Part 1:
Overview of S-Cube
Part 2:
Service engineering research in S-Cube and links with
industrial use cases
Part 3:
Collaboration opportunities and wrap-up
Outline
 Details on the S-Cube Integrated Research
Framework
 Research Areas
– Service Engineering
– Cross-layer Adaptation
– End-to-end Quality Provision
 Case studies: a methodological approach
Details on the IRF
S-Cube Integrated Research Framework
 S-Cube focuses on long-term research
 Main research focus:
– Software service and systems
– Adaptivity and evolution
- of services
- of agile service networks
 S-Cube developed an Integrated Research Framework
– 4 views on research issues
 S-Cube evolved a methodology for case studies
documentation
Integrated Research Framework Views
 Conceptual Research Framework
 Reference Life Cycle
 Logical Run Time Architecture
 Logical Design Environment
Integrated Research Framework
Conceptual Research Framework
Integrated Adaptation and Monitoring Capabilities
Adaptation and Monitoring Specifications
Local A&M
Capabilities
A&M
Specifications
Local A&M
Capabilities
A&M
Specifications
Local A&M
Capabilities
Service
Composition
& Coordination
(SCC)
Service
Infrastructure
(SI)
Local QDNA
Capabilities
Monit. Spec.
Monit. Capab.
QDNA Spec.
QDNA Capab.
A&M
Specifications
Business
Process
Management
(BPM)
Local Design
Capabilities
Engineering
and
Design
(SED)
Design
Specifications
Local Design
Capabilities
Design
Specifications
Local Design
Capabilities
Design
Specifications
QDNA
Specifications
Monitoring
(SAM)
QDNA
Specifications
and
Integrated QDNA
Capabilities
Adaptation
Quality Definition, Negotiation & Assurance
(SQDNA)
© S-Cube – 6
Integrated Research Framework
Reference Life-Cycle
Early requirements
engineering
Requirements
engineering
& design
Identify
adaptation
need
Identify
adaptation
strategy
Enact
adaptation
Operation &
management
Construction
Deployment &
provisioning
© S-Cube – 7
Service
composition
Human
provided
service
Resource
Broker
Software
service
Human
Service
Interface
Service
Container
Integrated Research Framework
Logical Run-Time Architecture
Resource
Resource
Test cases,
run-time
models, …
Discovery
and Registry
Infrastr.
Negotiation
logic
Run-time QA
Engine
Monitoring
logic
Negotiation
Engine
Monitoring
logic
Adaptation
Engine
Monitoring
Engine
Communication backbone
`
Core services
Application-specific
components
Management
interface
© S-Cube – 8
Integrated Research Framework
Logical Design Environment
• BPEL
PPM Modeller
A&M Modeller
Service
Modellers
QoS Modeller
A&M
Configurators
Monitoring transformations and code generation
SC&C Modellers
SLA Modellers
Service
Infrastructure
Service
Composition &
Coordination
A&M Modeller
Adaptation transformation and code generation
BPM Monitoring
Deployment
Service
Compositions
Deployment
SC&C Monitoring
& Adaptation
Deployment
Verification
BPM
Verification
KPI Modeller
ASN and BPMN
Deployment
Quality properties translformation
• PPM ↔ QoS
• ASN
• Choreography
(BPMN)
Quality properties translformation
• KPI ↔ PPM
BPM ↔ SC&C transformation
• ASN+BPMN ↔ Service Choreography
• Service Choreography ↔ Orchestration
Business
Process
Management
BPM Modellers
Deployment
SC
Verification
Transformation
and Generation
Modelling
Service
Deployment
© S-Cube – 9
Research Areas in Service
Engineering
Service Engineering
for Service-Based Applications (SBA)
www.s-cube-network.eu
Composition &
Coordination
(SCC)
Infrastructure
(SI)
Engineering & Design
(SED)
Adaptation & Monitoring
(SAM)
Business
Process
Mgt. (BPM)
Quality Definition, Negotiation & Assurance
(SQDNA)
© S-Cube – 3/#
Adaptation design
 Focus on activation of adaptation strategies
 Instance-level adaptation
 Context
 Model-based
Main Ingredients of an Adaptable SBA [6]
Bucchiarone, C. Cappiello, E. di Nitto, R. Kazhamiakin, V. Mazza, and M. Pistore,
“Design for adaptation of Service-Based applications: Main issues and requirements,” in WEOSA 2009
Life-cycle for SBAs
WESOA, 2009
Adaptation Strategies
 To mantain aligned the application behaviour with the context
and system requirements
– Service substitution
– Re-execution
– (Re-)negotiation
– (Re-)composition
– Compensation
– Log/Update adaptation Information
– Fail
WESOA, 2009
Adaptation Triggers
 The adaptation may be motivated by a variety of triggers
