S-CUBE LP: Context-aware Adaptation of Business Processes
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

S-CUBE LP: Context-aware Adaptation of Business Processes

on

  • 782 views

 

Statistics

Views

Total Views
782
Views on SlideShare
613
Embed Views
169

Actions

Likes
0
Downloads
8
Comments
0

1 Embed 169

http://vc.infosys.tuwien.ac.at 169

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

S-CUBE LP: Context-aware Adaptation of Business Processes Presentation Transcript

  • 1. S-Cube Learning Package Self-adaptation:Context-aware Adaptation of Business Processes Fondazione Bruno Kessler (FBK) Antonio Bucchiarone, FBK www.s-cube-network.eu
  • 2. Learning Package Categorization S-Cube Adaptation and Monitoring Self-adaptation Context-aware Adaptation of Business Processes © Antonio Bucchiarone
  • 3. Learning Package Overview Problem Description Context-aware Adaptation Evaluation and Discussion Related Work Conclusions © Antonio Bucchiarone
  • 4. Let’s Consider a Scenario (1)  Logistics Domain  At the Automobile terminal of the Bremen sea port  2 million new vehicles are handled each year and the goal is: “to deliver them from the manufacturer to the dealer”  For each vehicle a lot of intermediate processes/services are involved © Antonio Bucchiarone
  • 5. Let’s Consider a Scenario (2) A Business Process is implemented to handle the delivery of cars from ships to retailers Each Business Process is realized as an orchestration of appropriate services (atomic and composite) – Services are asynchronous and non-deterministicUnloading Car Check Move To Loading Move ToService Service Consignment Service Storage Painting Equipment Service Service Service Service Ship Technical Truck Storage Consignment Unloading Treatment Loading Store Car Move To Cleaning Service Treatment Service Service Other Available Services Pull To Car Repair Drop Ticket Treatment Service Service Service Move To Wait For Request Move To Move To Terminal Treatment Ticket Storage Place Service Service Service Service Service © Antonio Bucchiarone
  • 6. Let’s Consider a Scenario (3) The execution environment of the scenario exposes some important characteristics, it is: – Open: externally forced situations such as “car damage” or “storage overbooking” can happen; – Dynamic: a set of available services and active business policies are changing – Non-deterministic: many services with unpredictable outcome © Antonio Bucchiarone
  • 7. Let’s Consider a Scenario (4) The scenario allow us to manage two important problems – Context-aware adaptation: If the car is damaged, a corresponding adaptation depends on the current context (i.e., current location of the car) - Ex. 1: If the car is on the ship, it still has to be unloaded and afterwards pulled to the repair area and repaired - Ex. 2: if the car is damaged anew immediately after repair, the Repair Service can be applied to it immediately (since the car is already at the repair area) – Scalability in dynamic environment: the operation of the repair area is temporarily suspended, this means that the Slow Repair service is temporarily removed from the list of available services - Ex. 1: to enable car repair, mobile repair groups are organized. They repair broken cars on the spot, without pulling them to repair area. A new service called Fast Repair is introduced. © Antonio Bucchiarone
  • 8. Learning Package Overview Problem Description Context-aware Adaptation Evaluation and Discussion Related Work Conclusions © Antonio Bucchiarone
  • 9. Key Elements Explicit Model of the Application – Context Model: We consider context as a set of context properties representing important characteristics of the environment (e.g., car location, car operability, Storage Availability, etc..) – Service Model: we consider asynchronous and non-deterministic services and we provide a way to model their protocols. Context-aware Execution Framework – We define a reference architecture to execute adaptation of business processes considering the available services and the current context configuration Context-aware Adaptation based on Automated Service Composition – The construction of the adaptation solution (service composition) is performed with the use of automated planning techniques © Antonio Bucchiarone
