S-Cube Learning Package           Cross-layer Adaptation:Multi-layer Monitoring and Adaptation of       Service Based Appl...
Learning Package Categorization                         S-Cube         Adaptation and Monitoring Principles,        Techni...
Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptat...
Problem Description   Service-based applications are multi-layered in nature, as we tend to    build software as a servic...
Problem Description   Most existing SOA monitoring and adaptation techniques address    layer-specific issues. These tech...
Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptat...
Multi-layer SBA Framework                              Overview   We propose an integrated framework that allows for the ...
Multi-layer SBA Framework                          Overview                            1. Monitoring and                  ...
Multi-layer SBA Framework                          Overview                            1. Monitoring and                  ...
Multi-layer SBA Framework                          Overview                             1. Monitoring and                 ...
Multi-layer SBA Framework                          Overview                            1. Monitoring and                  ...
Multi-layer SBA Framework                           1                                                              2      ...
Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptat...
Monitoring and Correlation   Goal: reveal correlations between what is being observed at the software    and at the infra...
Monitoring and Correlation (2)Data sources available throughDynamo/Astro, Laysi, and EcoWare•   Dynamo Interrupt samplers:...
Monitoring and Correlation (3)    Technical integration of Dynamo/Astro, Laysi, and EcoWare, achieved using     a Siena p...
Monitoring and Correlation (4)                                       ResourcesDynamo/Astro and EcoWare: L. Baresi and S. G...
Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptat...
Analysis of Adaptation needs   Monitoring and correlation produce simple and complex metrics that need to    be evaluated...
Analysis of Adaptation needs (2)   Influential factor analysis tool:        Receives the (software, infrastructure, aggr...
Analysis of Adaptation needs (3)                                                      ResourcesBackground papers: B. Wetzs...
Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptat...
Identification of Multi-layer Strategies   Goal: Manage the impact of adaptation actions across the systems    multiple l...
Identification of Multi-layer Strategies (2)   SBA Model Updater        Whenever a new set of adaptation actions is rece...
Identification of Multi-layer Strategies (3)                                                    ResourcesBackground papers...
Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptat...
Adaptation Enactment   Goal: Apply the actions of the identified adaptation strategy to the SBA   This is achieved by Dy...
Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptat...
Evaluation     CT-Scan Scenario                                                                         Legend:           ...
Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptat...
Conclusions and Future work   Multi-layer adaptation and monitoring approach for SBA:       The approach is based on a v...
Conclusions and Future work    Future work includes:        Evaluate the approach through new application scenarios.    ...
Further ReadingS. Guinea, G. Kecskemeti, A. Marconi, and B.Wetzstein. Multi-layered Monitoring and Adaptation. Accepted as...
Acknowledgements      The research leading to these results has      received funding from the European      Community’s S...
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S-CUBE LP: Multi-layer Monitoring and Adaptation of Service Based Applications

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S-CUBE LP: Multi-layer Monitoring and Adaptation of Service Based Applications

  1. 1. S-Cube Learning Package Cross-layer Adaptation:Multi-layer Monitoring and Adaptation of Service Based Applications Fondazione Bruno Kessler (FBK), University of Stuttgart (USTUTT), Politecnico di Milano (Polimi), MTA Sztaki (SZTAKI) Annapaola Marconi, FBK www.s-cube-network.eu
  2. 2. Learning Package Categorization S-Cube Adaptation and Monitoring Principles, Techniques and Methodologies for SBAs Cross-layer Adaptation Multi-layer Monitoring and Adaptation of Service Based Applications
  3. 3. Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptation needs Identification of multi-layer strategies Adaptation Enactment Evaluation Conclusions
  4. 4. Problem Description Service-based applications are multi-layered in nature, as we tend to build software as a service on top of infrastructure as a service. Adaptation and monitoring goal:  Observe different quality values corresponding to the specified requirements (KPI, PPM, SLAs), and, in case of the violation of the target values,  Adapt the running business process (or future instances) so the violation is either prevented or corrected.
  5. 5. Problem Description Most existing SOA monitoring and adaptation techniques address layer-specific issues. These techniques used in isolation, cannot deal with real-world domains: 1. The violation of the high-level SBA requirements may be motivated by different factors and at different layers and components. Given the complexity of the application it is not possible to immediately discover which specific element caused the overall quality degrade. 2. Even if the problem is identified, it may not be clear whether the associated adaptation action is suitable. Indeed, the adaptations should be analyzed with respect to the impact they may have on other elements of the SBA and on the other requirements. Multi-layer monitoring and adaptation is essential in truly understanding problems and in developing comprehensive solutions.
