Design and evaluation of a wide-area event notification service

Abstract

The components of a loosely coupled system are typically designed to operate by generating and responding to asynchronous events. An event notification service is an application-independent infrastructure that supports the construction of event-based systems, whereby generators of events publish event notifications to the infrastructure and consumers of events subscribe with the infrastructure to receive relevant notifications. The two primary services that should be provided to components by the infrastructure are notification selection (i.e., determining which notifications match which subscriptions) and notification delivery (i.e., routing matching notifications from publishers to subscribers). Numerous event notification services have been developed for local-area networks, generally based on a centralized server to select and deliver event notifications. Therefore, they suffer from an inherent inability to scale to wide-area networks, such as the Internet, where the number and physical distribution of the service's clients can quickly overwhelm a centralized solution. The critical challenge in the setting of a wide-area network is to maximize the expressiveness in the selection mechanism without sacrificing scalability in the delivery mechanism. This paper presents SIENA, an event notification service that we have designed and implemented to exhibit both expressiveness and scalability. We describe the service's interface to applications, the algorithms used by networks of servers to select and deliver event notifications, and the strategies used to optimize performance. We also present results of simulation studies that examine the scalability and performance of the service. is maximizing expressiveness in the selection mechanism without sacrificing scalability in the delivery mechanism [Carzaniga et al. 2000a]. Expressiveness refers to the ability of the event notification service to provide a powerful data model with which to capture information about events, to express filters and patterns on notifications of interest, and to use that data model as the basis for optimizing notification delivery. In terms of scalability, we are referring not simply to the number of event generators, the number of event notifications, and the number of notification recipients, but also to the need to discard many of the assumptions made for local-area networks, such as low latency, abundant bandwidth, homogeneous platforms, continuous and reliable connectivity, and centralized control. We recognize that there are other important attributes of an event notification service besides expressiveness and scalability, including security, reliability, and fault tolerance, but we do not address them in this paper.Intuitively, a simple event notification service that provides no selection mechanism can be reduced to a multicast routing and transport mechanism for which there are numerous scalable implementations. However, once the service provides a selection mechanism, then the overall efficiency of the service and its routing of no...

Run Time Models in Adaptive Service Infrastructure

“…Therefore, the predictive models, representing the system alternatives, are evaluated at run time and this poses strong requirements on the models themselves. PMF has been experimented to manage the performance of the PFM publish/subscribe middleware [16,19]. The experiment shows that the usage of predictive models improves the decision step.…”

“…In the last scenario, we shortly describe the PFM framework developed as part of a project internal to our group. In particular, we describe the application of PFM to the management of the performance of the Siena publish/subscribe middleware [16,19]. PFM allows the management of the system performance at run time based on monitoring and model-based performance evaluation [17].…”

“…Since the project is still in a preliminary stage, we cannot provide a detailed description of it. In the last scenario we describe how to manage the performance of the Siena publish/subscribe middleware [16,19] by using the Performance Management Framework (PFM). PFM allows the management of the system performance at run time based on monitoring and model-based performance evaluation [17].…”

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Content-Based Publish/Subscribe Using Distributed R-Trees

“…A straightforward approach for avoiding false positives and false negatives is to organize the subscribers in a tree structure according to containment relationships [6], such that the subscription of a peer contains the subscriptions of its descendants. Indeed, if an event matches the containee, it has to match the container (this guarantees no false negatives); conversely, if it does not match the container, it cannot match the containee (this guarantees no false positives).…”