Subscriber Assignment for Wide-Area Content-Based Publish/Subscribe

— We study the problem of assigning subscribers to brokers in a wide-area content-based publish/subscribe system. A good assignment should consider both subscriber interests in the event space and subscriber locations in the network space, and balance multiple performance criteria including bandwidth, delay, and load balance. The resulting optimization problem is NP-complete, so systems have turned to heuristics and/or simpler algorithms that ignore some performance criteria. Evaluating these approaches has been challenging because optimal solutions remain elusive for realistic problem sizes. To enable proper evaluation, we develop a Monte Carlo approximation algorithm with good theoretical properties and robustness to workload variations. To make it computationally feasible, we combine the ideas of linear programming, randomized rounding, coreset, and iterative reweighted sampling. We demonstrate how to use this algorithm as a yardstick to evaluate other algorithms, and why it is be...

• Type: Journal
• Year: 2012
• Where: TKDE
• Authors: Albert Yu, Pankaj K. Agarwal, Jun Yang
• Keywords: Approximation Algorithm, Formal Methods, Google Groups, Monte Carlo Approximation, TKDE 2012