multi-layer monitoring and adaptation -...

www.s-cube-network.eu

Multi-layer Monitoring and AdaptationSam Guinea (Polimi), Gabor Kecskemeti (SZTAKI),

Annapaola Marconi (FBK), Branimir Wetzstein (USTUTT)

ICSOC 2011, December 7 - Paphos, Cyprus

Overview

Problem Description

Multi-layer SBA Framework– Monitoring and correlation– Analysis of adaptation needs– Identification of multi-layer strategies– Adaptation Enactment

Example scenario

Conclusions

Problem

Application LayerService Layer

Infrastr. Layer

Service-based applications are multi-layered in nature, as we tend to build software as a service on top of infrastructure as a service.

Most existing SOA monitoring and adaptation techniques address layer-specific issues

Problem


Infrastr. Layer



KPI

Problem


Infrastr. Layer



KPIKPI

Problem


Infrastr. Layer



KPI

Problem


Infrastr. LayerKPI

Multi-layer monitoring and adaptation is essential in truly understanding problems and in developing comprehensive solutions.

Challenges

From layer-specific symptoms to holistic diagnosisMulti-layer monitoring and analysis:

Correlate and analyze local monitoring information to derive the adaptation problem to be tackled

From layer-specific actions to holistic adaptation strategiesMulti-layer adaptation: Local adaptations should be coordinated to take into account the impact

they may have on other elements of the SBA

Multi-layer monitoring and adaptation is essential in truly understanding problems and in developing comprehensive solutions.

Overview

Problem Description


Example Scenario

Conclusions

Multi-layer SBA FrameworkOverview

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 MAPE loops in service-based systems.

reveals correlations between the observed events at the different layers

analyzes anomalous situations and pinpoints the parts of the system that needs to adapt

generates adaptation strategies coordinating the local adaptation capabilities

enacts the identified adaptation strategy

Multi-layer SBA Framework

12

34

The framework integrates layer specific monitoring and adaptation techniques developed within the S-Cube project

Overview

Problem Description


Example Scenario

Conclusions

Goal : reveal correlations between what is being observed at the different layers to enable global system reasoning

Monitoring and Correlation

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 infrastructure nodes and the status of the infrastructure resources .

The collected data are then aggregated and manipulated (EcoWare) to produce higher-level correlated data under the form of general (e.g. Reliability, Avg Reponse time, ..) and domain-specific metrics (Esperevent processing language).

Monitoring and Correlation (2)

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

Overview

Problem Description


Example Scenario

Conclusions

Analysis of Adaptation needs

Goal : analyze the correlated monitoring data and derive possible layer-specific adaptation actions

Influential Factor Analysis Detects the influential factors of bad performances

finding the relation between the set of correlated metrics (potential influential factors) and the bad KPI categories

Adaptation Needs Analysis Given the influential factors it identifies possible (layer-specific)

adaptation actions that can enhance the system performances

Analysis of Adaptation needs (2)

Influential factor analysis tool: Receives the (software, infrastructure, aggregated) metric values for a set of

execution instances (e.g. duration of whole process, path executed, response time of external services, infrastructure node on which service was executed)

Uses machine learning techniques (decision trees) to find out for which combination of metrics and values bad KPI categories have been reached

Example: whenever activity A is executed through service S and service S is executed on node N the “overall process duration” is bad

Analysis of Adaptation needs (3)

Adaptation needs analysis tool: Receives the decision tree and an adaptation actions model 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 possible layer-specific adaptation actions

Overview

Problem Description


Example Scenario

Conclusions

Identification of Multi-layer Strategies

Achieved by the Cross Layer Adaptation Manager by Identifying the application components that are affected

by the adaptation actions Proposing an adaptation strategy that properly coordinates

the layer-specific adaptation capabilities

Goal : Manage the impact of adaptation actions across the system's multiple layers and identify a “good” adaptation strategy

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

SBA model: contains the current configuration of the system components (e.g. business processes, services, infrastructure resources) and their dependencies


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 layer-specific adaptation capabilities.

The Cross-layer Rule Engine uses each checker's outcome to progressively update the adaptation strategy tree, where each path represent a potential adaptation strategy.


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

Overview

Problem Description


Example Scenario

Conclusions

Adaptation Enactment

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 (i) the BPEL activities, (ii) the partner-link set and (iii) the internal state. Static BPEL modifier: For more extensive process restructuring a new modified

XML definition is created for the process

LAYSI Run-time resource allocation taking into account dynamic system constraints Deployment of new infrastructure nodes for high demand situations

Goal : Apply the actions of the identified adaptation strategy to the SBA

Learning Package Overview

Problem Description

Multi-layer SBA Framework

Monitoring and correlation

Analysis of adaptation needs

Identification of multi-layer strategies

Adaptation Enactment

Example scenario

Conclusions

CT-Scan Scenario

The approach has been evaluated on a medical imaging procedure forComputed Tomography (CT) Scans, an e-Health scenario characterized by strong dependencies between the software layer and infrastructural resources

Legend:CSDA – cross sectional data acquisitionFTR – frontal tomographic reconstructionSTR – sagittal tomographic reconstructionATR – axial tomographic reconstruction3D – volumetric reconstructionPACS – picture archiving and communication

CS data acquisition

Image reconstructions Data storage

CSDA FTR STR ATR 3D PACS

CSDAFTR

STRATR

3D

PACS

PACS3D

N1 N2N3

N4N5

CT-Scan Scenario

The approach has been evaluated on a medical imaging procedure for Computed Tomography (CT) Scans

CS data acquisition



CSDAFTR

STRATR

3D

PACS

PACS3D

N1 N2N3

N4N5

Multi-layer M&A

M: KPI violation App L.

A:

cm

KPI

CT-Scan Scenario


CS data acquisition



CSDAFTR

STRATR

3D

PACS

PACS3D

N1 N2N3

N4N5

Multi-layer M&A


A: App L. problem

cm

Parallelize P

P:

KPI

P: Analysis of cross-layer impact

CT-Scan Scenario


CS data acquisition



CSDAFTR

STRATR

3D

PACS

PACS3D

N1 N3N4

N5

Multi-layer M&A


A: App L. problem

cm

Parallelize P


FTR

STR

ATR

3D

PACSCSDA

N2

CT-Scan Scenario


Data storage

CSDAFTR

STRATR

3D

PACS

PACS3D

N1 N3N4

N5

Multi-layer M&A


A: App L. problem

cm

Parallelize P


CS data acquisition

Image reconstructions


FTR STR ATR

3D

PACSCSDA

N2

CS data acquisition Image reconstructions

CSDA

FTR

STR

ATR

3D

PACS

FTR

STR

ATR

3D

PACS

CSDA

E: Dynamic deployment

Overview

Problem Description


Example Scenario

Conclusions

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.

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. This will require the development of new

specialized monitors and adaptations

Thank youfor the attention !

Monitoring and CorrelationReferences

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)

Analysis of Adaptation needsResources

Background papers:

Virtual Campus Learning Package

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

Analyzing Business Process Performance Using KPI Dependency Analysis” as the name of the learning package.

Identification of Multi-layer StrategiesResources

Background papers:

A. Zengin, A. Marconi, L. Baresi, and M. Pistore. CLAM: Managing Cross-layer Adaptation in Service-Based Systems. In Proc. SOCA 2011

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

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