Collaboration opportunities and wrap-up 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

Details on the S-Cube Integrated Research Framework 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 (c) Prof. Dr. Klaus Pohl

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 (c) Prof. Dr. Klaus Pohl

Integrated Research Framework Views Conceptual Research Framework Reference Life Cycle Logical Run Time Architecture Logical Design Environment (c) Prof. Dr. Klaus Pohl

Integrated Research Framework Conceptual Research Framework © S-Cube – 6 (c) Prof. Dr. Klaus Pohl

Integrated Research Framework Reference Life-Cycle © S-Cube – 7 (c) Prof. Dr. Klaus Pohl

Integrated Research Framework Logical Run-Time Architecture © S-Cube – 8 (c) Prof. Dr. Klaus Pohl

Integrated Research Framework Logical Design Environment © S-Cube – 9 (c) Prof. Dr. Klaus Pohl

Research Areas in Service Engineering

Service Engineering for Service-Based Applications (SBA) (c) Prof. Dr. Klaus Pohl

Service Techniques & Methods Planes Adaptation & Monitoring (SAM) Business Process Mgt. (BPM) Adaptation & Monitoring (SAM) Engineering & Design (SED) Composition & Coordination (SCC) Infra- structure (SI) Service Technologies Layers Quality Definition, Negotiation & Assurance (SQDNA) © S-Cube – 3/# (c) Prof. Dr. Klaus Pohl

Adaptation design Focus on activation of adaptation strategies Instance-level adaptation Context Model-based (c) Prof. Dr. Klaus Pohl

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 (c) Prof. Dr. Klaus Pohl

Life-cycle for SBAs WESOA, 2009 (c) Prof. Dr. Klaus Pohl

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 (c) Prof. Dr. Klaus Pohl

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 (c) Prof. Dr. Klaus Pohl

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 (c) Prof. Dr. Klaus Pohl (c) Prof. Dr. Klaus Pohl 18

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 (c) Prof. Dr. Klaus Pohl

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 (c) Prof. Dr. Klaus Pohl

Cappiello, 2010 (c) Prof. Dr. Klaus Pohl

Preventive and Corrective strategies Cappiello, 2010 (c) Prof. Dr. Klaus Pohl

Quality evaluation Representing users According to quality evaluation techniques for service compositions Representing users Importance of each user Importance of quality dimensions for each user Cappiello, 2010 (c) Prof. Dr. Klaus Pohl

Comparing strategies Domain specific! (c) Prof. Dr. Klaus Pohl

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 (c) Prof. Dr. Klaus Pohl

Cross-Layer Adaptation (c) Prof. Dr. Klaus Pohl

Service Techniques & Methods Planes Adaptation & Monitoring (SAM) Engineering & Design (SED) Business Process Mgt. (BPM) Adaptation & Monitoring (SAM) Composition & Coordination (SCC) Infra- structure (SI) Service Technologies Layers Quality Definition, Negotiation & Assurance (SQDNA) © S-Cube – 3/# (c) Prof. Dr. Klaus Pohl

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/# (c) Prof. Dr. Klaus Pohl

WP Vision Integrated A&M Framework Conceptual Model: A&M taxonomy Instantiations: Cross-layer A&M Proactive adaptation HCI and context-awareness Self-adaptation Monitoring mechanisms Adaptation mechanisms Monitored events Adaptation requirements Adaptation strategies detect trigger achieve realize Conceptual architecture: Results from SotA Research results of S-Cube members R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting © S-Cube – 5/# (c) Prof. Dr. Klaus Pohl

Key Results Achieved Cross-layer Adaptation and Monitoring REQUIREMENTS Cross-layer integrated monitoring mechanisms Monitored events Monitoring mechanisms Means to identify adaptation needs across layers Adaptation effectiveness Event propagation and alignment Cross-layer Models application monitoring events adaptation strategies Adaptation requirements Means to identify adaptation strategies across layers Adaptation compatibility and integrity Adaptation coordination Adaptation strategies Adaptation mechanisms Cross-layer integrated and coordinated adaptation mechanisms R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting © S-Cube – 9/# (c) Prof. Dr. Klaus Pohl

