MANAGEMENT AND FEDERATION OF STREAM PROCESSING APPLICATIONS

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1 DISS. ETH NO MANAGEMENT AND FEDERATION OF STREAM PROCESSING APPLICATIONS A dissertation submitted to ETH ZURICH for the degree of Doctor of Sciences presented by MICHAEL ALEXANDER DULLER Master of Science ETH in Computer Science, ETH Zurich born July 29, 1981 citizen of Austria accepted on the recommendation of Prof. Gustavo Alonso Prof. Friedemann Mattern Prof. M. Tamer Özsu Prof. Nesime Tatbul 2011

2 Abstract A decade ago, stream processing has enabled a new class of applications by employing a fundamentally different processing model than conventional data base management systems. These applications process large volumes of continuous streams of input data with high throughput. Examples include network intrusion detection, financial transaction processing, and variable road tolling. Data stream management systems have evolved into industrial strength solutions for this class of applications and use long-running queries to process high volumes of continuous input data with low latency. However, they still lack flexibility in terms of large-scale deployment, integration, extensibility, and interoperability. In the last years, a substantial ecosystem of new applications has emerged that can potentially benefit from stream processing. They range from the federation of existing but heterogeneous streaming applications to automated deployments of streaming applications in large clusters or cloud environments to processing personal information like photos, , or text (SMS) messages as data streams. These applications introduce different requirements on how stream processing solutions can be deployed, integrated, extended, and federated. This thesis explores stream processing with the help of traditional stream processing applications as well as applications that process personal information as data streams. Despite or even because of the very different application use cases, the thesis identifies v

3 vi ABSTRACT the fundamental properties that are common to all stream processing systems. The result is a generic model for stream processing and an architecture for a dynamic platform that supports the model. The model separates processing (operators) and data management (buffers) into distinct entities. Stream processing systems that have been implemented conforming to the model, including existing systems that have been ported, benefit from deployment and runtime support by the platform. They also support basic interfaces for data exchange. Intermediate and final results are made available in the model s explicit entities for data management. These properties enable the automated deployment of applications, facilitate the federation of applications running on heterogeneous stream processing systems, and leverage stream processing in new application domains. This thesis validates the generality of the model, the feasibility in terms of overhead, and the claims made in terms of deployment and integration. Experiments on PlanetLab, on a cluster, and on individual nodes confirm that the model and platform proposed in the thesis enable the interoperability between heterogeneous stream processing engines, facilitate the distributed deployment, and add functionality to the engines (ability to replace operators at runtime or to run a distribution-agnostic engine in a distributed setup) with negligible overhead.

4 Zusammenfassung Vor zehn Jahren hat Stream Processing eine neue Art von Anwendungen ermöglicht, indem es ein von konventionellen Datenbanksystemen fundamental verschiedenes Verarbeitungsmodell einsetzt. Diese Anwendungen verarbeiten grosse Mengen von kontinuierlichen Datenströmen mit hohem Durchsatz. Beispiele solcher Anwendungen sind das Erkennen von Einbrüchen in Netzwerke, das Verarbeiten von Finanztransaktionen und das Berechnen variabler Strassenbenutzungsgebühren. Stream Processing-Systeme haben sich zu industrietauglichen Lösung für diese Art von Anwendungen entwickelt und benutzen im System ruhende Abfragen um grosse Mengen von fortlaufenden Eingabedaten mit niedriger Latenz zu verarbeiten. Diesen Systemen fehlt allerdings die nötige Flexiblität bezüglich Einsatz im grossen Massstab, Integration, Erweiterbarkeit und Interoperabilität. In den vergangenen Jahren entstand ein umfangreiches Ökosystem neuer Anwendungen, die potentiell von Stream Processing profitieren könnten. Diese reichen vom Zusammenschluss von verschiedenartigen Stream Processing-Anwendung über den automatischen Einsatz von Stream Processing-Anwendungen in grossen Clusteroder Cloud-Umgebungen bis zur Verarbeitung von persönlichen Daten wie Fotos, oder SMS Nachrichten als Dataströme. Diese Anwendungen stellen verschiedenste Anforderungen, wie Stream Processing-Lösungen eingesetzt, integriert, erweitert und zusammengeschlossen werden können. vii

5 viii ZUSAMMENFASSUNG Diese Dissertation untersucht Stream Processing sowohl anhand seiner traditionellen Anwendungen als auch anhand der Anwendungen, die persönliche Daten als Datenströme verarbeiten. Trotz oder gerade wegen dieser sehr verschiedenen Anwendungsfälle identifiziert diese Dissertation die fundamentalen Eigenschaften, die allen Stream Processing-Systemen zugrunde liegen. Das Ergebnis ist ein generisches Modell für Stream Processing und eine Architektur für eine dynamische Plattform, die das Modell unterstützt. Das Modell trennt Verarbeitung (Operatoren) und Verwaltung (Puffer) von Daten in eigene Einheiten. Stream Processing-Systeme, die dem Modell entsprechend implementiert wurden, einschliesslich existierender System, die portiert wurden, profitieren von Unterstützung durch die Plattform beim initialen Einsatz und während der Laufzeit. Sie unterstützen zudem grundlegende Schnittstellen für den Austausch von Daten. Zwischen- und endgültige Resultate werden explizit verfügbar gemacht in den Einheiten für Datenverwaltung des Modells. Diese Eigenschaften ermöglichen den automatischen Einsatz von Anwendungen, erleichtern den Zusammenschluss von Anwendungen, die auf verschiedenartigen Stream Processing-Systemen laufen und verhelfen Stream Processing in neuen Anwendungsbereichen zum Durchbruch. Diese Dissertation bestätigt die Allgemeingültigkeit des Modells, die Umsetzbarkeit bezüglich zusätzlichem Aufwand und die Ansprüche bezüglich erleichtertem Einsatz und Integration. Experimente auf PlanetLab, auf einem Cluster und auf individuellen Maschinen bestätigen, dass das vorgeschlagene Modell und die Plattform den Zusammenschluss von verschiedenartigen Stream Processing-Systemen ermöglichen, den verteilten Einsatz erleichtern und zusätzliche Funktionalität (Fähigkeit Operatoren zur Laufzeit zur ersetzen oder ein nicht-verteiltes Stream Processing-System in einer verteilten Konfiguration auszuführen) bringen bei vernachlässigbarem zusätzlichem Aufwand.

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