  • 10. Context Property Diagram (1) Every context property is modeled with a context property diagram, which is a state transition system capturing all possible property values and value changes. Each transition is additionally labeled with a context event Research work on context property diagrams: Piergiorgio Bertoli, Raman Kazhamiakin, Massimo Paolucci, Marco Pistore, Heorhi Raik, Matthias Wagner: Control Flow Requirements for Automated Service Composition. ICWS 2009: 17-24 Raman Kazhamiakin, Massimo Paolucci, Marco Pistore, Heorhi Raik: Modelling and Automated Composition of User-Centric Services. OTM Conferences (1) 2010: 291-308 © Antonio Bucchiarone
  • 11. Context Property Diagram (2) Context Property diagrams in the Car Logistics scenario • It captures how the car location can change over time • Initially the car is on the ship • The repair location is where the car can be repaired • It represents can operability status • When the car is damaged it is in the status nok © Antonio Bucchiarone
  • 12. Annotated Services (1) In order to model complex service protocols (e.g., specified in Abstract BPEL), we use state transition systems, where: – Transitions correspond to service actions (input/output messages). – Each transition can be additionally annotated with context effects and context preconditions. – Some non-deterministic outcomes can be labeled as improbable © Antonio Bucchiarone
  • 13. Annotated Service(2) Annotated Service in the Car Logistics scenario It is applicable only to an operable car This outcome is also labeled as improbable Its non-deterministic outcomes are annotated with two different effects © Antonio Bucchiarone
  • 14. Automated Adaptation do we need to adapt? is the adaptation strategy? to implement the chosen strategy? © Antonio Bucchiarone
  • 15. Execution Framework (1)• Execution of the Monitoring and Process Contextprocess activities Updating of• Invocation of Engine Manager Contextavailable services (S) Properties (C) o i x Execution of M, S, C the business Execution Engine process MDetection of adaptationproblems (ξ); ξ Madapt Adaptation Process derivation (Madapt) Adaptor © Antonio Bucchiarone
  • 16. Execution Framework (2) Ship Technical Truck Storage Consignment Unloading Treatment Loading Annotated Servicesthe next action in the processcannot be executed becauseits precondition is violated Preconditions and Effects External Context Events Properties © Antonio Bucchiarone
  • 17. Execution Framework (3) Ship Technical Truck Storage Consignment Unloading Treatment Loading Annotated ServicesLocal adaptation S1 S2 S3 Preconditions Effects S4 S5 Context Properties External Events
  • 18. Adaptation Problem Adaptation problems sent to the Adaptor component comprises: – The current status of the system (values of the context properties, states of the services involved). – A set of available services that may be used for adaptation. – The adaptation goal: “to unblock the process”, i.e. to satisfy the precondition of the next activity in the process. © Antonio Bucchiarone
  • 19. Adaptation as Planning Problem • An adaptation problem is transformed into a planning problem and planning techniques are used to generate the adaptation process Adaptation Problem © Antonio Bucchiarone
  • 20. Adaptation as Planning Problem obtaining the Adaptation Process (Madapt) A set of n services and m context property diagrams are transformed into state transition systems (STS) While encoding services as STSs, we prohibit all “improbable” outcomes. We guarantee that the plan will be build under the assumption that no exogenous events and service failures happen during the generation of the adaptation process. The planning domain Σ is a product of all STSs of the annotated services and context property diagrams synchronized on preconditions and effects. Σ The set of goal configurations G(ξ) is transformed into set of configurations G of planning domain Σ . We denote the planning goal as a reachability goal After all, we apply an automatic composition approach to domain Σ and planning goal ρ and generate a controller Σc (plan), such that (domain Σ reaches goal ρ when controlled by Σc). The state transition system Σc is further translated into executable process Madapt, which implements the adaptation strategy. © Antonio Bucchiarone
