Urban community-based adaptation: opportunities and challenges
Adaptation of Service-based Systems - KIT
Transcript of Adaptation of Service-based Systems - KIT
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Universitätsstr. 38 70178 Stuttgart Germany Office: 1.361 Phone: +49 (0)711 7816 476
Jun.-Prof. Dr. Dimka Karastoyanova IAAS, University of Stuttgart [email protected]
Adaptation of
Service-based Systems
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Topics
Service-based Systems Adaptation Classification of Triggers and Approaches
Adaptation Approaches from our Research Summary/Conclusion
© Dimka Karastoyanova
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The Famous Travel Agency Process Choreography of several applications/compositions
© Dimka Karastoyanova
Plan Trip
Submit to Travel Agent
Receive eTickets
Select Legs
Order Tickets
Send eTickets
Get Flight Submission
Get Ticket Order
Receive Confirmation
Reserve Seats
Confirm Flights
Send Itinerary
Traveler Agent Airline
Receive Itinerary
Charge Credit Card
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Multi-Scale Human Skeleton Simulation
Choreography of simulations of skeleton, bone, tissue, and cell scale
Picture adopted from www.anatomium.com, www.med-ed.virgina.edu and R. Bartl University München
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Bone Growth Simulation
Orchestration to simulate bone growth depending on load, exercise, etc. Understand diseases, e.g. fractures
Based on the Finite Element Method (FEM) Initial and boundary condition Simulation is solved via a PDE as a matrix
equation (Ax=b)
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Modeled and realized as a choreography
Bone Growth Simulation
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Service-Based Systems: Choreographies of complex services/service-based applications
(SBAs) Services may be choreographies or orchestrations themselves
© Dimka Karastoyanova
Service-Based Systems
Choreography
Services
Orchestrations
Choreography
Services
Orchestrations
Choreography
Services
Orchestrations
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SBA model: Three layers Current research focus:
Model, execute, monitor and adapt on all three levels coherently
© Dimka Karastoyanova
SBA Life cycle includes adaptation Framework for QoS aware, adaptable
SBAs
Service-Based Applications
Choreography
Services
Orchestrations
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Service-Based Applications (SBAs)
Services: Are units of functionality Described using a unified IDL Independent of
implementation technology Self-contained stable service
interfaces Virtualization of components
SBAs comprise services And follow the principles of the
Service Oriented Architecture (SOA) style
The SOA roles and operations:
© Dimka Karastoyanova
DBMS .NET J2EE
Virtual Component
Concrete Component
Assembly StP ...
Web Service
(E)JB
implements implements implements
Transport Medium
Find
Bind
Publish
Service Requestor
Service Provider
Service Discovery
Technology for implementing SBAs is workflows
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Service Compositions
© Dimka Karastoyanova
What?
Who?
With?
Get Order
Check Customer
Accept Order
Reject Order
Always!
Credit Card Information
Customer Address
Worklists
Workitem
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Adaptation of Service Compositions (How?)
© Dimka Karastoyanova
Adaptation on all dimensions Control flow changes Functions changes Exchange human participants
On model (evolution, versioning) and instance level (ad-hoc changes) (When?)
SC Model and/or Infrastructure changes to accommodate adaptation (What realization?)
Many Triggers for Adaptation (Why?)