 Component Services
– Service functionality
– Service quality
 SBAs context
– Business context
– Computational context
– User context
WESOA, 2009
Adaptation Strategies & Triggers
 To re-align the application within the system and/or context
requirements
 Each trigger can be associated with a set of adaptation
strategies
WESOA, 2009
Design Guidelines for triggers and adaptation
strategies
 Design adaptable SBAs implies relate adaptation triggers and
adaptation strategies together
– Modeling adaptation triggers
– Realizing adaptation strategies
– Associating adaptation strategies to triggers
 Design approaches
– Built-in adaptation
- Adaptation needs and adaptation configuration known a priori
– Abstraction-based adaptation
- Adaptation need fixed, but adaptation configuration not known a
priori
– Dynamic adaptation
- Adaptation needs not known at design time or cannot be
enumerated
WESOA, 2009
Designing reliable
service compositions
 Focus on service compositions
 To design more reliable service-based processes
– inserting monitors, adaptation strategies, changing process structure,
…
– Which is the best choice?
 Context-awareness (user-dependent)
 Based on quality evaluation
Cappiello, C.; Pernici, B.: QUADS: Quality-Aware Design of dependable Service-based
processes. 2010
Cappiello, 2010
Preventive and Corrective strategies
Cappiello, 2010
 Quality evaluation
– According to quality evaluation techniques for service compositions
 Representing users
– Importance of each user
– Importance of quality dimensions for each user
Cappiello, 2010
Comparing strategies
Domain specific!
Evaluating alternative strategies for a service
Example: Redundancy selected
• additional quality constraints for redundant services
are derived at design time
• basis for service selection at run time
Cross-Layer Adaptation
www.s-cube-network.eu
Composition &
Coordination
(SCC)
Infrastructure
(SI)
Engineering & Design
(SED)
Adaptation & Monitoring
(SAM)
Business
Process
Mgt. (BPM)
Quality Definition, Negotiation & Assurance
(SQDNA)
© S-Cube – 3/#
WP Vision
Key Challenges
 Comprehensive and integrated adaptation and monitoring
principles, techniques, and methodologies
– Across SBA layers, across SBA boundaries, across SBA life-cycle
 Context- and HCI-aware A&M
– Improve SBA adaptation based on the contextual knowledge
 Mixed initiative SBA adaptation
– From self-adaptation to human-in-the-loop adaptation
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 4/#
WP Vision
Integrated A&M Framework
Conceptual Model:
 A&M taxonomy
Monitored
events
Instantiations:
detect
Monitoring
mechanisms
trigger
Adaptation
requirements
Adaptation
mechanisms
achieve
Adaptation
strategies
 Cross-layer A&M
 Proactive adaptation
 HCI and contextawareness
 Self-adaptation
realize
Conceptual architecture:
 Results from SotA
 Research results of SCube members
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 5/#
Key Results Achieved
Cross-layer Adaptation and Monitoring
REQUIREMENTS
Cross-layer integrated
monitoring mechanisms
Monitored
events
Means to identify
adaptation needs
across layers
Monitoring
mechanisms
Adaptation
effectiveness
Event
propagation and
alignment
Adaptation
requirements
Means to identify
adaptation strategies
across layers
Adaptation
compatibility
and integrity
Adaptation
strategies
Adaptation
coordination
Adaptation
mechanisms
Cross-layer integrated and
coordinated adaptation mechanisms
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 9/#
Cross-layer Adaptation and Monitoring
Integrated Monitoring Mechanisms
 Results
– Unify monitoring of business properties and low-level service properties
centered around service compositions at run-time [1]
- Instance and class properties in the same model (ASTRO)
- Rich notation for basic events and probes (Dynamo)
– Integrate run-time and design-time events monitoring [2]
- Monitoring of run-time properties and model changes (EMF events, from design
environment) in the same framework (WildCat model for monitored data)
– Multifactor Monitoring [3]
- Unified language for querying various factors: service behavior, service quality,
service context, service structure
- Formal model for the representation of queries: event calculus
- Automated translation rules for different factors
1.