  • 21. Adaptation as Planning Problem obtaining the Adaptation Process (Madapt) We exploit the ASTRO automated composition approach - www.astroproject.org  Sophisticated AI planning techniques (Planning as Model Checking)  Asynchronous domains, non-determinism, partial observability  Complex goals: preferences and recovery conditions (EaGle)  Control and data flow composition requirements Research work on ASTRO automated service composition: Annapaola Marconi, Marco Pistore: Synthesis and Composition of Web Services. SFM 2009: 89-157 Annapaola Marconi, Marco Pistore, Paolo Traverso: Automated Composition of Web Services: the ASTRO Approach. IEEE Data Eng. Bull. 31(3): 23-26 (2008) Annapaola Marconi, Marco Pistore, Piero Poccianti, Paolo Traverso: AutomatedWeb Service Composition at Work: the Amazon/MPS Case Study. ICWS 2007: 767-774. Annapaola Marconi, Marco Pistore, Paolo Traverso: Specifying Data-Flow Requirements for the Automated Composition of Web Services. SEFM 2006: 147-156 M. Pistore, P. Traverso, P. Bertoli, and A. Marconi, Automated synthesis of composite BPEL4WS web services” in Proc. ICWS 2005
  • 22. Learning Package Overview Problem Description Context-aware Adaptation Evaluation and Discussion Related Work Conclusions © Antonio Bucchiarone
  • 23. Evaluation (1) We implemented our approach into a prototype tool.  To analyze the adaptation modeling overhead, and to compare the approach with other approaches (i.e., rule-based)  For the planning problem we use a customized NuSMV language, extended to allow the specification of goals with preferences.  The planning problem is then passed to WSYNTH, one of the tools in the ASTRO toolset.  The adaptation process returned by WSYNTH is the controlled domain which we then translate back to BPEL. To evaluate our tool, we implemented a complex logistics scenario  Its formalization includes 16 context property diagrams, 24 annotated stateful services and a dozen of business policies  Evaluation focused on three main points: general structure and complexity of adaptation activities, planning performance and modelling overhead. © Antonio Bucchiarone
  • 24. Evaluation (2) adaptation complexity Superficial analysis of the complex logistics scenario unveils at least 30 cases that require adaptation. Adaptation processes typically has more than 8 actions, while several adaptation cases, due to non-determinism, require exceedingly complex adaptation involving more than 60 actions Using built-in adaptation in such scenario is virtually impossible © Antonio Bucchiarone
  • 25. Evaluation (2) planning performance The performance of the planning algorithm was tested on a 2GHz, 3Gb Dual Core machine running Windows. In our experiments, the delay caused by planning and adaptation process never exceeded 4 seconds. Given the high complexity of the scenario, it demonstrates The practical applicability of our approach
  • 26. Evaluation (2) modeling overhead In order to evaluate the modeling effort of our approach we implemented the same adaptation mechanism using a rule-based approach. We defined 35 rules and verified the whole rule-based system. In order to compare scalability of our approach respect to rule-based, we simulated the replacement of a service by a new one with the same functionality but different usage policies.  Our approach required only proper annotation of a new service  The rule-based implementation required modifying 3 rules and re- verifying the whole rule system. Our approach proved to be considerably more scalable than rule-based and built-in
  • 27. Discussion Comparison to Rule-based approaches Research problem:  Self-adaptation Solution proposed:  Adaptation of business processes using a Context-aware Re-Planning technique Comparison to rule-based and built-in approaches:  Using built-in adaptation with complex scenario is virtually impossible  The performance of the planning algorithm demonstrates the applicability of our approach (<4 seconds to generate an adaptation solution) while re- verifying the whole rule-based system means run the analyzer for some hours (> 1 hour).  Our approach proved to be considerably more scalable than rule-based and built-in © Antonio Bucchiarone
