[Colloquium] Yang/Dissertation Defense/1-31-07

[Margaret Jaffey]

		Department of Computer Science/The University of Chicago

				*** Dissertation Defense ***


Candidate:  Lingyun Yang

Date:  Wednesday, January 31, 2007

Time and Location:  10:00 a.m., location to be announced

Title:  Anomaly Management in Grid Environments

Abstract:
          Recent experience in deploying Grid middleware has  
demonstrated the
challenges one faces in delivering robust service in distributed and
shared environments. In particular, unexpected ("anomalous")  
variations in
resource availability and performance can cause significant difficulties
for applications that need to deliver reliable performance to their  
users.
Preventing, detecting, and diagnosing such unexpected anomalous
behaviors-what is known as anomaly management-is not a new concept, and
has been studied in many areas. However, the autonomy, heterogeneity and
dynamicity of Grid environments introduce particular difficulties, as do
the complexities of the often tightly coupled applications executed in
such environments [126]. Application performance problems can result  
from
interactions among an application and the different resources on  
which it
executes. For example, even modest contention on a network link may  
have a
major impact on the performance of communication-intensive numerical
modeling applications. In this context, I hypothesize that: by
incorporating anomaly management mechanisms into Grid systems, we can
allow end users to prevent, detect, and diagnose application-level
anomalies in complex Grid environments.
       To evaluate this thesis, we study new challenges in the three  
aspects
of the application anomaly management in the Grid environments: (1)
avoiding anomalies before they occur, (2) detecting application  
anomalies
when they occur, and (3) diagnosing why anomalies occur, once they are
detected. We present novel techniques to solve these challenges and also
evaluate each technique using real applications. We conduct experiments
that show that our new techniques can help users detect and diagnose the
cause of performance anomalies.This information provides the data needed
to achieve reliable application-level performance, even when resource
performance or availability may change during application execution.

Candidate's Advisor: Prof. Ian Foster

A draft copy of Ms. Yang's dissertation will be available soon 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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