Baresi, Guinea, Pistore, Trainotti. Dynamo+ASTRO: an Integrated Approach for BPEL Monitoring. In
ICWS 2009
2.
Morin, Ledoux, Ben Hassine, Chauvel, Barais, Jezequel. Unifying Runtime Adaptation and Design
Evolution In CIT 2009
3.
Zisman, Spanoudakis, Mahbub. A Monitoring Approach for Run-time Service Discovery. Under
submission.
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 31/#
Cross-layer Adaptation and Monitoring
Identification of Adaptation Needs
 Results
– Domain assumptions [1]
•External services
•User context
Domain
- Explicit encoding of domain assumptions
- Associate them to requirements
- Monitor assumption violations
- verify requirements at run time to trigger adaptation
Assumptions
•Non-functional req’s
•Functional req’s
– Replacement policies [2]
Requirements
SBA
•Composition (BPEL)
•Service protocols
•QoS models
- Explicit encoding of rules for service substitution
- Take into account the SBA execution point
- Consider variety of aspects: QoS, structural, behavior, context, changing
requirements
- Take into account availability of new services
1.
Gehlert, Bucchiarone, Kazhamiakin, Metzger, Pistore, Pohl: Exploiting Assumption-Based
Verification for the Adaptation of Service-Based Applications. SOA@SAC Conference, 2010
2.
Mahbub, Zisman. Replacement Policies for Service-Based Systems. MONA+ workshop,
ICSOC/ServiceWave conference, 2009
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 32/#
Cross-layer Adaptation and Monitoring
Identification of Adaptation Strategies
 Results
– Adaptation based on quality factor analysis
- Identify influential factors for KPI violations across layers: data mining techniques
- Identify adaptation requirements: analysis of the dependency trees
- Associate adaptation actions to basic SBA metrics and properties
- Identify adaptation strategy based on the effects of adaptation actions
Quality modeling
for analysis and
adaptation
Adaptation
Actions
Model
Process
adaptation
Adaptation
Actions
1.
Identification of
Adaptation
Strategies
Metrics
Model
Analysis of
influencing quality
factors
Adaptation
Requirements
Kazhamiakin, Wetzstein, Karastoyanova, Pistore, Leymann. SBA Adaptation based on quality factor
analysis . MONA+ Workshop, ICSOC/ServiceWave Conference 2009
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 33/#
Cross-layer Adaptation and Monitoring
Coordinated Adaptation
 Results
– Framework for self-supervising processes
- Generic adaptation framework with uniform and
extendable language for adaptation coordination
- Encoding of recovery actions
- Encoding of coordinated strategies
- Wide range of adaptation actions
- Service-level: ignore, halt, retry, rebind
- Process-level: change parameters, change
partner, restore, recovery subprocess
- Pluggable run-time architecture to
accommodate different adaptation types
- AOP techniques for extending process engine
functionalities
1.
Baresi, Guinea, Pasquale. Integrated and Composable Supervision of BPEL processes. ICSOC
Conference 2008
2.