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 Baresi, Guinea, Pistore, Trainotti. Dynamo+ASTRO: an Integrated Approach for BPEL Monitoring. In ICWS 2009 Morin, Ledoux, Ben Hassine, Chauvel, Barais, Jezequel. Unifying Runtime Adaptation and Design Evolution In CIT 2009 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/# (c) Prof. Dr. Klaus Pohl

Cross-layer Adaptation and Monitoring Identification of Adaptation Needs Results Domain assumptions [1] Explicit encoding of domain assumptions Associate them to requirements Monitor assumption violations verify requirements at run time to trigger adaptation Replacement policies [2] 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 Gehlert, Bucchiarone, Kazhamiakin, Metzger, Pistore, Pohl: Exploiting Assumption-Based Verification for the Adaptation of Service-Based Applications. SOA@SAC Conference, 2010 Mahbub, Zisman. Replacement Policies for Service-Based Systems. MONA+ workshop, ICSOC/ServiceWave conference, 2009 Assumptions Domain Requirements SBA design time run-time Non-functional req’s Functional req’s Composition (BPEL) Service protocols QoS models External services User context R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting © S-Cube – 32/# (c) Prof. Dr. Klaus Pohl

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 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/# (c) Prof. Dr. Klaus Pohl

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 Baresi, Guinea, Pasquale. Integrated and Composable Supervision of BPEL processes. ICSOC Conference 2008 Baresi, Guinea. Self-supervising BPEL processes. PoliMi TR 74.2009 R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting © S-Cube – 34/# (c) Prof. Dr. Klaus Pohl

End-to-End Quality Provision (c) Prof. Dr. Klaus Pohl

Service Techniques & Methods Planes Adaptation & Monitoring (SAM) Engineering & Design (SED) Business Process Mgt. (BPM) Adaptation & Monitoring (SAM) Composition & Coordination (SCC) Infra- structure (SI) Service Technologies Layers Quality Definition, Negotiation & Assurance (SQDNA) © S-Cube – 3/# (c) Prof. Dr. Klaus Pohl

Motivational „Scenario“ response time: 1 s time: 2 s 2. Pay Renewal Fee 3. Update Vehicle Record 4.a. E-Mail Confirmation 1. Identify Vehicle [valid] [not valid] ePay response time: 2 s cost: 1.50 € SecurePay response time: 3 s cost: 1 € Yahoo response time: 1.5 s GMail 4.b. Mail Validation Sticker = service invocation/activity = third-party service = internal service = alternative binding RenewalHandler A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010 TODO: show what end-to-end means, Negotiation and assurance

WP Vision Key Challenges Devise novel principles, techniques & methods for Quality... ...Definition [Mo1-45] D End-to-End Quality Reference Model (completed in Y1) Rich and Extensible Quality Definition Language Exploiting HCI knowledge for automatic quality contract establishment Proactive SLA negotiation and agreement (from Y3) Run-time Quality Assurance Techniques Quality Prediction Techniques to Support Proactive Adaptation A Mention that explicit negotiation is not always needed; e.g., in case there are taci agreements / contracts (e.g., using freely available services, …) N ...Assurance [Mo18–45] ...Negotiation [Mo10–45] (c) Prof. Dr. Klaus Pohl

Key Results Achieved Quality Reference Model Problems Understanding quality attributes across SOA layers Solution Quality Reference Model S-Cube Publications NNN A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010 © S-Cube – 39/<Mx>

Key Results Achieved Knowledge Modelling 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” SLOs accessible via KM!!! A. Metzger / WP-JRA-1.3 – Month 12 Review Meeting, Essen, April 2009 © S-Cube – 9/18 (c) Prof. Dr. Klaus Pohl

Key Results Achieved Knowledge Modelling 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 UML-Based Quality Models Statically Inferred QoS Attributes Model Design by Contracts Models Functional Quality in Service Composition Model Service Networks and KPIs Model Grid Quality Model Softw. Eng. SOC BPM Grid A. Metzger / WP-JRA-1.3 – Month 12 Review Meeting, Essen, April 2009 © S-Cube – 10/18 (c) Prof. Dr. Klaus Pohl

