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ABSTRACT

A variety of emerging online data delivery applications challenge existing techniques for data delivery to human users, applications, or middleware that are accessing data from multiple autonomous servers. In this paper, we develop a framework for formalizing and comparing pull-based solutions and present dual optimization approaches. The first approach, most commonly used nowadays, maximizes user utility under the strict setting of meeting a priori constraints on the usage of system resources. We present an alternative and more flexible approach that maximizes user utility by satisfying all users. It does this while minimizing the usage of system resources. We discuss the benefits of this latter approach and develop an adaptive monitoring solution Satisfy User Profiles (SUPs). Through formal analysis, we identify sufficient optimality conditions for SUP. Using real (RSS feeds) and synthetic traces, we empirically analyze the behavior of SUP under varying conditions. Our experiments show that we can achieve a high degree of satisfaction of user utility when the estimations of SUP closely estimate the real event stream, and has the potential to save a significant amount of system resources. We further show that SUP can exploit feedback to improve user utility with only a moderate increase in resource utilization.

Introduction

The diversity of data sources and Web services currently
available on the Internet and the computational Grid, as
well as the diversity of clients and application requirements
poses significant infrastructure challenges. In this paper, we
address the task of targeted data delivery. Users may have
specific requirements for data delivery, e.g., how frequently
or under what conditions they wish to be alerted about update
events or update values, or their tolerance to delays
or stale information. Initially these users were humans but
they are being replaced by decision agents.



CONCLUSION

We focused on pull-based data delivery that supports user
profile diversity. Minimizing the number of probes to
sources is important for pull-based applications to conserve
resources and improve scalability. Solutions that can adapt
to changes in source behavior are also important due to the
difficulty of predicting when updates occur. We have
addressed these challenges through the use of a new
formalism of a dual optimization problem (OptMon2),
reversing the roles of user utility and system resources. This
revised specification leads naturally to a surprisingly
simple, yet powerful algorithm (SUP) which satisfies user
specifications while minimizing system resource
consumption. We have formally shown that SUP is optimal
for OptMon2 and under certain restrictions can be optimal
for OptMon1 as well. Using RSS data traces as well as
synthetic data, that SUP can satisfy user profiles and
capture more updates compared to existing policies. SUP is
adaptive and can dynamically change monitoring
schedules. Our experiments show that using feedback in
SUP improves the performance with a moderate increase in
the number of needed probes.