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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Multi-layer SBA Framework
1
2
4 3
 The framework integrates layer specific monitoring and adaptation
techniques developed within S-Cube

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. 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. Monitoring and Correlation (2)
Data sources available through
Dynamo/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. 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. Monitoring and Correlation (4)
Resources
Dynamo/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: 63-71
Laysi
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. 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. 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. 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. Analysis of Adaptation needs (3)
Resources
Background 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: 176-186
Virtual Campus Learning Package
Analyzing Business Process Performance Using KPI Dependency Analysis” as the
name of the learning package.

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. Identification of Multi-layer Strategies
 Goal: Manage the impact of adaptation actions across the system's
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 concern's
requirements.

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 checker's 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. Identification of Multi-layer Strategies (3)
Resources
Background 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. 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. 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. 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. 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. 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. 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. 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. Further Reading
S. Guinea, G. Kecskemeti, A. Marconi, and B.Wetzstein. Multi-layered Monitoring and Adaptation. Accepted as full
reserach 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. In
Proceedings 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 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.
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. 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).