Exploiting HCI knowledge for automatic quality contract establishment 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 Marco Comuzzi and Barbara Pernici. A Framework for QoS-Based Web service Contracting. In ACM Transactions on the Web, 3(3), 2009 Marco Comuzzi, Kyriakos Kritikos and Pierluigi Plebani. Semantic-aware Service Quality Negotiation. In ServiceWave 2008 Marco Comuzzi, Kyriakos Kritikos and Pierluigi Plebani. A semantic based framework for supporting negotiation in Service Oriented Architectures. In Proceedings IEEE CEC 2009 Kyriakos Kritikos and Dimitris Plexousakis. Mixed-Integer Programming for QoS-Based Web Service Matchmaking. In IEEE Transactions on Services Computing, 2(2), 2009 negotiatable vs. non-negotiatable attributes: performance vs. Reputation A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010 © S-Cube – 42/<Max> (c) Prof. Dr. Klaus Pohl

© S-Cube – 43/<Max> 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 © S-Cube – 43/<Max> (c) Prof. Dr. Klaus Pohl

© S-Cube – 44/<Max> 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 © S-Cube – 44/<Max> (c) Prof. Dr. Klaus Pohl

© S-Cube – 45/<Max> Automatic quality contract establishment Results: Negotiation Techniques D N Framework for quality (QoS) contracting [1, CD-JRA-1.3.3] (1) Phase 1: Matchmaking 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 (2) Phase 2: Selection 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 © S-Cube – 45/<Max> (c) Prof. Dr. Klaus Pohl

Run-time Quality Assurance Techniques 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 Domenico Bianculli, Carlo Ghezzi and Cesare Pautasso. Embedding Continuous Lifelong Verification in Service Life Cycles. In Proceedings PESOS @ ICSE 2009 Domenico Bianculli and Carlo Ghezzi. SAVVY-WS at a glance: supporting verifiable dynamic service compositions. In Proceedings ARAMIS @ ASE 2008 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 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 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 © S-Cube – 46/<Max> (c) Prof. Dr. Klaus Pohl

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 © S-Cube – 47/<Max> (c) Prof. Dr. Klaus Pohl

© S-Cube – 48/<Max> 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 A X W, A |-- R ? A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010 © S-Cube – 48/<Max> (c) Prof. Dr. Klaus Pohl 48

© S-Cube – 49/<Max> 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 If requirements are not met: (4) adapt SBA (e.g., replace services) ( JRA-1.2) X M M  A ?  W, A’, M |-- R ? A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010 © S-Cube – 49/<Max> (c) Prof. Dr. Klaus Pohl 49

Quality Prediction Techniques to Support Proactive Adaptation 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 Julia Hielscher, Raman Kazhamiakin, Andreas Metzger and Marco Pistore. A Framework for Proactive Self-Adaptation of Service-based Applications Based on Online Testing. In ServiceWave 2008, Nr. (5377), Springer, 10-13 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 © S-Cube – 50/<Max> (c) Prof. Dr. Klaus Pohl

Quality Prediction Techniques to Support Proactive Adaptation PROSA-Framework [1, 2, 3, CD-JRA-1.3.4] 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 Service-based Application Decide on Adaptation monitor adapt predict Online Testing A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010 © S-Cube – 51/<Max> (c) Prof. Dr. Klaus Pohl

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 (c) Prof. Dr. Klaus Pohl

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 (c) Prof. Dr. Klaus Pohl

Case studies Directly from S-CUBE Derived from NEXOF-RA 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 (c) Prof. Dr. Klaus Pohl (c) Prof. Dr. Klaus Pohl 55

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 © S-Cube (c) Prof. Dr. Klaus Pohl 56

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 © S-Cube (c) Prof. Dr. Klaus Pohl 57

Domain description Purpose Content 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 © S-Cube (c) Prof. Dr. Klaus Pohl 58

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 © S-Cube (c) Prof. Dr. Klaus Pohl 59

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 (c) Prof. Dr. Klaus Pohl

Coverage of life cycle Barbara Pernici – Month 24 Review Meeting, April 2010 (c) Prof. Dr. Klaus Pohl (c) Prof. Dr. Klaus Pohl 61

Classifying & Comparing case studies P. Plebani - IE4SOC Opening - Stockholm 23/11/2009 © S-Cube (c) Prof. Dr. Klaus Pohl (c) Prof. Dr. Klaus Pohl 62

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 © S-Cube (c) Prof. Dr. Klaus Pohl

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 © S-Cube (c) Prof. Dr. Klaus Pohl 64

SEEKING NEW CASE STUDIES (OR SCENARIOS FOR EXISTING ONES)! (c) Prof. Dr. Klaus Pohl