Baresi, Guinea. Self-supervising BPEL processes. PoliMi TR 74.2009
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 34/#
End-to-End Quality Provision
www.s-cube-network.eu
Composition &
Coordination
(SCC)
Infrastructure
(SI)
Engineering & Design
(SED)
Adaptation & Monitoring
(SAM)
Business
Process
Mgt. (BPM)
Quality Definition, Negotiation & Assurance
(SQDNA)
© S-Cube – 3/#
Motivational „Scenario“
1. Identify
Vehicle
= service invocation/activity
= third-party service
= internal service
= alternative binding
[not valid]
ePay
response time: 2 s
cost: 1.50 €
SecurePay
[valid]
response
time: 2 s
2. Pay
Renewal Fee
response time: 3 s
cost: 1 €
3. Update Vehicle
Record
RenewalHandler
response
time: 1 s
Yahoo
response time: 1.5 s
GMail
4.a. E-Mail
Confirmation
4.b. Mail Validation
Sticker
response time: 2 s
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
response
time: 1 s
WP Vision
Key Challenges
Devise novel principles, techniques & methods for Quality...
...Definition [Mo1-45]
D
A
• End-to-End Quality Reference
Model (completed in Y1)
• Rich and Extensible Quality
Definition Language
• Run-time Quality
Assurance Techniques
• Quality Prediction
Techniques to Support
Proactive Adaptation
...Assurance [Mo18–45]
N
• Exploiting HCI
knowledge for
automatic quality
contract establishment
• Proactive SLA
negotiation and
agreement (from Y3)
...Negotiation [Mo10–45]
Key Results Achieved
Quality Reference Model
 Problems
– Understanding quality attributes across SOA layers
 Solution
– Quality Reference Model
 S-Cube Publications
1. NNN
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
Key Results Achieved
Knowledge Modelling
D
 S-Cube Quality Reference Model (QRM)
– Model containing 77 quality attributes in 10 categories
 Deliverable CD-JRA-1.3.2 [Mo 12]: “Quality reference model for SBAs”
Quality (77)
Dependability (20)
Performance (6)
Quality of (Use)
Context (9)
Usability (11)
Cost (3)
Network- &
SI-related (7)
Config. &
Mgt. (4)
Datarelated (7)
Security (9)
Other (1)
SLOs accessible via
KM!!!
A. Metzger / WP-JRA-1.3 – Month 12 Review Meeting, Essen, April 2009
© S-Cube – 9/18
Key Results Achieved
Knowledge Modelling
D
 S-Cube Quality Reference Model (QRM)
– Approach
- Data collection: describe most important quality models in disciplines
- Quality attributes analysis: identify relevant attributes
- Consolidation: synthesize S-Cube QRM from quality attributes / models
– Models Analyzed
- ISO Software Quality Model
Softw.
Eng.
- UML-Based Quality Models
- Statically Inferred QoS Attributes Model
- Design by Contracts Models
SOC
- Functional Quality in Service Composition Model
BPM
- Service Networks and KPIs Model
Grid
- Grid Quality Model
A. Metzger / WP-JRA-1.3 – Month 12 Review Meeting, Essen, April 2009
© S-Cube – 10/18
Exploiting HCI knowledge for automatic
quality contract establishment
D
N
 Problems
– Need for automated service contract negotiation (time can be critical factor)
– User interaction and experience impacts on negotiation
– Quality modelling languages offer limited capabilities to support automated negotiation
- Lack of formalization (hinders automation)
- Negotiation-related concepts missing; e.g., negotiatable vs. non-negotiatable attributes
- Missing shared terminology between provider and consumers
 Solution
– Quality meta-model (QMM) encompassing the concepts for a rich, extensible, and
semantically enriched quality definition language
– Automated negotiation techniques based on QMM concepts
– Considering codified UI aspects in QMM and negotiation techniques
 S-Cube Publications
1. Marco Comuzzi and Barbara Pernici. A Framework for QoS-Based Web service Contracting. In ACM Transactions on
the Web, 3(3), 2009
2. Marco Comuzzi, Kyriakos Kritikos and Pierluigi Plebani. Semantic-aware Service Quality Negotiation. In ServiceWave
2008
3. Marco Comuzzi, Kyriakos Kritikos and Pierluigi Plebani. A semantic based framework for supporting negotiation in
Service Oriented Architectures. In Proceedings IEEE CEC 2009
4. Kyriakos Kritikos and Dimitris Plexousakis. Mixed-Integer Programming for QoS-Based Web Service Matchmaking. In
IEEE Transactions on Services Computing, 2(2), 2009
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
Automatic quality contract establishment
Foundation: Quality Definition
D
N
 S-Cube Quality Meta Model (Excerpt) [1, 2, 3, CD-JRA-1.3.3]
– Based on Quality Reference Model [CD-JRA-1.3.2],
built from quality attributes relevant for each of the
layers ( JRA-2.1, JRA-2.2, JRA-2.3)
– Augmented by negotiation-related concepts
– Considering UI aspects; e.g., user models ( JRA-1.1)
Quality selection model: Specifies the
importance of each quality attribute for the
requester and how the best service should be
selected.
