1. S-Cube Learning Package
Service Level Agreements:
Preventing SLA Violations in Service
Compositions Using Aspect-Based Fragment
Substitution
TU Wien (TUW), University of Stuttgart (USTUTT)
Branimir Wetzstein, USTUTT
www.s-cube-network.eu

2. Learning Package Categorization
S-Cube
Adaptable Coordinated
Service Compositions
Adaptable and QoS-aware
Service Compositions
Preventing SLA Violations in Service Compositions
Using Aspect-Based Fragment Substitution

3. Learning Package Overview
 Problem Description
 Aspect-based Fragment Substitution
 Discussion
 Conclusions
© Philipp Leitner

4. Let’s Consider a Scenario (1)
 Assume you are a provider of a composite service
 Commercial customers order products via a Web service
interface
 Your process checks an internal stock, orders some missing
parts, and assembles the product
© Philipp Leitner

5. Let’s Consider a Scenario (2)
 (Of course) there are contractual obligations
– Delivery in time
– Availability of your service
– Product quality
 These can be formulated using Service Level Agreements
– Note: in this context not necessarily
WSLA style machine processable
SLAs – can also be just regular
contracts
© Philipp Leitner

6. Let’s Consider a Scenario (3)
 As a provider, you want to receive timely notifications if these
obligations are likely to be violated
– Usually there are penalties to be paid if contractual obligations are
(repeatedly) violated
– Even if this is not the case, customer satisfaction will suffer and
existing customers may terminate their contracts
 Based on these notifications …
!
[ v yi g
e rt n
eh
– … countermeasures can be taken (i.e.,
Re
eie
cv Sc
et
l
e
a iae
vl b]
a l
G a e Cre
e y n
tPm
Ps
rf
e
t hg
a
Ct m
uoe
s r
adaptations can be triggered)Or
re
d Sp r
u le
pi
Cc
hk
e
O r rm
re o
dF
Sk
tc
o [ ou le
ns p r
pi S Or
h re
i
p d
Sp r
u le
pi 1
a iae
vl b]
a l
O r rm
re o
dF
Sp r
u le
pi 2
Cc O r
a e re
nl d
© Philipp Leitner

7. Let’s Consider a Scenario (4)
 After an SLO violation is predicted for a process instance, the goal is to adapt the
process instance in order to prevent the violation
 Different types of adaptation actions:
 Data Manipulation, Service Rebinding (in composition)
 Environmental (adapt hosting environment)
 Structural adaptation
(change structure of composition)
 Idea:
 The provider can pre-model a set of alternative process fragments (process
variants) with different QoS characteristics
 The best possible alternative(s) is then selected and executed at runtime
based on SLO prediction

8. Learning Package Overview
 Problem Description
 Aspect-Based Fragment Substitution
 Discussion
 Conclusions
© Philipp Leitner

9. Approach: SLA Violation Prevention via
Fragment Substitution
1. Define Checkpoints in the service composition
2. Use Machine Learning techniques to generate predictions for service
level objectives (SLOs) at a check point
3. Adapt service composition by substituting alternative fragments which
likely can prevent the predicted SLO violation

10. Background:
Related Approaches in S-Cube
 This S-Cube approach uses the techniques presented in the
following S-Cube Learning Package
 Runtime Prediction of SLA Violations Based on Service Event Logs
 See also:
Leitner, Wetzstein, Rosenberg, Michlmayr, Dustdar, and Leymann. Runtime Prediction of Service Level
Agreement Violations for Composite Services. In Proceedings of the 2009 International conference on
Service-Oriented Computing (ICSOC/ServiceWave'09), Springer-Verlag, Berlin, Heidelberg, 176-186.
Leitner, Michlmayr, Rosenberg, and Dustdar. Monitoring, Prediction and Prevention of SLA Violations in
Composite Services. In Proceedings of the 2010 IEEE International Conference on Web Services (ICWS '10).
IEEE Computer Society, Washington, DC, USA, 369-376.

