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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/255839643 Slides of PhD defense Data August 2013 CITATIONS READS 0 64 1 author: Sebastian Gtz Technische Universitt Dresden

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Slides of PhD defense

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SLIDE 2

Department of Computer Science, Software Technology Group

Multi-Quality Auto-Tuning by Contract Negotiation

Verteidigung der Dissertation von Dipl.-Inf. Sebastian Götz

17.07.2013

Betreuer:

  • Prof. Dr. rer. nat. habil. Uwe Aßmann

Zweitgutachter:

  • Prof. Dr. rer. nat. habil. Heinrich Hußmann

Fachreferent:

  • Prof. Dr. rer. nat. habil. Dr. h. c. Alexander Schill
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SLIDE 3

Noise Reduction Leveler Loudness Adjustment

Motivation

Example: Audio-Processing (https://auphonic.com/)

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 2 params audio file

Generate Sound Effects Change Change Change

? ? ? ? ? ? ? ?

Configuration Qualities, Quality of Service (QoS), Non-functional Properties (NFPs)

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SLIDE 4

Context: Self-adaptive Systems

Goal: Self-adaptive Systems (SAS) Robert Laddaga 1997: "Self Adaptive Software evaluates its own behavior and changes behavior when the evaluation indicates that it is not accomplishing what the software is intended to do,

  • r

when better functionality

  • r

performance is possible.“ [L97]

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 3

Autonomic Manager

Knowledge Monitor Analyze Plan Execute

MAPE-K Loop [KC03]

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SLIDE 5

Context

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation

Internet

UMTS/LTE W-LAN LAN

QoS Demands Objectives

Slide 4

Which variant of which software should be used? How good is each variant in comparison to the others? Which resources should be utilized? How to achieve the best possible user satisfaction for the least possible cost?

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SLIDE 6

Motivation

  • User objectives relate to qualities: energy, performance, domain-specific

qualities as noise-levels, etc.

  • Often multiple, competing qualities are to be considered in combination [ST09]

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 5

Multi-Quality Auto-Tuning (MQuAT)

A novel approach to design & operate self-optimizing systems covering multiple objectives.

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SLIDE 7

Problems / Related Work

Problem 1: Developers cannot reuse solutions to build self-optimizing systems although many specific approaches exist.

  • Fixed set of considered properties (e.g., bandwidth, response time)
  • Fixed architecture (e.g., specific to servers, mobile phones or cars)
  • Fixed optimization technique (e.g., integer linear programming)

Goal: A generic approach to self-optimizing systems. Solution: A model-driven development approach to self-optimization

  • A component-based metamodel enabling the developer to specify the

properties of interest and the system‘s architecture.

  • Technology bridges to utilize multiple optimization techniques (generation
  • f optimization problems).

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 6

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SLIDE 8

Optimization Problem Description

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 7

Noise Reduction Generate Sound Effects Leveler Synchronization

? ? ? ? ? ? ? ? ? ? ?

Machine #1 Machine #2 Machine #3

…

CPU RAM Net CPU RAM Net CPU RAM Net

?

Arm Leg Board #1

…

Data-flow Graph Tree

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SLIDE 9

Problems / Related Work

Problem 2: Existing (specific) approaches do not cover dependencies between qualities.

  • Quality-contract-based approaches
  • COMQUAD  QoS characteristics (e.g., response_time < 5ms) [RZ03]
  • THESEUS  SLAs; QoS intervals (e.g., 2ms < response_time < 5ms) [S10]
  • No context-dependent QoS statements (e.g., response_time(size) = f(size))
  • Both projects identified the need to cover QoS dependencies [ZM03, S10]

Goal: Explicit coverage of (context-dependent) interaction between qualities. Solution:

  • An extended notion of quality contracts and
  • A process for quality contract refinement.

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 8

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SLIDE 10

Problems / Related Work

Problem 3: Competing qualities demand for multi-objective optimization having a high computational complexity (NP-hard) [NW99]

  • Multi-objective approaches (e.g., OCTOPUS)
  • „a priori“:

aggregation of objectives prior to optimization

  • „a posteriori“:
  • ptimization delivers set of multi-dimensional solutions

(Pareto front)

  • Optimization at runtime requires feasible, assessable time requirements

Goal: A generic, assessable runtime multi-objective optimization approach. Solution:

  • 4 runtime technology bridges to multi-objective optimization techiques.
  • Scalability analysis of supported techniques.

