS School of Computing, Singapore * Visiting Faculty at NUS SoC: June 22, 2010 through September 17, 2010
Colloquium Lecture
Dr. Divy Agrawal serves on the faculty of Computer Science
at the University of California at Santa Barbara. He is widely known for his
ground-breaking work on efficient replication and fault-tolerant quorum protocols.
His research interests are in the areas of distributed systems, databases,
and large-scale information systems such as data warehouses, digital libraries,
and other data/information rich environments.
Divy has served as the program committee chair of the ACM SIGMOD
Conference 2010, and on numerous other committees and editorial boards.
See http://www.cs.ucsb.edu/~agrawal/ for further information.
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
Public Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Monday, 23 August 2010
11:00
60 Minutes
E1 4
024
Saarbrücken
Abstract
Over the past two decades, database and systems researchers
have made significant advances in the development of algorithms and
techniques to provide data management solutions that carefully balance
the three major requirements when dealing with critical data: high
availability, scalability, and data consistency. However, over the
past few years the data requirements, in terms of availability and
scalability, from Internet scale enterprises that provide services and
cater to millions of users has been unprecedented. Current proposed
solutions to scalable data management, driven primarily by prevalent
application requirements, significantly downplay the data consistency
requirements and instead focus on very high availability and almost
unlimited scalability to support data-rich applications for millions
to tens of millions of users. In particular, the "newer" data
management systems limit consistent access only at the granularity of
single objects, rows, or keys, thereby significantly trading-off
consistency in order to achieve very high scalability and
availability. But the growing popularity of "cloud computing", the
resulting shift of a large number of Internet applications to the
cloud, and the quest towards providing data management services in the
cloud, has opened up the challenge for designing data management
systems that provide consistency guarantees at a granularity which
goes beyond single rows and keys. In this talk, we analyze the design
choices that allowed modern scalable data management systems to
achieve orders of magnitude higher levels of scalability compared to
traditional databases. With this understanding, we highlight some
design principles for data management systems providing scalable and
consistent data management as a service in the cloud. We conclude the
talk by presenting results from two prototype systems which strike a
middle-ground between the two radically different data management
architectures: traditional database management systems where the data
is treated as a "whole" versus modern key-value stores where data is
treated as a collection of independent "granules".
have made significant advances in the development of algorithms and
techniques to provide data management solutions that carefully balance
the three major requirements when dealing with critical data: high
availability, scalability, and data consistency. However, over the
past few years the data requirements, in terms of availability and
scalability, from Internet scale enterprises that provide services and
cater to millions of users has been unprecedented. Current proposed
solutions to scalable data management, driven primarily by prevalent
application requirements, significantly downplay the data consistency
requirements and instead focus on very high availability and almost
unlimited scalability to support data-rich applications for millions
to tens of millions of users. In particular, the "newer" data
management systems limit consistent access only at the granularity of
single objects, rows, or keys, thereby significantly trading-off
consistency in order to achieve very high scalability and
availability. But the growing popularity of "cloud computing", the
resulting shift of a large number of Internet applications to the
cloud, and the quest towards providing data management services in the
cloud, has opened up the challenge for designing data management
systems that provide consistency guarantees at a granularity which
goes beyond single rows and keys. In this talk, we analyze the design
choices that allowed modern scalable data management systems to
achieve orders of magnitude higher levels of scalability compared to
traditional databases. With this understanding, we highlight some
design principles for data management systems providing scalable and
consistent data management as a service in the cloud. We conclude the
talk by presenting results from two prototype systems which strike a
middle-ground between the two radically different data management
architectures: traditional database management systems where the data
is treated as a "whole" versus modern key-value stores where data is
treated as a collection of independent "granules".
Contact
Gerhard Weikum
500
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Uwe Brahm, 02/14/2011 13:37
Petra Schaaf, 08/17/2010 12:01 -- Created document.
Petra Schaaf, 08/17/2010 12:01 -- Created document.