11. Background:
Runtime Prediction of SLA Violations
 Goal: predict the value of a process metric (which is the basis of an SLO) while the process
instance is still running
 The prediction model is created using machine learning regression techniques (artificial
neural networks) based on historical process instances
Order Product Supplier In SLO
…
ID Type Delivery Stock Value
Art. Neural
Network
1 Bike TourX 28 h No … 96 h
Machine
Monitoring Learning
Database 2 Bike RX - Yes … 48 h
… … … … …
 At predefined checkpoints, both measured and estimated facts are used as input to the
prediction model (which provides a predicted numerical value as output)
Art. Neural
Check Order Product Supplier In Predicted
… Network
point ID Type Delivery Stock SLO
7101 Book 28 h No … 72 h

12. Aspect-based Fragment Substitution (1)

13. Aspect-based Fragment Substitution (2)
 Aspect-Oriented Programming (AOP) terminology
– Cross-cutting concerns are implemented using loosely-coupled program fragments
(advices)
– During weaving, advices are inserted directly into the program code of target
applications at well-defined join points
– Decision of whether to insert a given advice: pointcut
 At design time:
– A set of fragments (= advices) is created which can be used as alternative
implementations of parts of business logic in the target composition
– Fragments are standalone (typically “incomplete”) compositions focusing on a certain
part of business logic and are linked into the original composition via join points
– A fragment definition also contains an impact model which defines how this fragment
affects composition performance metrics
 At runtime:
 A predicted SLA violation at a check point triggers the adaptation
 A set of adaptation alternatives is identified based on impact models; the selected
alternative is weaved into the target composition

14. Linking of Fragments and Weaving (1)
 Design Time: Linking of fragments into the target composition
 Defines the join points in the execution where control flow changes from the
target composition to the fragment or vice versa
 Join Points: START, END, TRANSPARENT (virtual activities)

15. Linking of Fragments and Weaving (2)
 Runtime: Selection and Weaving of fragments into the target composition
 After SLO Prediction… selection of one or more fragments based on their
impact models
 Dynamic weaving of new fragment logic into the running composition instance

16. Generic Fragments
 Generic Fragments:
 Fragments consisting only of virtual activities
 Instantiated via linking
 Used for recurring domain-independent adaptation actions such as removal,
reordering, or parallelizing of activities

17. Selection of Fragments
 How do we determine which fragments will likely prevent the
predicted violation?
 Main idea: impact models
– Describe the impact that every fragment has on the metrics that are
used to generate predictions of violations
– Can be created using e.g., SLAs, data mining, QoS aggregation, …
–  repeat prediction as if adaptation has taken place
Order Product Supplier In
… Predicted
ID Type Delivery Stock
SLO
7101 Book 48 h No … 72 h
Artificial
Neural
Network
Impact Clauses
Adaptation Impact
Order Product Supplier In
Order Product Supplier In … Predicted
ID
Order Type
Product Delivery
Supplier Stock
In … Predicted
ID Type Delivery Stock … SLO after
Predicted
ID Type Delivery Stock SLO after
adaptation
SLO after
7101 Book 32 h No … adaptation
7101 Book 32 h No … adaptation
7101 Book 32 h No …
56 h
56 h
56 h

18. Architectural Overview (1)

19. Architectural Overview (2)
1. Define SLAs (SLA DB) and a set of alternative fragments
(Advice DB)
2. Train a Prediction Model for each Checkpoint and use it at
runtime for prediction at a check point (Violation Predictor)
3. Select an optimal set of fragments based on their impact
models and the SLO prediction (Advice Selector)
4. Weave the selection into the
composition instance and resume
process execution (Advice Weaver)