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 9

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SLIDE 11

Problems / Related Work

Problem 3: Competing qualities demand for multi-objective optimization having a high computational complexity (NP-hard) [NW99]

  • Multi-objective approaches (e.g., OCTOPUS)
  • „a priori“:

aggregation of objectives prior to optimization

  • „a posteriori“:
  • ptimization delivers set of multi-dimensional solutions

(Pareto front)

  • Optimization at runtime requires feasible, assessable time requirements

Goal: A generic, assessable runtime multi-objective optimization approach. Solution:

  • 4 runtime technology bridges to multi-objective optimization techiques.
  • Scalability analysis of supported techniques.

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 10

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SLIDE 12

Part 2: Runtime System Hardware Infrastructure Developer Part 1: Development

Overview

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 11

Users

Request Multiple Objectives Runtime Model Running Components Multiple Objective Function Computation Runtime Optimization

(Contract Negotiation)

Models CCM QoS Contracts Code Bench- marks QCL Refine- ment Genericity / Reuse QoS Dependencies Runtime MOO

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SLIDE 13

PART 1: DEVELOPMENT

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 12

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SLIDE 14

Cool Component Model [GWS+10]

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 13

Cool Component Model Structure Models (i.e., types) Variant Models (i.e., instances)  runtime

<<instance of>>

Quality Contract Language

<<refined by>>

Behavior Models

<<enrich>>

Expressions Units DataTypes Requests Reconfigurations Workloads

Base Layer Core Layer SAS Layer

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SLIDE 15

Cool Component Model [GWS+10]

  • Example CCM Structure Model for Servers:
  • Example Unit Library

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 14 Server Net CPU RAM DbxCard

clock_rate: GHz performance : FLOP/s cpuLoad : Percent cpu_time : Second free : GB = total – used used : GB total : GB throughput : GB/s bandwidth : Mb/s time : Second threshold : dB amplification : dB 1..* 1..* 1..* 1..* 1..* <<container>>

NoiseReduction

<meta> audio_length : Second response_time : Second noiseReductionLevel: dB apply

  • Example CCM Structure Model for Sort:

library { simple unit Watt : Integer simple unit Second : Integer; simple unit dB : Real; complex unit Joule = Watt Second; factor KW = 1000 Watt; }

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SLIDE 16

Quality Contract Language [GWC+12a]

Quality Modes Software Dependencies Resource Dependencies Quality Provisions Contracts characterize implementations 17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 15 Quality Modes

1 contract Dbx implements NoiseReduction.apply { 2 3 mode professional { 4 requires component SpecialNoiseReduction { 5 min capability: 100 [percent] 6 } 7 8 requires resource DbxCard { 9 min <time>(audio-length) [ms] 10 } 11 12 provides min noiseReductionLevel: 25 dB 13 provides min <response_time>(audio_length) [s] 14 } 15 16 mode amateur { 17 /* More requirements and provisions here ... */ 18 } 19 }

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SLIDE 17

Contract Refinement [GWC+12b]

  • Target systems and user input are unknown to developer.
  • Developer creates contract templates:
  • Developer creates Benchmark Suite using Profiler Framework [WGR13]

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 16

contract Dbx implements NoiseReduction.apply { mode professional { ... provides min response_time:

<response_time>(audio_length) [s]; } ... }

for(i = 0; i <= N; i++) { Profiler.getProfiler(„response_time“).start(); dbx.apply(sample_files[i]); Profiler.getProfiler(„response_time“).stop(); }

NoiseReduction

<meta> audio_length : Second response_time : Second noiseReductionLevel: dB apply

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SLIDE 18
  • Target systems and user input are unknown to developer.
  • Developer creates contract templates:
  • Benchmarks executed at deployment time on each target machine:

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 17 audio_length response_time 1s 2s ... 120s 945ms 1823ms ... 110215ms <response_time>(audio_length) [s] 1.147*10^(-6)*audio_length^2-1922 [s];

One contract per machine and implementation.

Contract Refinement [GWC+12b]

contract Dbx implements NoiseReduction.apply { mode professional { ... provides min response_time:

<response_time>(audio_length) [s]; } ... } NoiseReduction

<meta> audio_length : Second response_time : Second noiseReductionLevel: dB apply

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SLIDE 19

PART 2: RUNTIME

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 18

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SLIDE 20

Contract Negotiation

17.07.2013 Slide 19

… denotes a global

  • ptimization

problem

  • f

a system

  • f

components, which are known and controllable by central coordinators as known from the self-adaptive system‘s community.