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Adaptation Triggers (Why?) Triggers can be generated on
each level of an SBA Value for participants - Service Networks QoS and nfp violations, KPIs – SCs and BPs SLAs violations – all layers Compliance violations – all layers Changes in policies – mostly BPs Unavailable services – Service Infrastructure
+ SCs Context change – all layers Organizational restructuring – mostly
business related Law – mostly the BP layer …
© Dimka Karastoyanova
Choreography
Services
Orchestrations
Service Network
Business Transaction
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Next:
• Adaptation of SCs: BPEL’n’Aspects
• SBA Adaptation: KPI Violation prediction and Adaptation
• Process Outsourcing – Fragmentation and Coordination
• Flexible Scientific Workflows
© Dimka Karastoyanova
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BPEL’n’Aspects
• Adaptation of SCs • Control flow change • Reaction to any kind of trigger • Standard-based • Engine extensions needed
© Dimka Karastoyanova
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BPEL’n’Aspects
Use of the AOP paradigm Insert WSs (aspect) into an SC
(program) as a reaction to event
Aspects are attached to processes
WS invocation before, instead or after activities i.e. control flow changes
Engine publishes process execution events
The dynamic weaving of the activities/WSs is triggered by these events
© Dimka Karastoyanova
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Aspect as a WS-Policy
© Dimka Karastoyanova
<wsp:PolicyAttachment> <wsp:AppliesTo> <DomainExpression/>+ </wsp:AppliesTo> (<wsp:Policy>...</wsp:Policy> | <wsp:PolicyReference>... </wsp:PolicyReference>)+ </wsp:PolicyAttachment>
<ws:Policy …> <wsp:All> <a4b:Aspect id="...">* <a4b:Advice name="..." compensating="true | false" alwaysCompensate="true | false"?> <wsa:EndpointReference>…
</wsa:EndpointReference> ... </a4b:Advice> <a4b:CompensationAspect aspectId="..."/>? <a4b:Pointcut>... <a4b:ProcessArtifact type=”activity |transitionCondition |...” identifier=”...”/> <a4b:When type= ”before | instead | after”/> </a4b:Pointcut>? </a4b:Aspect> </wsp:All> </ws:Policy>
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Architecture and Prototype
BPEL Engine Publishes relevant events:
activity status, variable modification, execution of implicit and
explicit CHs
Aspect Management Tool: specify and deploy aspects
© Dimka Karastoyanova
Weaver Weaves in aspects
Audit component Stores execution events
published by the engine Stores variable values at
aspect weaving time needed for later compensation
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Preventing Violations of KPIs
• Adaptation approach: any • Adaptation trigger: KPI violations • Engine extensions needed • Run time approach
© Dimka Karastoyanova
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Adaptable and QoS-Aware Service Compositions For service compositions implementing business processes:
Use adaptation as reaction to the changes in QoS parameters of a Service-Based Application
Adaptation trigger: KPI targets are not reached Approach: Analyze the reason for the KPI violation (influential factors) and optimize
through adaptation
Service Composition
KPIs
Modeling
Adaptation Monitoring
Modeling of metrics, KPIs, available adaptation actions etc.
Analysis Runtime monitoring and evaluation of KPIs
Analysis and Prediction of KPI Violations
Adaptation for optimization of process performance
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Runtime
© Dimka Karastoyanova
Identification of Adaptation
Requirements and Strategies
Adaptation Enactment
KPI Dependency Analysis
Metrics Model
Adaptation Action Model
Runtime KPI Prediction
Checkpoint Model
Checkpoint Model
Monitoring
Selection of an Adaptation
Strategy
Preferences Model
List of alt. Adaptation Strategies
Metric Values
Metric Values
Instance Tree(s)
Adaptation Strategy
Classification Model(s)
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Modeling
© Dimka Karastoyanova
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Analysis and Prediction Analyze the influential factors and
explain the KPI target violations (why?):
Explanation model is created using decision tree techniques (machine learning) based on historical process instances (monitoring)
Result is a KPI dependency tree explaining
which metrics (combinations) and
which value ranges of those metrics lead to good or bad KPI values
© Dimka Karastoyanova
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Analysis and Prediction
Delivery Time Shipment
Delivery Time Supplier
Order In Stock?
Order In Stock?
Delivery Time Supplier
< 2 > 4 2 < x <4
yes no yes no
< 3 > 3 < 1 > 1
green 20/1
green 50
red 20
red 10
red 80/2
green 30
green 5
Delivery Time Shipment
< 2 > 4
green 20/1
Order in Stock = No
2 < x <4
red 10
red 80/2
Instance Tree
Classification Model
Delivery Supplier = 2,0
We want to achieve this path
Try to ensure that “shipment delivery time”
< 2 days
Predict the KPI class while the process instance is still running
Using the prediction model (dependency tree) and runtime information /measured metrics
Result: an instance tree showing the KPI classes in relation to characterizing metrics of adaptable entities shows which adaptation actions are needed
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Identify Adaptation Actions
© Dimka Karastoyanova
Adaptation Action Type: Service Substitution
Subject: Purchase Order Process: Supplier Service
Impact max del time <= 4 days Model: delivery cost = 10€
availability WS > 0,99 reliability = 0.5
Service: Supplier A *express*
Choose and combine adaptation actions which address adaptation requirements according to their
impact model
Req Id Adaptation Strategy
1
Supplier A *express* AND Shipper 1
Supplier A *express* AND Shipper 2
Supplier A *premium* AND Shipper 2
…
… …
Id Adaptation Requirement Predicted KPI Classes
1 SuTime<4,5 AND ShTime<3 AND Su=A
Green AND very good
2 SuTime<4,5 AND ShTime<2 AND Su=B
Green AND good
3 SuTime<7,5 AND ShTime<2
AND Su=A
Green AND very good
4 SuTime<7,5 AND ShTime<2
AND Su=B
Green AND good
… … …
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Architecture of the Prototype
© Dimka Karastoyanova
Process Engine
List
ener
Prediction Dashboard
QoS Monitor
Classifier
Metrics Database
CEP Engine
Monitoring
Chec
k Po
int
List
ener
.
