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-deterministic
Unloading Car Check Move To Loading
Move To
Service 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 Context
process activities Updating of
• Invocation of Engine Manager Context
available services (S) Properties (C)
o i x
Execution of
M, S, C the business
Execution Engine process M
Detection of adaptation
problems (ξ); ξ Madapt
Adaptation Process
derivation (Madapt)
Adaptor
© Antonio Bucchiarone

16. Execution Framework (2)
Ship Technical Truck
Storage Consignment
Unloading Treatment Loading
Annotated
Services
the next action in the process
cannot be executed because
its 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
Services
Local 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 Reading
A. 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: 291-308
A. Marconi, M. Pistore: Synthesis and Composition of Web Services. SFM 2009: 89-157
P. Bertoli, R. Kazhamiakin, M. Paolucci, M. Pistore, H. Raik, M. Wagner: Control Flow Requirements for Automated
Service Composition. ICWS 2009: 17-24
P. Bertoli, R. Kazhamiakin, M. Paolucci, M. Pistore, H. Raik, M. Wagner: Continuous Orchestration of Web Services via
Planning. ICAPS 2009
A. Marconi, M. Pistore, A. Sirbu, H. Eberle, F. Leymann, T. Unger: Enabling Adaptation of Pervasive Flows: Built-in
Contextual Adaptation. ICSOC/ServiceWave 2009: 445-454
A. 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: 467-476
H. 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