A Dual Framework and Algorithms for Targeted Data Delivery
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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.