Decision Tree
Prediction
3
3
4
4
Delivery Time Shipment
< 2 > 4
green
2 < x <4
red red
Classification Model
Learner
Strategy Identifier &
Selector
Analysis Database
Adaptation Enactor
Performance Dashboard
Adaptation Dashboard
1
2
2
1
3
5
5 6
Adaptation
1
1
Adaptation Interface
7
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28
Outsourcing Processes: • Adaptation of SCs • Control flow adaptation, split into
fragments • Trigger: Organizational Adaptation • Design time (and Run time) • Language extension • Engine extension, Coordination
Infrastructure • Standard-based
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Outsourcing: Splitting Processes
Outsourcing part of a process using the concept of sub-processes Autonomy of sub-processes
29
P P1 P2
Split
Call as sub-process
© Dimka Karastoyanova
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Splitting a Process to Multiple Organizations Alternatively:
process fragment/partition is outsourced to an organization, each process is performed autonomously
© Dimka Karastoyanova
P
A1 A2 A3
A4 A5 A6
A7 A8 A9
A10
A11 A12
A13
A14
A15
O1 O2 O3
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Projecting Onto Organizations Step 1: Project process onto
organizations Define partitions/fragments Split control connectors
© Dimka Karastoyanova
A1 A2 A3
A4 A5
A6
A7 A8 A9
A10
A11 A12
A13
A14
P1 P2 P3
A15
O1 O2 O3
Step 2: Wire/connect the processes to model original business logic
O1 O2 O3
A1 A2 A3
A4 A5
A6
A7 A8 A9
A10
A11 A12
A13
A14
A15
P1 P2 P3
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Step 1: Deriving the Wiring
Splitting Control Links
© Dimka Karastoyanova
O2
A B
A B B’
q
A’ q
Control Signal
O1
L
Split
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Deriving the Wiring (2)
Splitting Control Links
© Dimka Karastoyanova
O2
A B
A B B’
q
A+ q
O1
L
S
A– FH
trueq
falseq
Scope
Fault Handler, catching “q=0” as join-fault
in=trueq
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Join Nodes
Splitting Control Links leading to join nodes
© Dimka Karastoyanova
O2
A B
A
B B’
q
A+ q
O1 A–
trueq
falseq
in=trueq
C C’
C+ r
O3 C–
truer
falser
C r
in=truer
j
j
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Projecting Onto Organizations
© Dimka Karastoyanova
A1 A2 A3
A4 A5
A6
A7 A8 A9
A10
A11 A12
A13
A14
A15
O1 O2 O3
P2 P3
a3 a1
a4
a4 ´ a6
a14 a10
a10 ´
a12 a15
a7 a8
a5
a13
P1
Step 3: Deploy partitions onto infrastructures Wires are split control connectors No changes in standards necessary if no structured activities are split
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Splitting a Scope
© Dimka Karastoyanova
A
B C
D E
S
O1
O2
A
B
S1
O1
C
D E
S2
O2
split
Coordinator
• C can only start if A completed successfully • If C completed successfully D may only run after S1
completed successfully • DPE: Link CE may be set to false in case S1 detects a fault
(but S2 did not) • S2 may get compensated although no fault occurred at S2 • … Coordination required
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Flexible Scientific Workflows
• Adaptation of SCs • Control flow change; any available
approach • Reaction to change in simulation
workflow model • Standard-based • Engine extensions needed
© Dimka Karastoyanova
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Scientific Workflows Scientific experiments/computations/
simulations modeled and executed as workflows
Characteristics: deal with huge amounts of data, are often long-running, usually data driven, can integrate multiple data sources
(i.e. sensors, data bases, file systems, etc.)
