We consider applications that require high rate, reliable message dissemination in a many-to-many environment. Examples of such applications include stock market centers and synchronized server clusters. As network capacity increases, the achievable throughput of messaging applications becomes bounded by processing times rather than communication speed. To reduce processing times we suggest the use of message aggregation. We consider performing message aggregation at either the sender, a message-server, or a network switch. The performance of each of these methods in terms of throughput and delay is analytically evaluated and compared against that of a naive implementation that does not perform message aggregation. We show that in typical real-world messaging applications, performing message aggregation can increase throughput by order of magnitude.We base our results on experiments that have been conducted using various operating systems running on different hardware platforms. Our results indicate that the achievable throughput of messaging applications is determined by the number of packets-per-second, rather than bytes-per-second, a receiver or a transmitter should handle.

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One of the main challenges in building a large scale publish subcribe infrastructure in an enterprise network, is to provide the subscribers with the required information, while minimizing the consumed host and network resources. Typically, previous approaches utilize either IP multicast or point-to-point unicast for efficient dissemination of the information.In this work, we propose a novel hybrid framework, which is a combination of both multicast and unicast data dissemination. Our hybrid framework allows us to take the advantages of both multicast and unicast, while avoiding their drawbacks. We investigate several algorithms for computing the best mapping of publishers' transmissions into multicast and unicast transport.Using extensive simulations, we show that our hybrid framework reduces consumed host and network resources, outperforming traditional solutions. To insure the subscribers interests closely resemble those of real-world settings, our simulations are based on stock market data and on recorded IBM WebShpere subscriptions.

We consider the one-dimensional bin packing problem and analyze the average case performance of bounded space algorithms. The analysis covers a wide variety of bin packing algorithms including Next-K Fit, K-Bounded Best Fit and Harmonic algorithms, as well as of others. We assume discrete item sizes, an assumption which is true in most real-world applications of bin packing. We present a Markov chains method which is general enough to calculate results for any i.i.d. discrete item size distribution. The paper presents many results heretofore unknown or conjectured from simulation. Some of the results are surprising as they differ considerably from results for continuous distributions.

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