A Dual Framework and Algorithms for Targeted Data Delivery
A Dual Framework and Algorithms for Targeted Data Delivery
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Date
2005-11-03T15:18:56Z
Authors
Roitman, Haggai
Raschid, Louiqa
Gal, Avigdor
Bright, Laura
Advisor
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Abstract
A variety of emerging wide area applications challenge existing techniques for
data delivery to users and applications accessing data from multiple autonomous
servers. In this paper, we develop a framework for comparing pull based
solutions and present dual optimization approaches. Informally, the first
approach maximizes user utility of profiles while satisfying constraints on the
usage of system resources. The second approach satisfies the utility of user
profiles while minimizing the usage of system resources.
We present a static optimal solution (SUP) for the latter approach and formally
identify sufficient conditions for SUP to be optimal for both.
A shortcoming of static solutions to pull-based delivery is that they cannot
adapt to the dynamic behavior of Web source updates.
Therefore, we present an adaptive algorithm (fbSUP) and show how it can
incorporate feedback to improve user utility with only a moderate increase in
probing. Using real and synthetic data traces, we analyze the behavior of SUP
and fbSUP under various update models.