  • 28. Learning Package Overview Problem Description Context-aware Adaptation Evaluation and Discussion Related Work Conclusions © Antonio Bucchiarone
  • 29. Related Work Frameworks to support adaptation of business processes D. Karastoyanova, A. Houspanossian, M. Cilia, F. Leymann, and A. P. Buchmann. Extending BPEL for Run Time Adaptability. In Proc. EDOC’05, pages 15–26, 2005. A. Marconi, M. Pistore, A. Sirbu, H. Eberle, F. Leymann, and T. Unger. Enabling Adaptation of Pervasive Flows: Built- in Contextual Adaptation. In Proc. ICSOC/ServiceWave, pages 445–454, 2009. M. Colombo, E. di Nitto, and M. Mauri. SCENE: A Service Composition Execution Environment Supporting Dynamic Changes Disciplined Through Rules. In Proc. ICSOC’06, pages 191–202, 2006. I. Lanese, A. Bucchiarone, and F. Montesi. A Framework for Rule-based Dynamic Adaptation. In Proc. TGC 2010, pages 284–300, 2010. X. Yong Lin and W. Jun. Context-Driven Business Process Adaptation for Ad Hoc Changes. In Proc. IEEE ICEBE’08, pages 53–60, 2008. V. Agarwal and P. Jalote. From Specification to Adaptation: An Integrated QoS-driven Approach for Dynamic Adaptation of Web Service Compositions. In Proc. ICWS, pages 275–282, 2010. G. Hermosillo, L. Seinturier, and L. Duchien. Using Complex Event Processing for Dynamic Business Process Adaptation. In Proc. IEEE SCC, pages 466–473, 2010. W. Kongdenfha, R. Saint-Paul, B. Benatallah, and F. Casati. An Aspect-Oriented Framework for Service Adaptation. In Proc. ICSOC’06, pages 15–26, 2006. A. Hallerbach, T. Bauer, and Manfred Reichert. Capturing variability in business process models: the Provop approach. Journal of Software Maintenance, 22(6-7):519–546, 2010. M. de Leoni. Adaptive Process Management in Highly Dynamic and Pervasive Scenarios. In Proc. YR-SOC, pages 83–97, 2009. © Antonio Bucchiarone
  • 30. Learning Package Overview Problem Description Context-aware Adaptation Evaluation and Discussion Related Work Conclusions © Antonio Bucchiarone
  • 31. Conclusions We a presented a novel approach to adapt business processes where:  running application and the adaptation logic are two separate components  Adaptation activities are generated at runtime, when a problem arises (i.e., Dynamic Adaptation) Future Work  To extend the framework to consider also abstract tasks of the business processes  The refinement of an abstract activity with executable service composition is done automatically and at runtime taking into account the current status of the execution environment.  We plan to consider other adaptation strategies, e.g., to roll the execution back to a branching point in an attempt to take different branches.  We plan to use the execution history of adapted instances as a training set to progressively improve the process model (Process Evolution)
  • 32. Further ReadingA. Bucchiarone, M. Pistore, H. Raik, R. Kazhamiakin: Adaptation of Service-based Business Processes by Context-Aware Replanning. Submitted to SOCA 2011.R. Kazhamiakin, M. Paolucci, M. Pistore, H. Raik: Modelling and Automated Composition of User-Centric Services.OTM Conferences (1) 2010: 291-308A. Marconi, M. Pistore: Synthesis and Composition of Web Services. SFM 2009: 89-157P. Bertoli, R. Kazhamiakin, M. Paolucci, M. Pistore, H. Raik, M. Wagner: Control Flow Requirements for AutomatedService Composition. ICWS 2009: 17-24P. Bertoli, R. Kazhamiakin, M. Paolucci, M. Pistore, H. Raik, M. Wagner: Continuous Orchestration of Web Services viaPlanning. ICAPS 2009A. Marconi, M. Pistore, A. Sirbu, H. Eberle, F. Leymann, T. Unger: Enabling Adaptation of Pervasive Flows: Built-inContextual Adaptation. ICSOC/ServiceWave 2009: 445-454A. Bucchiarone, C. Cappiello, E. Di Nitto, R. Kazhamiakin, V. Mazza, M. Pistore: Design for Adaptation of Service-Based Applications: Main Issues and Requirements. ICSOC/ServiceWave Workshops 2009: 467-476H. Ehrig, C. Ermel, O. Runge, A. Bucchiarone, P. Pelliccione: Formal Analysis and Verification of Self-Healing Systems.FASE 2010: 139-153 © Antonio Bucchiarone
  • 33. Acknowledgements The research leading to these results has received funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] under grant agreement 215483 (S-Cube). © Antonio Bucchiarone