  6. 6. Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptation needs Identification of multi-layer strategies Adaptation Enactment Evaluation Conclusions
  7. 7. Multi-layer SBA Framework Overview We propose an integrated framework that allows for the installation of multi- layered control loops in service-based systems. 1. Monitoring and Correlation 4. Adaptation 2. Analysis of enactment adaptation needs 3. Identification of Multi-layer Strategies
  8. 8. Multi-layer SBA Framework Overview 1. Monitoring and Correlation 4. Adaptation 2. Analysis of enactment adaptation needs 3. Identification of Multi-layer Strategies 1. Monitoring and correlation: reveals correlations between the observed software and infrastructure level events
  9. 9. Multi-layer SBA Framework Overview 1. Monitoring and Correlation 4. Adaptation 2. Analysis of enactment adaptation needs 3. Identification of Multi-layer Strategies 2. Analysis of adaptation needs: identifies anomalous situations and pinpoints the parts of the architecture that needs to adapt
  10. 10. Multi-layer SBA Framework Overview 1. Monitoring and Correlation 4. Adaptation 2. Analysis of enactment adaptation needs 3. Identification of Multi-layer Strategies 3. Identification of multi-layer strategies: generates adaptation strategies with regard to the currently available adaptation capabilities of the system
  11. 11. Multi-layer SBA Framework Overview 1. Monitoring and Correlation 4. Adaptation 2. Analysis of enactment adaptation needs 3. Identification of Multi-layer Strategies 4. Adaptation Enactment: enacts the generated adaptation strategy
  12. 12. Multi-layer SBA Framework 1 2 4 3  The framework integrates layer specific monitoring and adaptation techniques developed within S-Cube
  13. 13. Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptation needs Identification of multi-layer strategies Adaptation Enactment Evaluation Conclusions
  14. 14. Monitoring and Correlation Goal: reveal correlations between what is being observed at the software and at the infrastructure layer to enable global system reasoning Sensors deployed throughout the system capture run-time data about its software (Dynamo/Astro) and infrastructural (Laysi) elements.  Dynamo/Astro provides means for gathering events regarding either process internal state, or context data  Laysi produces low-level infrastructure events and can be queried to better understand how services are assigned to hosts. The collected data are then aggregated and manipulated (EcoWare) to produce higher-level correlated data under the form of general and domain- specific metrics.  Possible to use predefined aggregate metrics such as Reliability, Average Response Time, or Rate, or domain-specific aggregates whose semantics is expressed using the Esper event processing language.
  15. 15. Monitoring and Correlation (2)Data sources available throughDynamo/Astro, Laysi, and EcoWare• Dynamo Interrupt samplers: interrupt the process and gather information• Dynamo Polling samplers: no process interruption, gather information through polling• Invocation Monitor: produces low-level events through the observation of the infrastructure managed by LAYSI• Information Collector: aggregates and caches the actual status of the service infrastructure
  16. 16. Monitoring and Correlation (3)  Technical integration of Dynamo/Astro, Laysi, and EcoWare, achieved using a Siena publish and subscribe event bus.  Input and output adapters used to align Dynamo, Laysi, and the event processors with a normalized message format