Requestor’s
Requirements
towards QoS
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
Automatic quality contract establishment
Foundation: Quality Definition
D
N
 S-Cube Quality Meta Model (Excerpt) [1, 2, 3, CD-JRA-1.3.3]
– Negotiation-related concepts
…
…
Negotiable: provider can fix the QoS value at execution time
(e.g., response time)
Non-negotiable: QoS value is pre-determined at execution
time (e.g., reputation)
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
Automatic quality contract establishment
Results: Negotiation Techniques
D
N

Framework for quality (QoS) contracting [1, CD-JRA-1.3.3]
–
(1) Phase 1: Matchmaking
-
-
–
(2) Phase 2: Selection
-
-
–
a) Service offer must cover the requirements on non-negotiable QoS
dimensions
b) Service offer must cover, at least partially, the requirements on negotiable
QoS dimensions expressed
c) Price associated to minimum quality profile  budget B of service requestor
Providers ranked by bidding function
- Maximize requirement coverage (maintaining price below B)
- Penalize services that only partially cover the requirements (utility function)
Provider of service associated to lowest bid L is selected
- Extra budget EB = B – L
(3) Phase 3: “Actual” Negotiation (if EB > 0)
-
Select for each negotiable QoS dimension a single QoS value
- Assuming EB should be spent (user model)
- Increase of QoS levels (order relation) based on priority of QoS attributes
(quality selection model)
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
Run-time Quality Assurance
Techniques



A
Problems
–
Design-time QA is not enough due to dynamic adaptation, context changes and open
nature of SBAs
–
Monitoring at run-time only checks “arbitrary” applications in operation (no systematic
coverage)
Solution
–
Understanding when in the life-cycle run-time verification should be performed
–
Exploiting consolidated design-time QA techniques (here: verification) during run-time
S-Cube Publications
1.
Domenico Bianculli, Carlo Ghezzi and Cesare Pautasso. Embedding Continuous Lifelong Verification in Service Life
Cycles. In Proceedings PESOS @ ICSE 2009
2.
Domenico Bianculli and Carlo Ghezzi. SAVVY-WS at a glance: supporting verifiable dynamic service compositions.
In Proceedings ARAMIS @ ASE 2008
3.
Domenico Bianculli, Carlo Ghezzi, Paola Spoletini, Luciano Baresi and Sam Guinea. A Guided Tour through
SAVVY-WS: a Methodology for Specifying and Validating Web Service Compositions. In Proceedings Advances in
Software Engineering 2008
4.
Andreas Gehlert, Antonio Bucchiarone, Raman Kazhamiakin, Andreas Metzger, Marco Pistore, Klaus Pohl:
Exploiting Assumption-Based Verification for the Adaptation of Service-Based Applications. In Proceedings SOAP
@ SAC 2010
5.
Brice Morin, Olivier Barais, Grégory Nain, and Jean-Marc Jézéquel. Taming dynamically adaptive systems with
models and aspects. In Proceedings ICSE 2009.