20. VRESCo Open Source Implementation
 The prototype used to do these experiments has been
developed as part of the VRESCo project
– Can be downloaded from Sourceforge
 The Sourceforge package also includes the assembling case
study
– However, setup is not very user-friendly
– Get in touch with us if interested in reproducing the case study
 Uses WEKA Machine Learning toolkit to implement the actual
prediction algorithms
© Philipp Leitner

21. Learning Package Overview
 Problem Description
 Aspect-Based Fragment Substitution
 Discussion
 Conclusions
© Philipp Leitner

22. Overall Performance Impact on Process
Execution Time (1)

23. Overall Performance Impact on Process
Execution Time (1)
 Online weaved compositions exhibit very little overhead as
compared to statically defined compositions
 Overhead consists of time necessary to…
– select the fragments
– implement the actual weaving (largest impact)
– to suspend and unsuspend
the composition

24. Weaving Time (1)

25. Weaving Time (2)
 Weaving time is relatively constant in [45 : 80] ms, even for
large fragments (more than 80 activities)
This should be fast enough for most applications
 As expected…
–Concrete activities are faster
to weave than transparent
activities
–Offline weaving is faster than
online weaving

26. Discussion - Advantages
 Runtime Fragment Substitution based on SLA Predictions has
a number of clear advantages …
– Coverage of many adaptation patterns – insertion, deleting, moving,
replacement of fragments, parallelizing of activities, etc.
– Flexibility – the set of available alternative fragments can be altered
(i.e., new fragments can be created or existing ones removed) after
the process has been deployed during process runtime
– Efficiency – even though training of the machine learning models takes
some time, generating predictions and the adaptation are very fast

27. Discussion - Disadvantages
 … but of course the approach also has some disadvantages.
– Bootstrapping problem – the approach assumes that some recorded
historical event logs are available for training of the prediction model
– Necessary monitoring – in order to know later which fragments have
been executed for a process instance
– Necessary domain knowledge – in order to define checkpoints and the
fragments and their impact models some domain knowledge is
necessary

28. Learning Package Overview
 Problem Description
 Aspect-Based Fragment Substitution
 Discussion
 Conclusions
© Philipp Leitner

29. Summary
 Machine learning based techniques can be used to predict
SLA Violations in service compositions at runtime
 Based on these predictions, runtime fragment substitution can
be used for preventing the SLA violation
 Steps:
1. Define a checkpoint in the composition and train a machine learning
model from historical event log
2. Whenever a composition instance passes the checkpoint, use the
monitored data of the instance as input for the machine learning
based prediction
3. Based on the available fragment alternatives, select and substitute
one or more fragments which likely can prevent the predicted
violation

30. Further S-Cube Reading
Leitner, Philipp; Wetzstein, Branimir; Karastoyanova, Dimka; Hummer, Waldemar; Dustdar, Schahram;
Leymann, Frank: Preventing SLA Violations in Service Compositions Using Aspect-Based Fragment
Substitution. In: Proceedings of the 8th International Conference on Service Oriented Computing (ICSOC
2010).
Leitner, Michlmayr, Rosenberg, and Dustdar. Monitoring, Prediction and Prevention of SLA Violations in
Composite Services. In Proceedings of the 2010 IEEE International Conference on Web Services (ICWS '10).
IEEE Computer Society, Washington, DC, USA, 369-376.
Leitner, Wetzstein, Rosenberg, Michlmayr, Dustdar, and Leymann. Runtime Prediction of Service Level
Agreement Violations for Composite Services. In Proceedings of the 2009 International conference on
Service-Oriented Computing (ICSOC/ServiceWave'09), Springer-Verlag, Berlin, Heidelberg, 176-186.
Wetzstein, Leitner, Rosenberg, Brandic, Dustdar, and Leymann. Monitoring and Analyzing Influential Factors
of Business Process Performance. In Proceedings of the 13th IEEE international conference on Enterprise
Distributed Object Computing (EDOC'09). IEEE Press, Piscataway, NJ, USA, 118-127.

31. 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).
© Philipp Leitner