  17. 17. Monitoring and Correlation (4) ResourcesDynamo/Astro and EcoWare: L. Baresi and S. Guinea. Self-Supervising BPEL Processes. IEEE Trans. Software Engineering, 37(2):247– 263, 2011. L. Baresi, M. Caporuscio, C. Ghezzi, and S. Guinea. Model-Driven Management of Services. In Proc. ECOWS 2010, pages 147–154. L. Baresi, S. Guinea, M. Pistore, M. Trainotti: Dynamo + Astro: An Integrated Approach for BPEL Monitoring. In Proc. ICWS 2009: 230-237. L. Baresi, S. Guinea, R. Kazhamiakin, M. Pistore: An Integrated Approach for the Run-Time Monitoring of BPEL Orchestrations. In Proc. ServiceWave 2008: 1-12 F. Barbon, P. Traverso, M. Pistore, M. Trainotti: Run-Time Monitoring of Instances and Classes of Web Service Compositions. In Proc. ICWS 2006: 63-71Laysi A. Kertesz, G. Kecskemeti, and I. Brandic. Autonomic SLA-Aware Service Virtualization for Distributed Systems. In Proceedings of the 19th International Euromicro Conference on Parallel, Distributed and Network- based Processing, PDP, pages 503–510, 2011. Virtual Campus learning package: SLA based Service infrastructures in the context of multi layered adaptation (SZTAKI)
  18. 18. Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptation needs Identification of multi-layer strategies Adaptation Enactment Evaluation Conclusions
  19. 19. Analysis of Adaptation needs Monitoring and correlation produce simple and complex metrics that need to be evaluated. A Key Performance Indicator consists of one of these metrics (e.g., overall process duration) and a target value function which maps values of that metric to a set of categories (e.g., process duration < 3 days is “good”, otherwise “bad”). Goal: if monitoring shows that many process instances have bad KPI performance, we need to analyze the influential factors that lead to these bad KPI values
  20. 20. Analysis of Adaptation needs (2) Influential factor analysis tool:  Receives the (software, infrastructure, aggregated) metric values for a set of process instances within a certain time period  Uses machine learning techniques (decision trees) to find out the relations between a set of metrics (potential influential factors) and the KPI value based on historical process instances Adaptation needs analysis tool:  Receives the decision tree and an adaptation actions model (manually defined) specifying a set of adaptation actions (e.g., service substitution, process structure change) and how they affects one or more metrics  Extracts the paths which lead to bad KPI values from the tree and combines them with available adaptation actions which can improve the corresponding metrics on the path, obtaining different sets of potential adaptation actions
  21. 21. Analysis of Adaptation needs (3) ResourcesBackground papers: B. Wetzstein, P. Leitner, F. Rosenberg, S. Dustdar, and F. Leymann. Identifying Influential Factors of Business Process Performance using Dependency Analysis. Enterprise IS, 5(1):79–98, 2011. R. Kazhamiakin, B. Wetzstein, D. Karastoyanova, M. Pistore, and F. Leymann. Adaptation of Service-Based Applications Based on Process Quality Factor Analysis. In ICSOC/ServiceWave Workshops, pages 395{404, 2010. B. Wetzstein, P. Leitner, F. Rosenberg, I. Brandic, S. Dustdar, F. Leymann: Monitoring and Analyzing Influential Factors of Business Process Performance. EDOC 2009: 141-150 P. Leitner, B. Wetzstein, F. Rosenberg, A. Michlmayr, S. Dustdar, F. Leymann: Runtime Prediction of Service Level Agreement Violations for Composite Services. ICSOC/ServiceWave Workshops 2009: 176-186Virtual Campus Learning Package Analyzing Business Process Performance Using KPI Dependency Analysis” as the name of the learning package.
  22. 22. Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptation needs Identification of multi-layer strategies Adaptation Enactment Evaluation Conclusions
  23. 23. Identification of Multi-layer Strategies Goal: Manage the impact of adaptation actions across the systems multiple layers. This is achieved by the Cross Layer Adaptation Manager (CLAM) in two ways :  Identifying the application components that are affected by the adaptation actions  Proposing an adaptation strategy that properly coordinates the layer-specific adaptation capabilities To achieve its goal CLAM relies on  A model of the SBA containing the current configuration of the system components (e.g. business processes, services, infrastructure resources) and their dependencies  A set of pluggable checkers, each associated with a specific application concern (e.g. service composition, service performances, infrastructure resources), to analyze whether the updated application model is compatible with the concerns requirements.
