Title :
A Dual Framework and Algorithms for Targeted Online Data Delivery
Author :
Roitman, Haggai ; Gal, Avigdor ; Raschid, Louiqa
Author_Institution :
IBM Haifa Res. Lab., Haifa, Israel
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.
Keywords :
Web services; data handling; optimisation; SUP; adaptive monitoring solution; dual algorithms; dual framework; formal analysis; human users; multiple autonomous servers; optimization approaches; resource utilization; satisfy user profiles; targeted online data delivery; Databases; Feedback; Feeds; Grid computing; Humans; Middleware; Resource management; Web and internet services; Web server; Web services; Distributed databases; client/server multitier systems; online data delivery.; online information services;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2010.15