Adaptive gossip-based broadcast

This paper presents a novel adaptation mechanism that allows every node of a gossip-based broadcast algorithm to adjust the rate of message emission 1) to the amount of resources available to the nodes within the same broadcast group and 2) to the global level of congestion in the system. The adaptation mechanism can be applied to all gossip-based broadcast algorithms we know of and makes their use more realistic in practical situations where nodes have limited resources whose quantity changes dynamically with time without decreasing the reliability.(undefined

Event Systems : How to Have Your Cake and Eat It Too

This paper addresses the fundamental tradeoffs in event systems between scalability (of event filtering, routing, and delivery mechanisms), expressiveness (when describing interests in events), and event safety (ensuring encapsulation and type-safe interaction with polymorphic events). We point out some ramifications underlying these tradeoffs and we propose a pragmatic approach to handle them. We achieve scalability using a multi-stage filtering strategy that combines approximate and perfect matching techniques for the purpose of event routing and filtering. We achieve expressiveness and event safety by viewing events as objects; instances of application-defined abstract types

Lightweight Probabilistic Broadcast

The growing interest in peer-to-peer applications has underlined the importance of scalability in modern distributed systems. Not surprisingly, much research effort has been invested in gossip-based broadcast protocols. These trade the traditional strong reliability guarantees against very good ``scalability'' properties. Scalability is in that context usually expressed in terms of throughput, but there is only little work on how to reduce the overhead of membership management at large scale. This paper presents Lightweight Probabilistic Broadcast (lpbcast), a novel gossip-based broadcast algorithm which preserves the inherent throughput scalability of traditional gossip-based algorithms and adds a notion of membership management scalability: every process only knows a random subset of fixed size of the processes in the system. We formally analyze our broadcast algorithm in terms of scalability with respect to the size of individual views, and compare the analytical results both with simulations and concrete measurements