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©paluno
Coordinated Run-time Adaptation of Variability-intensive Systems
An Application in Cloud Computing
Andreas Metzger...
©paluno
2
Motivation
& Problem
Statement
FCORE
Approach
FCORE
Models
FCORE
Analysis
Con-
clusion &
Outlook
©paluno
Motivation
Adaptive Software Systems
 Adaptive software systems can modify own structure and
behavior at run time...
©paluno
Motivation
Coordination among Adaptive Systems
 Distributed systems (e.g., cloud systems or cyber-physical system...
©paluno
Motivation
Use Case: Conflicts in Adaptive Cloud Systems
 „AdvancedTV“ Use Case from EU Project Cloud Wave
 Clou...
©paluno
6
Motivation
& Problem
Statement
FCORE
Approach
FCORE
Models
FCORE
Analysis
Con-
clusion &
Outlook
©paluno
FCORE Approach
Main Ideas and Challenges
 Explicitly model adaptations and dependencies among systems
during desi...
©paluno
8
Motivation
& Problem
Statement
FCORE
Approach
FCORE
Models
FCORE
Analysis
Con-
clusion &
Outlook
©paluno
FCORE Models
Main Underlying Concepts
 FCORE = DSPL Feature Model + Goal Models
9
Concepts from
Dynamic Software
...
©paluno
FCORE Models
Why DSPL Models? (Challenge 1)
 DSPLs can build on proven engineering foundations of SPLs!
 DSPL ex...
©paluno
FCORE Models
Which Kinds of DSPL Models? (Challenges 1&3)
Approach Expressiveness Analysis
Basic-FM
High redundanc...
©paluno
FCORE Models
Why Goal Models? (Challenge 2)
 Soft Goals provide high-level of abstraction to describe
influences ...
©paluno
FCORE Models
CloudWave Use Case: Simplified
13
©paluno
FCORE Models
CloudWave Use Case: SaaS
14
©paluno
FCORE Models
CloudWave Use Case: IaaS
15
©paluno
16
Motivation
& Problem
Statement
FCORE
Approach
FCORE
Models
FCORE
Analysis
Con-
clusion &
Outlook
©paluno
FCORE Analysis
Main Underlying Strategy (Challenge 3)
 Formalize FCORE Model as CSP (justification see above)
 P...
©paluno
FCORE Analysis
Formalization: Features
18
A
= Feature
selected
 Feature
 Requires-Relation
 Excludes-Relation
...
©paluno
FCORE Analysis
Formalization: Goals
20
 Softgoals and Attributes
Softgoal satisfaction:
sgVal = [-1.0, +1.0]
©paluno
FCORE Analysis
Performance (Challenge 3)
 FCORE Filter
 No performance issues
 Just compute goal satisfaction f...
©paluno
22
Motivation
& Problem
Statement
FCORE
Approach
FCORE
Models
FCORE
Analysis
Con-
clusion &
Outlook
©paluno
Conclusion and Outlook
 Concluded: FCORE as an approach for coordinating among
adaptive, variability intensive sy...
©paluno
The research leading to these results has
received funding from the European Union's
Seventh Framework Programme F...
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Coordinated run-time adaptation of variability-intensive systems: an application in cloud computing (VACE 2016)

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Presentation by Andreas Metzger for the International Workshop on Variability and Complexity in Software Design (VACE) at ICSE 2016 in Austin, Texas, USA.

Reference:
A. Metzger, A. Bayer, D. Doyle, A. Molzam Sharifloo, K. Pohl, F. Wessling, “Coordinated run-time adaptation of variability-intensive systems: an application in cloud computing”, Proceedings of the 1st ICSE 2016 International Workshop on Variability and Complexity in Software Design (VACE), Austin, Texas, USA, 2016

Abstract:
Distributed systems, such as cloud systems or cyber-physical systems, involve the orchestration of different variability-intensive, adaptive sub-systems. Each of these sub-systems may perform adaptations simultaneously and independently from each other. Yet, if dependencies between the adaptations of the sub-systems are not considered, this may lead to conflicting adaptations or untapped synergies among adaptations.