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
Run-time Quality Assurance Techniques
Results
 Life-cycle Model [1, CD-JRA-1.3.4]
– QA oriented life cycle
layered on existing iterative life cycle
( WP-JRA-1.1)
– Due to open nature,
SBS need to "continuously" assert
properties that have a “lifelong” validity
- E.g., there is no guarantee that a service
implementation eventually fulfils the contract
promised (e.g., SLA)
- E.g., during design-time QA, it is not
possible to model the behaviour of the
underlying distributed infrastructure (e.g.,
Internet)
– Existing QA techniques applied
at each stage of the service life cycle
– Combining different techniques can
improve the overall quality of QA
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
A
Run-time Quality Assurance Techniques
Results
A
 Run-time Verification: Basic Approach [2, 3, 4]
– Activities during Design-Time:
- Specify service composition (workflow): W ( JRA-1.1, JRA-2.2)
- Assume properties of the outside world / context: A ( Negotiation)
- e.g., QoS of services as stipulated in SLAs
- Formalize requirements towards workflow: R ( JRA-1.1, JRA-2.2)
- Check (e.g., using model checker) that workflow meets requirements
- W, A |-- R ?
W
R
X
W, A |-- R ?
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
A
Run-time Quality Assurance Techniques
Results
A
 Run-time Verification: Basic Approach [2, 3, 4, CD-JRA-1.3.4]
– Activities during Run-time:
- (1) Monitor assumptions ( JRA-1.2): M
- (2) Check violation of assumptions: M  A ?
- If violated: (3) check if requirements are still met
- based on past monitoring data M + assumptions on
“future” invocations A’
- W, A’, M |-- R
X
- If requirements are not met: (4) adapt SBA (e.g.,
replace services) ( JRA-1.2)
M
M  A ?  W, A’, M |-- R ?
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
Quality Prediction Techniques to
Support Proactive Adaptation



A
Problems
–
Reactive adaptation has significant shortcomings
–
Quality prediction needed to enable pro-active adaptation ( JRA-1.2)
–
Unnecessary pro-active adaptations need to be avoided (as they can be costly)
Solution
–
Exploiting synergies between monitoring and online testing
–
Determining (and fostering) confidence of failure prediction
S-Cube Publications
1.
2.
3.
4.
5.
Julia Hielscher, Raman Kazhamiakin, Andreas Metzger and Marco Pistore. A Framework for Proactive SelfAdaptation of Service-based Applications Based on Online Testing. In ServiceWave 2008, Nr. (5377), Springer, 1013 December 2008.
Andreas Gehlert, Julia Hielscher, Olha Danylevych and Dimka Karastoyanova. Online Testing, Requirements
Engineering and Service Faults as Drivers for Adapting Service Compositions. In Dimka Karastoyanova, Raman
Kazhamiakin, Andreas Metzger and Marco Pistore editors, Proceedings of the International Workshop on Service
Monitoring, Adaptation and Beyond (MONA+ 2008), December 13, 2008, Madrid, Spain, Pages 39--50, 2008.
Andreas Gehlert, Andreas Metzger, Dimka Karastoyanova, Raman Kazhamiakin, Klaus Pohl, Frank Leymann,
Marco Pistore. Adaptation of Service-Based Systems based on Requirements Engineering and Online Testing.
Internet of Services Book (S. Dustdar, Ed.) – to be published Andreas Metzger, Osama Sammodi, Klaus Pohl. Towards Pro-Active Adaptation with Confidence – Augmenting
Monitoring with Online Testing. SEAMS Workshop @ ICSE 2010
Philipp Leitner, Branimir Wetzstein, Florian Rosenberg, Anton Michlmayr, Schahram Dustdar, and Frank Leymann.