  24. 24. Identification of Multi-layer Strategies (2) SBA Model Updater  Whenever a new set of adaptation actions is received from the Quality Factor Analysis tool, the SBA Model Updater module updates the current application model by applying the received adaptation actions Cross-Layer Rule Engine  Detects the SBA components affected by the adaptation and identifies, through a set of predefined rules, the associated adaptation checkers.  Each checker is responsible for checking local constraint violations and for searching local solutions to the problem. This analysis may result in a new adaptation action to be triggered. This is determined through the interaction with a set of pluggable application-specific adaptation capabilities.  The Cross-layer Rule Engine uses each checkers outcome to progressively update the adaptation strategy tree. Adaptation Strategy Selector  In case of multiple available adaptation strategies (paths in the adaptation tree), selects the best adaptation strategy according to a set of predefined metrics
  25. 25. Identification of Multi-layer Strategies (3) ResourcesBackground papers: A. Zengin, R. Kazhamiakin, and M. Pistore. CLAM: Cross-layer Management of Adaptation Decisions for Service-Based Applications. In Proc. ICWS, 2011. R. Kazhamiakin, M. Pistore, A. Zengin: Cross-Layer Adaptation and Monitoring of Service-Based Applications. ICSOC/ServiceWave Workshops 2009: 325-334
  26. 26. Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptation needs Identification of multi-layer strategies Adaptation Enactment Evaluation Conclusions
  27. 27. Adaptation Enactment Goal: Apply the actions of the identified adaptation strategy to the SBA This is achieved by DyBPEL, at the software layer, and by LAYSI, at the infrastructure layer : DyBPEL  Process runtime modifier: Intercepts running processes and modifies them (i) on its BPEL activities, (ii) on its partner-link set and (iii) on its internal state.  Static BPEL modifier: For more extensive process restructuring a new modified XML definition is created for the process LAYSI  Negotiation bootstrapping – for new negotiation techniques  Service broker replacement – for handling broker failures  Deployment of new service instances – for high demand situations
  28. 28. Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptation needs Identification of multi-layer strategies Adaptation Enactment Evaluation Conclusions
  29. 29. Evaluation CT-Scan Scenario Legend: CSDA – cross sectional data acquisition FTR – frontal tomographic reconstruction STR – sagittal tomographic reconstruction ATR – axial tomographic reconstruction 3D – volumetric information PACS – picture archiving and communication The approach has been evaluated on a medical imaging procedure for Computed Tomography (CT) Scans, an e-Health scenario characterized by strong dependencies between the software layer and infrastructural resources For more details on the CT-Scan application scenario, please refer to S. Guinea, G. Kecskemeti, A. Marconi, and B.Wetzstein. Multi-layered Monitoring and Adaptation. Accepted as full research paper at ICSOC 2011.
  30. 30. Learning Package Overview Problem Description Multi-layer SBA Framework Monitoring and correlation Analysis of adaptation needs Identification of multi-layer strategies Adaptation Enactment Evaluation Conclusions
  31. 31. Conclusions and Future work Multi-layer adaptation and monitoring approach for SBA:  The approach is based on a variant of the well-known MAPE (Monitor, Analyze, Plan and Execute) control loops that are typical in autonomic systems.  All the steps in the control loop acknowledge the multi-layered nature of the system, ensuring that we always reason holistically, and adapt the system in a cross-layered and coordinated fashion.  The proposed framework integrates a set of adaptation and monitoring techniques, mechanisms, and tools developed within the S-Cube project  The approach has been evaluated on the e-Health CT-Scan scenario.
  32. 32. Conclusions and Future work  Future work includes:  Evaluate the approach through new application scenarios.  Add new adaptation capabilities and adaptation enacting techniques.  Integrate new layers, such as a platforms, typically seen in cloud computing setups, and business layers. This will require the development of new specialized monitors and adaptations  Study the feasibility of managing different kinds of KPI constraints.
  33. 33. Further ReadingS. Guinea, G. Kecskemeti, A. Marconi, and B.Wetzstein. Multi-layered Monitoring and Adaptation. Accepted as fullreserach paper at ICSOC 2011.L. Baresi and S. Guinea. Self-Supervising BPEL Processes. IEEE Trans. Software Engineering, 37(2):247–263, 2011.L. Baresi, M. Caporuscio, C. Ghezzi, and S. Guinea. Model-Driven Management of Services. In Proc. ECOWS 2010,pages 147–154.L. Baresi, S. Guinea, M. Pistore, M. Trainotti: Dynamo + Astro: An Integrated Approach for BPEL Monitoring. In Proc.ICWS 2009: 230-237.A. Kertesz, G. Kecskemeti, and I. Brandic. Autonomic SLA-Aware Service Virtualization for Distributed Systems. InProceedings of the 19th International Euromicro Conference on Parallel, Distributed and Network-based Processing,PDP, pages 503–510, 2011.B. Wetzstein, P. Leitner, F. Rosenberg, S. Dustdar, and F. Leymann. Identifying Influential Factors of Business ProcessPerformance using Dependency Analysis. Enterprise IS, 5(1):79–98, 2011.R. Kazhamiakin, B. Wetzstein, D. Karastoyanova, M. Pistore, and F. Leymann. Adaptation of Service-BasedApplications Based on Process Quality Factor Analysis. In ICSOC/ServiceWave Workshops, pages 395{404, 2010.A. Zengin, R. Kazhamiakin, and M. Pistore. CLAM: Cross-layer Management of Adaptation Decisions for Service-Based Applications. In Proc. ICWS, 2011.R. Kazhamiakin, M. Pistore, A. Zengin: Cross-Layer Adaptation and Monitoring of Service-Based Applications.ICSOC/ServiceWave Workshops 2009: 325-334
  34. 34. 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).
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