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Attributed Graph Rewriting for Complex Event Processing Self-Management

Authors:

Wilson A. Higashino,

Cédric Eichler,

Miriam A. M. Capretz,

Luiz F. Bittencourt,

Thierry MonteilAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 11, Issue 3

Article No.: 19, Pages 1 - 39

https://doi.org/10.1145/2967499

Published: 20 September 2016 Publication History

Abstract

The use of Complex Event Processing (CEP) and Stream Processing (SP) systems to process high-volume, high-velocity Big Data has renewed interest in procedures for managing these systems. In particular, self-management and adaptation of runtime platforms have been common research themes, as most of these systems run under dynamic conditions. Nevertheless, the research landscape in this area is still young and fragmented. Most research is performed in the context of specific systems, and it is difficult to generalize the results obtained to other contexts. To enable generic and reusable CEP/SP system management procedures and self-management policies, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism. AGeCEP represents queries in a language- and technology-agnostic fashion using attributed graphs. Query reconfiguration capabilities are expressed through standardized attributes, which are defined based on a novel classification of CEP query operators. By leveraging this representation, AGeCEP also proposes graph rewriting rules to define consistent reconfigurations of queries. To demonstrate AGeCEP feasibility, this research has used it to design an autonomic manager and to define a selected set of self-management policies. Finally, experiments demonstrate that AGeCEP can indeed be used to develop algorithms that can be integrated into diverse CEP systems.

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Cited By

Qin CEichelberger HSchmid K(2019)Enactment of Adaptation in Data Stream Processing with Latency Implications—A Systematic Literature ReviewInformation and Software Technology10.1016/j.infsof.2019.03.006Online publication date: Mar-2019
https://doi.org/10.1016/j.infsof.2019.03.006
Matko VBrezovec B(2018)Improved Data Center Energy Efficiency and Availability with Multilayer Node Event ProcessingEnergies10.3390/en1109247811:9(2478)Online publication date: 18-Sep-2018
https://doi.org/10.3390/en11092478
Higashino WCapretz MBittencourt L(2017)CEPaaS: Complex Event Processing as a Service2017 IEEE International Congress on Big Data (BigData Congress)10.1109/BigDataCongress.2017.31(169-176)Online publication date: Jun-2017
https://doi.org/10.1109/BigDataCongress.2017.31

Index Terms

Attributed Graph Rewriting for Complex Event Processing Self-Management

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cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 11, Issue 3

September 2016

117 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/3000604

Editors:
Manish Parashar
Rutgers University, USA
,
Franco Zambonelli
University of Modena e Reggio Emilia, Italy

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Copyright © 2016 ACM.

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Publication History

Published: 20 September 2016

Accepted: 01 June 2016

Revised: 01 June 2016

Received: 01 January 2016

Published in TAAS Volume 11, Issue 3

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Cited By

Qin CEichelberger HSchmid K(2019)Enactment of Adaptation in Data Stream Processing with Latency Implications—A Systematic Literature ReviewInformation and Software Technology10.1016/j.infsof.2019.03.006Online publication date: Mar-2019
https://doi.org/10.1016/j.infsof.2019.03.006
Matko VBrezovec B(2018)Improved Data Center Energy Efficiency and Availability with Multilayer Node Event ProcessingEnergies10.3390/en1109247811:9(2478)Online publication date: 18-Sep-2018
https://doi.org/10.3390/en11092478
Higashino WCapretz MBittencourt L(2017)CEPaaS: Complex Event Processing as a Service2017 IEEE International Congress on Big Data (BigData Congress)10.1109/BigDataCongress.2017.31(169-176)Online publication date: Jun-2017
https://doi.org/10.1109/BigDataCongress.2017.31

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