Hierarchical Probabilistic Multicast∗

In our DACE project [6], diverging requirements expressed through QoS are mainly explored by a variety of different delivery semantics implemented through different dissemination algorithms ranging from “classic” Reliable Broadcast [18], to new and original algorithms, like the broadcast algorithm we introduce in [7], and which ensures reliable delivery of events despite network failures. While striving for strong scalability, we have invested considerable effort in exploring probabilistic (gossip-based) algorithms. These appear to be more adequate in the field of large scale event dissemination than traditional strongly reliable approaches like [18]. Basically, probabilistic algorithms trade the strong reliability guarantees against very good scalability properties, yet still achieve a “pretty good degree of reliability” [12]. Until now, most work on gossip-based algorithms considers broadcasting information to all participants in a system, paying little or no attention to individual and dynamic requirements, as typically encountered in contentbased dissemination. We present here Hierarchical Probabilistic Multicast (hpmcast [9]), a novel gossip-based algorithm which deals with the more complex case of multicasting an event to a subset of the system only. Requirements, such as limiting the consumption of local memory resources by view and message buffering, as well as exploiting locality (the proximity of participants) and redundancy (commonalities in interests of these participants), are all addressed. Though hpmcast has been motivated by our specific context of TPS, it is general enough to be applied to any context in which a strongly scalable primitive for event, message, or information dissemination is required.