This paper introduces FCORE, a model-based approach, which facilitates coordinating adaptations among variability-intensive systems. The permissible run-time reconfigurations of each system is specified by an FCORE model, which combines feature models used in Dynamic Software Product Lines with goal models. FCORE models are mapped to constraint satisfaction problems to determine conflicts and synergies among the adaptations of the systems during execution. We demonstrate the FCORE approach by using a cloud system as a typical exemplar for a distributed system. The cloud system is part of an industrial use case concerned with offering value-added cloud services.

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Coordinated run-time adaptation of variability-intensive systems: an application in cloud computing (VACE 2016)

  1. 1. ©paluno Coordinated Run-time Adaptation of Variability-intensive Systems An Application in Cloud Computing Andreas Metzger†, Andreas Bayer†, Daniel Doyle*, Amir Molzam Sharifloo†, Klaus Pohl†, Florian Wessling† † paluno (The Ruhr Institute for Software Technology), University of Duisburg Essen, Germany * Intel, Ireland
  2. 2. ©paluno 2 Motivation & Problem Statement FCORE Approach FCORE Models FCORE Analysis Con- clusion & Outlook
  3. 3. ©paluno Motivation Adaptive Software Systems  Adaptive software systems can modify own structure and behavior at run time to cope with dynamic changes in…  M = machine (software)  self-healing  W = world (context)  context-aware  R = changing requirements  ???  @runtime: adaptive systems monitor changes in M and W or even R directly  M, W |≠ R (requirements violation)  self-modification 3 M, W |= R ?
  4. 4. ©paluno Motivation Coordination among Adaptive Systems  Distributed systems (e.g., cloud systems or cyber-physical systems) orchestrate many adaptive sub-systems  Each sub-system may perform adaptations simultaneously and independent of each other  However, adaptations may affect shared phenomena, thus:  Conflicts between adaptations may occur  Synergies among adaptations may be missed 4 Shared Phenomenon M, W |= R ? M, W |= R ? M, W |= R ?
  5. 5. ©paluno Motivation Use Case: Conflicts in Adaptive Cloud Systems  „AdvancedTV“ Use Case from EU Project Cloud Wave  Cloud application that offers services in parallel to running TV programme  Two adaptive systems:  Cloud Infrastructure (IaaS: CPU, RAM, …)  Cloud Application (SaaS)  Adaptations of Cloud Infrastructure  Horizontal Scaling  E.g., turning off virtual machines to save energy  Vertical Scaling  …  Adaptations of Cloud Application  Different levels of social media features  No  Partial  Unlimited  … 5 Performance -- VM  -- Performance ++ Socia Media  -- Performance
  6. 6. ©paluno 6 Motivation & Problem Statement FCORE Approach FCORE Models FCORE Analysis Con- clusion & Outlook
  7. 7. ©paluno FCORE Approach Main Ideas and Challenges  Explicitly model adaptations and dependencies among systems during design time  Challenge 1: Developers must model adaptations of their systems  Sufficiently compact, yet expressive modeling technique  Challenge 2: Systems developed by different developers/organizations  Suitably (small) common denominator to describe dependencies among systems  Analyze models at run time to determine conflicts and identify optimizations (synergies)  Challenge 3: Self-adaptation at run time must be fast enough to be effective (otherwise may be too late)  Efficient model analysis during system execution 7
  8. 8. ©paluno 8 Motivation & Problem Statement FCORE Approach FCORE Models FCORE Analysis Con- clusion & Outlook
  9. 9. ©paluno FCORE Models Main Underlying Concepts  FCORE = DSPL Feature Model + Goal Models 9 Concepts from Dynamic Software Product Lines Feature Models to describe adaptations Concepts from Goal Models to describe dependencies via shared phenomena Main underlying assumption: “Known Unkowns!“