Bone Growth Simulation:
Scientific Workflows
© Dimka Karastoyanova
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SimTech Differences in life cycles: scientific workflows vs. service compositions Goal: enable scientific workflows using the life cycle of SCs
© Dimka Karastoyanova
Phase
User
Legend
Action
Analysis
Scientist
Modeling
Pause
Run/Resume
Execution and Monitoring
Scientist
Deployment
Functions Dimension Adaptation
Logic Dimension Adaptation
Modeling
Adaptation
Analysis
Execution and Monitoring
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Execution of Partially Specified Processes? Execution of process instances is based on process model The model is considered to be complete by the execution engine
i.e. the instances are terminated after the last activity
© Dimka Karastoyanova
A
B C
E D
F G
H
Initial version of the model
A
B C
E D
F G
H
Subsequent version of the model
Execute instance
I J
A
B C
E D
F G
H
I J
K
Execute instance
Next version
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Our Approach: Model-as-you-go
© Dimka Karastoyanova
Analysis
Execution and Monitoring
Run/Resume
Modeling
Scientist
Modeling
Suspend
In what workflow life cycle phase we are … What the scientist experiences …
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Model-as-you-go
© Dimka Karastoyanova
Analysis
Execution and Monitoring
Run/Resume
Modeling
Scientist
Execution and Monitoring
In what workflow life cycle phase we are … What the scientist experiences …
Suspend
Computation is suspended at
this point
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Model-as-you-go
© Dimka Karastoyanova
Analysis
Execution and Monitoring
Run/Resume
Modeling
Scientist
Modeling
In what workflow life cycle phase we are … What the scientist experiences …
Suspend
Computation is suspended at
this point
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Model-as-you-go
© Dimka Karastoyanova
Analysis
Execution and Monitoring
Run/Resume
Modeling
Scientist
Execution and Monitoring
In what workflow life cycle phase we are … What the scientist experiences …
Suspend
Computation is suspended at
this point
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Model-as-you-go
© Dimka Karastoyanova
Analysis
Execution and Monitoring
Run/Resume
Modeling
Scientist
Modeling
In what workflow life cycle phase we are … What the scientist experiences …
Suspend
Computation is suspended at
this point
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Model-as-you-go – Major Extensions
© Dimka Karastoyanova
All-in-one
Modeling
Monitoring
Execution
Adaptation
1) 2) Execution control 3) Adaptation as modeling experience
4) Iteration 5) Breakpoints 6) History of variable data Var A = 1
Var A = 2
Var A = 2
Failure!
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© Dimka Karastoyanova
Model-as-you-go Framework
Workflow Engine
Monitoring
Workflow Editor
Modeling
Migration, Versioning, …
Provenance Mining
Process Mining
Pre-Deployment
WSDL Generator DD Generator
Monitor Instance Monitor
Adaptation
Validation Logic Dimension
Functions Dimension
Process Repair
Deployment Strategy
Change Tracking
III Auditing
Provenance EI
ECI Adaptation Interface Deployment Interface
III = Instance Information Interface EI = Event Interface ECI = Execution Control Interface
Breakpoint Registry
Execution Control
Result Display
(run, suspend, resume, (de-)register breakpoints)
Single, integrated tool from user
perspective
Adaptation as modeling
experience
Breakpoints for automatic suspension
Control over workflow execution
Variable history
Component
Model-as-you-go framework
Interaction between components
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© Dimka Karastoyanova
Adaptation of Service Based Systems and Applications: Relevant for many application domains
Can be carried out on one or more layers of an application / system and
across organizational boundaries
Is complex - consider dependencies to other aspects – data, domain, SBA architecture, triggers, etc.
Still a lot to be done
Future: Coherent cross-layer adaptation
Coherent approach to select adaptation action(s) for a trigger
Choreography adaptation, Cross organizational adaptation, split and merge
Views for different user types (Business Transactions, Service Networks) and their use for adaptation
Summary
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References NoE S-Cube: http://www.s-cube-network.eu/
Excellence Cluster SimTech: http://www.simtech.uni-stuttgart.de/
IAAS at the University of Stuttgart: http://www.iaas.uni-stuttgart.de/ Wetzstein, Branimir; Zengin, Asli; Kazhamiakin, Raman; Marconi, Annapaoloa; Pistore,
Marco; Karastoyanova, Dimka; Leymann, Frank: Preventing KPI Violations in Business Processes based on Decision Tree Learning and Proactive Runtime Adaptation. In: Journal of Systems Integration, January, 2012.
Guinea, Sam; Kecskemeti, Gabor; Marconi, Annapaola; Wetzstein, Branimir: Multi-layered Monitoring and Adaptation. In Proceedings of ICSOC 2011.
Khalaf, Rania; Leymann, Frank: Coordination for Fragmented Loops and Scopes in a Distributed Business Process. In Proceedings of BPM 2010.
Sonntag, Mirko; Karastoyanova, Dimka: Next Generation Interactive Scientific Experimenting Based On The Workflow Technology. In International Conference on Modelling and Simulation (MS 2010).
Sonntag, Mirko; Karastoyanova, Dimka: Compensation of Adapted Service Orchestration Logic in BPEL’n’Aspects. In Proceedings of BPM 2011.