www.s-cube-network.eu 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
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S-CUBE LP: Multi-layer Monitoring and Adaptation of Service Based Applications
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www.s-cube-network.eu
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
Learning Package Categorization
S-Cube
Cross-layer Adaptation
Multi-layer Monitoring and Adaptation of
Service Based Applications
Adaptation and Monitoring Principles,
Techniques and Methodologies for SBAs
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
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.
Problem Description
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.
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
Multi-layer SBA Framework Overview
1. Monitoring and Correlation
2. Analysis of adaptation needs
3. Identification of Multi-layer Strategies
4. Adaptation enactment
We propose an integrated framework that allows for the installation of multi-
layered control loops in service-based systems.
Multi-layer SBA Framework Overview
1. Monitoring and Correlation
2. Analysis of adaptation needs
3. Identification of Multi-layer Strategies
4. Adaptation enactment
1. Monitoring and correlation: reveals correlations between the
observed software and infrastructure level events
Multi-layer SBA Framework Overview
1. Monitoring and Correlation
2. Analysis of adaptation needs
3. Identification of Multi-layer Strategies
4. Adaptation enactment
2. Analysis of adaptation needs: identifies anomalous situations
and pinpoints the parts of the architecture that needs to adapt
Multi-layer SBA Framework Overview
1. Monitoring and Correlation
2. Analysis of adaptation needs
3. Identification of Multi-layer Strategies
4. Adaptation enactment
3. Identification of multi-layer strategies: generates adaptation
strategies with regard to the currently available adaptation
capabilities of the system
Multi-layer SBA Framework Overview
1. Monitoring and Correlation
2. Analysis of adaptation needs
3. Identification of Multi-layer Strategies
4. Adaptation enactment
4. Adaptation Enactment: enacts the generated adaptation strategy
Multi-layer SBA Framework
1
2
3 4
The framework integrates layer specific monitoring and adaptation
techniques developed within S-Cube
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
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.
Monitoring and Correlation (2)
• 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
Data sources available through
Dynamo/Astro, Laysi, and EcoWare
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
Monitoring and Correlation (4) Resources
Dynamo/Astro and EcoWare:
Laysi
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
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)