A Dual Framework and Algorithms for Targeted Online Data Delivery
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, the authors 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. They 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. They discuss the benefits of this latter approach and develop an adaptive monitoring solution Satisfy User Profiles (SUPs).