Information flow based event distribution middleware

Event distribution middleware supports the integration of distributed applications by accepting events from information producers and disseminating applicable events to interested consumers. In this paper we present a flexible new model, the Information Fl

Information Flow Based Event Distribution Middleware

Guruduth Banavar1, Marc Kaplan1, Kelly Shaw2, Robert E. Strom1, Daniel C. Sturman1, and Wei Tao3

1

IBM T. J. Watson Research Center

Hawthorne, NY

{banavar, kaplan, strom, sturman}

2

Dept. of Computer ScienceStanford Universitykashaw@cs.stanford.edu

3

Dept. of Computer Science

University of Utahtao@cs.utah.edu

@

Abstract

Event distribution middleware supports the integration ofdistributed applications by accepting events frominformation producers and disseminating applicableevents to interested consumers. In this paper we present aflexible new model, the Information Flow Graph (IFG), forspecifying the flow of information in such a system. Weillustrate the use of the IFG for: (1) content-basedpublish/subscribe; (2) stateless event transformations thatconsolidate events from diverse sources; and (3) statefulevent interpretation functions for deriving trends,summaries, and alarms from published events and fordefining equivalent event sequences. We introduce twotechniques for efficient implementation of such systems: (1) a flow graph rewriting optimization which allowsstateless IFGs to be converted to a form which can exploitefficient multicast technology developed for content-basedpublish/subscribe systems; and (2) an algorithm forconverting a sequence of events to the shortest equivalentsequence of events with respect to an event interpretationfunction.

1Introduction

Event distribution middleware is growing inimportance with the need to glue together heterogeneous,distributed, and dynamically changing components oflarge information systems. The middleware performs thefunction of collecting messages from producers, filteringand transforming them as necessary, and routing them tothe appropriate consumers. This approach is currentlybeing applied in domains such as finance, processautomation, and transportation. The Gryphon project atIBM Research is advancing the technology of eventdistribution middleware and extending its range ofapplication.

Using subject-based publish/subscribe systems as astarting point, Gryphon has introduced the followingextensions:

1.Content-based publish/subscribe. Rather thantreating events as uninterpreted data with a single“subject” field, we associate schemas with eventstreams, and express subscriptions as predicates overall fields in the event.

2.Stateless event transformations. To supportscenarios where events from multiple publishers aresimilar but not identical, Gryphon supportstransformations on events. These operations arestateless in the sense that they do not depend uponprior events.

3.Event stream interpretations. To supportsubscribers who are interested not only in publishedevents but also in events such as summaries, trends,and alarms, derived from a sequence of relatedevents, the Gryphon model supports several“stateful” operations as well (operations whoseresults depend on the event history). State can alsobe used to express the “meaning” of an eventstream, and by implication, the equivalence of twoevent streams.

In this paper, we describe Gryphon’s approach to eventdistribution middleware based on the concept ofinformation flow graphs (IFGs). We show that IFGs notonly are a flexible and powerful model for expressingevent flows, but also can be efficiently implemented on adistributed network of event brokers.

Section 2 defines the IFG model. The motivations forcontent-based subscription, and efficient and scalablealgorithms developed by the Gryphon project for matchingevents to subscriptions and delivering them are omittedhere, since they are discussed in detail in [1] and [3].Section 3 introduces motivating examples of stateless andstateful event transformations. Section 4 discusses theimplementation problems of the IFG approach, and thenpresents an overview of two implementation techniques wehave developed to address these problems. Section 5discusses related work, the current status of this work, andconcludes.

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