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A middleware platform_to_federate_complex_event_processing
- 1. A Middleware Platform to Federate Complex Event Processing Fawaz Paraïso, Gabriel Hermosillo, Romain Rouvoy, Philippe Merle, Lionel Seinturier The Sixteenth IEEE International EDOC Conference (2012) University of Lille & Inria lille-Nord Europe (France) 1
- 2. Agenda • Motivation • Challenges • Contribution • Validation • Conclusion & Perspectives 2
- 3. Motivation • What do we mean by event? – A piece of data that represents something happened in the real world • Event-driven behaviour in daily life – Computer – Systems –… 3
- 4. Motivation • Events are everywhere Produce events 4
- 5. Motivation • Events are useless if they are not filtered and correlated Processing Events 5
- 6. Motivation • What is Complex Event Processing (CEP)? – Real time processing – Intelligent business applications • What applications can benefit from CEP? – Real-time supply chain management – Algorithm trading – Monitoring (transaction, network, …) – Credit card fraud detection 6
- 7. Motivation • The need for real-time processing of information is relevant for many systems – Business activity monitoring – Fraud detection – Nuclear crisis management 7
- 8. National Motivation Radiation Survey Network Weather Forecast Experts Police Firemen Population Media Nuclear Central Emergency Army Medical Service Operation Decision Local authority 8
- 9. Agenda • Motivation • Challenges • Contribution • Validation • Conclusion & Perspectives 9
- 10. Challenges • Challenge 1: Communication heterogeneity 10
- 11. Challenges • Challenge 2: Heterogeneous CEP Engines StreamCruncher Esper Etalis CEP ruleCore Server 11
- 12. Challenges • Challenge 3: Scalability Performance & Scalability 12
- 13. Challenges • Challenge 4: Adaptability 13
- 14. Agenda • Motivation • Challenges • Contribution • Validation • Conclusion & Perspectives 14
- 15. Contribution • A Middleware Platform to Federate Complex Event Processing – Federate distributed CEP Engines – Supports multiple communication services REST, JMS, WS-Notification – The DiCEPE Platform is an SCA-based solution – Implemented in SCA using FraSCAti Reflective component model Runtime adaptative system 15
- 16. Contribution • Distributed Platform Architecture DiCEPE DiCEPE DiCEPE 16
- 17. Contribution • Platform Architecture * Listener Binding Rest * Binding Engine Statement JMS Context DiCEPE Legend Composite Component Service Reference Property Wire 17
- 18. Contribution • Platform Architecture – Communication heterogeneity Orchestrate heterogenenous services Different bindings REST, WS, JMS, JNA, UPnP, RPC ,RMI, JGroups, etc. – Reconfiguration capability Dynamic reconfigurable runtime architecture 18
- 19. Contribution • Platform architecture – Facililate the integration of CEP engine Compose an heterogenous piece of software to build a new service Supports various Implementation technologies (Java, BPEL, C, C++, Python, …) Interface definiton language (WSDL, Java) 19
- 20. Contribution • The Platform adresses the challenges of : – Communication heterogeneity – Heterogeneous CEP – Scalability – Adaptability 20
- 21. Agenda • Motivation • Challenges • Contribution • Validation • Conclusion & Perspectives 21
- 22. Validation • DiCEPE for nuclear crisis management Available here: http://dicepe-broker.soceda.cloudbees.net 22
- 23. Validation • The SCA validates the challenge: – Communication heterogeneity – Heterogeneous CEP – Scalability – Adaptability 23
- 24. Validation • Integration with the Esper and Etalis engine Input 1 Events EventExecutionWorker 1 2 EsperServiceProvider Configuration 3 EPL 4 4 Event object statement 2 Statements PrologEngineWrapper Listeners 5 3 Etalis Output EtalisEventListener Prolog Events Overview of Esper Engine Architecture EtalisWrapper 5 3 4 DiCEPE 4 Architecture 1 2 3 1 2 24
- 25. Validation • The integration of Esper and Etalis CEP engine validates the challenge : – Communication heterogeneity – Heterogeneous CEP – Scalability – Adaptability 25
- 26. Validation • DiCEPE Cost Analysis Implementation Avg. Exec. Time SCA overhead Esper 27 sec - DiCEPE (Esper+ FraSCAti) 30 sec 11% 26
- 27. Validation • DiCEPE Scalability Firemen Events Failures Avg. Sessions Avg. response 10,000 500,000 0 89 0.113 ms 15,000 750,000 0 135 0.142 ms + 50% + 50% - + 51% + 26% 27
- 28. Validation • The scalability analysis validates the challenge – Communication heterogeneity – Heterogeneous CEP – Scalability – Adaptability 28
- 30. Validation • The FraSCAti validates the challenge: – Communication heterogeneity – Heterogeneous CEP – Scalability – Adaptability 30
- 31. Agenda • Motivation • Challenges • Contribution • Validation • Conclusion & Perspectives 31
- 32. Conclusion & Perspectives • DiCEPE offers interoperability between CEP engines via federation • Flexible component architecture – Successful integration and validation of CEP engines – Multiple communication protocols • Real scalability • Integrate a Domain Specific Language(DSL) to express rules • Deployment of DiCEPE on heterogeneous cloud environments • Error handling capabilities for distributed environments 32
- 33. Thank you Questions? @email: [email protected] 33