[Colloquium] Ranganathan/Dissertation Defense/8-23-04

[Margaret Jaffey]

This is an announcement of Kavitha Ranganathan's dissertation defense,  
which will be held two weeks from today.
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		Department of Computer Science/The University of Chicago

				*** Dissertation Defense ***


Candidate:  Kavitha Ranganathan

Defense Date:  Monday, August 23, 2004

Time and Location:  10:00 a.m. in Ryerson 255

Thesis Title:  Management of Storage and Compute Resources in Large  
Distributed Communities

Abstract:
Data Grids seek to harness geographically distributed resources  
(storage, compute and network) for large-scale data-intensive problems  
such as those encountered in high energy physics, bioinformatics, and  
other disciplines. These problems typically involve numerous, loosely  
coupled jobs that access large data sets. Effective resource management  
in such environments is challenging, because of a need to address a  
variety of metrics and constraints (for example, resource utilization,  
response time, global and local allocation policies) while dealing with  
multiple, potentially independent sources of resources and jobs.

The focus of this dissertation is the design of efficient and robust  
resource management techniques that are effective across a wide range  
of Data Grid scales, topologies and problem classes. We identify  
specific requirements for a Data-Grid resource manager and propose a  
decentralized and scalable architecture based on these requirements.  
The solution comprises of autonomous agents spread across the Grid that  
dynamically adapt their strategy according to perceived environment and  
workload characteristics.

We also study the nature of sharing in such environments to ascertain  
whether independent users will be willing to unconditionally let others  
use their resources. We find that providing incentives for cooperation  
is a critical step for the eventual success of such systems. To this  
end, we define and evaluate soft-incentive schemes that can be easily  
implemented under the proposed architecture.

We then go on to develop a family of job scheduling and data movement  
(replication) strategies that fit into the proposed architecture.  
Instead of studying the two aspects separately, we evaluate various  
combinations of job and data scheduling strategies, under a wide range  
of workload patterns, Grid topologies and user behavior. Two evaluation  
techniques were used: a discrete event simulator, which we built for  
modeling wide-area collaborations and a Grid testbed of 28 sites spread  
across the county. Our results suggest that decoupling data movement  
and computation scheduling has significant advantages when compared to  
the traditional approach of data movement being tightly coupled to job  
execution. We also find that dynamically adapting the scheduling  
strategy according to workload characteristics has considerable  
performance advantages.

Candidate's Advisor:  Prof. Ian Foster

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A draft copy of Ms. Ranganathan's dissertation is available in Ry 161A.
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Margaret P. Jaffey				margaret at cs.uchicago.edu
Department of Computer Science
Student Support Rep (Ry 161A)		(773) 702-6011
The University of Chicago		http://www.cs.uchicago.edu
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