  10. 10. ©paluno FCORE Models Why DSPL Models? (Challenge 1)  DSPLs can build on proven engineering foundations of SPLs!  DSPL extend existing software product line engineering approaches by moving their capabilities to run time  Variability binding is postponed to run time, allowing a DSPL to activate or deactivate certain features  Configurations of a DSPL are expressed in terms of a product line variability model, usually a feature model 10 Classical SPL Dynamic SPL variability describes different pos- sible software systems variability describes different possible configurations (i.e., adaptations) of the same system
  11. 11. ©paluno FCORE Models Which Kinds of DSPL Models? (Challenges 1&3) Approach Expressiveness Analysis Basic-FM High redundancy in models (replication of FM sub-trees) Cardinalities only 1..1 / 0..n SAT solver Cardinality-Based FM Alternative-Groups Cardinalities n..m Feature-Cardinalities n..m (i.e., instantiation of features) SAT solver Extended-FM Feature-Attributes (Integer, Enumeration, …) CSP solver 11
  12. 12. ©paluno FCORE Models Why Goal Models? (Challenge 2)  Soft Goals provide high-level of abstraction to describe influences of features (~ “tasks”) on goal satisfaction  Well-known from requirements engineering  Defining dependencies among systems requires agreeing on a set of shared soft goals  E.g., in cloud computing, these soft goals may be derived from standardized QoS catalogues for SLAs 12
  13. 13. ©paluno FCORE Models CloudWave Use Case: Simplified 13
  14. 14. ©paluno FCORE Models CloudWave Use Case: SaaS 14
  15. 15. ©paluno FCORE Models CloudWave Use Case: IaaS 15
  16. 16. ©paluno 16 Motivation & Problem Statement FCORE Approach FCORE Models FCORE Analysis Con- clusion & Outlook
  17. 17. ©paluno FCORE Analysis Main Underlying Strategy (Challenge 3)  Formalize FCORE Model as CSP (justification see above)  Perform automated reasoning on formalization  Two main usages:  FCORE Filter: Validity check of given configurations (= detecting conflicts)  E.g., 1 CPU + Unlimited Social Media  violation of high performance  FCORE Search: Search for configurations with high goal satisfaction (= exploiting synergies)  E.g., 6 CPUs + No Social Media  high performance + low costs 17
  18. 18. ©paluno FCORE Analysis Formalization: Features 18 A = Feature selected  Feature  Requires-Relation  Excludes-Relation  Feature Group
  19. 19. ©paluno FCORE Analysis Formalization: Goals 20  Softgoals and Attributes Softgoal satisfaction: sgVal = [-1.0, +1.0]
  20. 20. ©paluno FCORE Analysis Performance (Challenge 3)  FCORE Filter  No performance issues  Just compute goal satisfaction for given configuration  Ca. 2ms for cloud use case  FCORE Search  CSP to find optimal configurations (maximize sgVal)  Experimental results for cloud use case 21
  21. 21. ©paluno 22 Motivation & Problem Statement FCORE Approach FCORE Models FCORE Analysis Con- clusion & Outlook
  22. 22. ©paluno Conclusion and Outlook  Concluded: FCORE as an approach for coordinating among adaptive, variability intensive systems  Building on DSPLs  Offering Modelling + Analysis  Exemplified for the case of cloud computing  Ongoing:  Implementation as part of CloudWave Adaptation Engine (jointly with IBM and intel)  Future:  Handling “Unknown Unknowns”: Extending DSPLs with dynamic learning and evolution 23
  23. 23. ©paluno The research leading to these results has received funding from the European Union's Seventh Framework Programme FP7/2007- 2013 under grant agreement 610802 (CloudWave) http://www.cloudwave-fp7.eu/ Thank You!

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