08-03-2017, 03:13 PM
A variety of emerging online data delivery applications challenge existing techniques for delivering data to human users, applications, or middleware that access data from multiple stand-alone servers. In this article, we developed a framework to formalize and compare solutions based on pull and present dual optimization approaches. The first approach, more commonly used today, maximizes the user's utility under strict compliance with complying with a priori restrictions on the use of system resources. We present an alternative and more flexible approach that maximizes user utility by satisfying all users. This does so by minimizing the use of system resources. We discussed the benefits of this latter approach and developed an adaptive monitoring solution to meet user profiles (SUPs). Through formal analysis, we identified sufficient optimality conditions for the SUP. Using real (RSS feeds) and synthetic traces, we analyze empirically the behavior of SUP in variable conditions. Our experiments show that we can achieve a high degree of user utility satisfaction when SUP estimates closely estimate the actual event flow, and has the potential to save a significant amount of system resources. In addition, we show that SUP can take advantage of feedback to improve user utility with only a moderate increase in resource utilization.