Runtime Prediction of Service Level Agreement Violations for Composite Services. In Proceedings Non-Functional
Properties and SLA Management @ ICSOC 2009
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
Quality Prediction Techniques to
Support Proactive Adaptation
A
 PROSA-Framework [1, 2, 3, CD-JRA1.3.4]
– Framework for exploiting online testing
for
pro-active adaptation
- Integration of online testing with
monitoring
- Integration of pro-active, corrective
adaptation with pro-active,
perfective adaptation
 Failure Prediction with Confidence
[4, CD-JRA-1.3.4]
– Determining whether failure prediction
is of expected confidence
– Initiating online tests to collect data
points for required confidence
– Invalidating data points in cases of
adaptation
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
monitor
Servicebased
Application
predict
Decide on
Adaptation
adapt
Online
Testing
Case studies: a methodological
approach
Towards validation: Case studies
 Goals:
– validate research results with realistic scenarios
– develop research challenges derived from case studies
 A methodology for case study documentation has been
developed
Case studies
 S-Cube proposal
– An approach to describe case studies derived from NEXOF-RA and
enriched with other elements from the RE literature
– The identification of a set of case studies which the approach is
applied on
Case studies
 Directly from S-CUBE
– Wine production (Donnafugata)
– Automotive supply chain (360Fresh and IBM)
 Derived from NEXOF-RA
– E-health diagnostic workflow (Siemens/Thales)
– Traffic management (Siemens)
– E-government (TIS and Engineering)
 For a complete description of the case studies analyzed in S-Cube, please refer to
the deliverable CD-IA-2.2.2 and for scenarios CD-IA-2.2.4
© S-Cube
The proposed case study description
approach
 Business goals: express the main purposes of some system in the terms
of the business domain in which the system will live or currently lives
 Domain assumptions and constraints: report properties of the domain or
restrictions on the design of the system architecture
 Domain description: phenomena occurring in the world together with the
laws that regulate such a world
 Abstract scenario description: a way to describe world phenomena
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
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Business goals and domain
assumptions/constraints
 Business goals and domain assumption/constraints rely on
the same elements:
– Description
– Rationale
– Involved stakeholders
– Conflicts
– Supporting material
– Priority
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
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Domain description
 Purpose
– Study and describe phenomena in the world as well as shared
phenomena
 Content
– Glossary
– Relationships among the main terms
- Through class diagrams, semantic networks, E/R diagrams, …
– Boundaries between the world and the machine
- Context diagrams
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
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Scenarios description
 Purpose: to describe possible situations and interactions
between the world and the machine
 Structure of description
– Involved actors
– Detailed operational description
– Problems and challenges
– Non-functional requirements and constraints
– Accompanying material
- sequence and activity diagrams
- (sub)use case diagrams
 From scenarios, abstract scenarios are derived (template)
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
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Case study description life-cycle
 The four elements NOT necessarily have to be defined
sequentially
– Goalsassumptionsdomainscenario
 They can be defined iteratively
 Some rules:
– All the terms used in the description have to be put in a glossary
– All identified actors have to appear in the context diagram (and vice
versa)
– From each scenario there exist at least one related business goal and
vice versa
– Scenarios are not overlapping
– Goals are not overlapping
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
© S-Cube
Coverage of life cycle
Barbara Pernici – Month 24 Review Meeting, April 2010
Classifying & Comparing case studies
P. Plebani - IE4SOC Opening - Stockholmwww.s-cube-network.eu
23/11/2009
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Case study meta-data
 Used to index case studies in the repository for facilitating
search mechanisms
 Meta-data:
– Source
– Real vs. Realistic
– Abstract
– Available solutions
– Licensing
– …
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
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Comparison dimensions
 S-Cube
– Description of business situations and presence of agile service
networks
– Need for negotiating, establishing, monitoring, enforcing QoS
– Need for service consumers with various different characteristics
– Need for distributed infrastructures
– Need for highly distributed service compositions
– Highly changing requirements at various levels (from business to
infrastructure)
 Others
– Security
– Reliability
– …
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
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SEEKING NEW CASE STUDIES
(OR SCENARIOS FOR EXISTING ONES)!