Optimal System Configuration Running System Optimization Problem Formulation Trans- formation Standard Solver Rekonf- iguration System Models Monitor ILP PBO ACO MOILP Accuracy Exact Approx. Objectives Single Multiple

[AGJ+13]

Multi-Quality Auto-Tuning by Contract Negotiation

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SLIDE 21

Contract Negotiation by ILP [GWC+11]

  • Base: Integer Linear Programming (ILP)
  • Goal: determine the variable assignment, which
  • Maximizes objective function and
  • Adheres to the constraints.
  • Avoids pruning of whole search space (worst case)

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 20

Variables Objective Constraints

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SLIDE 22

Contract Negotiation by ILP [GWC+11]

Decision Variables Select Impl. Map to HW NFP Provisions NFP Requirements

Knapsack

Resource Provisions Resource Requirements fixed

Knapsack

Architectural Constraints Objective Function ILP Constraints 30.01.2013

CCM Variant Model Runtime Description of Hard- & Software Infrastructure CCM Structure Model Architecture of Hard- & Software System QCL Contracts Characterizing Non-functional Behavior of Implementations CCM Behavior Models

17.07.2013 Slide 21 Multi-Quality Auto-Tuning by Contract Negotiation

  • Integer Linear Programming (ILP)

Usage Variables

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SLIDE 23

ILP by Example

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 22

/* objective function: minimize energy consumption (based on cpu_time) */ min: 5700.0 b#Quicksort#delayed#R1 + 495.0 b#UnsortedFilter#slow#R1 + 10285.0 b#Quicksort#immediate#R1 + 6160.0 b#Javasort#immediate#R1 + 385.0 b#UnsortedFilter#fast#R1 + 2250.0 b#Random#slow#R1 + 5940.0 b#Javasort#delayed#R1 + 2695.0 b#Random#fast#R1; /* architectural constraints */ b#Random#fast#R1 + b#Random#slow#R1 = b#Quicksort#delayed#R1 + b#Quicksort#immediate#R1 + b#Javasort#immediate#R1 + b#Javasort#delayed#R1; b#UnsortedFilter#fast#R1 + b#UnsortedFilter#slow#R1 = 1; b#Quicksort#immediate#R1 + b#Quicksort#delayed#R1 + b#Javasort#immediate#R1 + b#Javasort#delayed#R1 = b#UnsortedFilter#slow#R1 + b#UnsortedFilter#fast#R1; /* resource negotiation */ usage#R1#Core[TM]_i7_CPU_Q_720_@_1.60GHz#frequency <= 1596.0; usage#R1#Core[TM]_i7_CPU_Q_720_@_1.60GHz#frequency >= 0; usage#R1#Core[TM]_i7_CPU_Q_720_@_1.60GHz#frequency = 100 b#Javasort#delayed#R1 + 100 b#UnsortedFilter#slow#R1 + 100 b#Quicksort#delayed#R1 + 300 b#Random#fast#R1 + 300 b#Quicksort#immediate#R1 + 100 b#Random#slow#R1 + 300 b#Javasort#immediate#R1 + 300 b#UnsortedFilter#fast#R1; ...

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SLIDE 24

ILP by Example

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 23

... /* software NFP negotiation */ Sort#response_time = 382.05714282441 b#Quicksort#delayed#R1 + 377.31428570997804 b#Quicksort#immediate#R1 + 399.771428570494 b#Javasort#immediate#R1 + 416.34285718949195 b#Javasort#delayed#R1; Filter#response_time = 23.921216866850248 b#UnsortedFilter#slow#R1 + 28.407017552658598 b#UnsortedFilter#fast#R1; ListGen#response_time = 107.6078431285458 b#Random#slow#R1 + 106.7843137012918 b#Random#fast#R1; Sort#response_time >= 50 b#UnsortedFilter#fast#R1; ListGen#response_time >= 50 b#Quicksort#delayed#R1 + 50 b#Javasort#immediate#R1 + 50 b#Javasort#delayed#R1; /* user request */ Filter#response_time <= 200.0; /* boolean restriction */ binary b#Quicksort#delayed#R1, b#UnsortedFilter#slow#R1, b#Quicksort#immediate#R1, b#Javasort#immediate#R1, b#UnsortedFilter#fast#R1, b#Random#slow#R1, b#Javasort#delayed#R1, b#Random#fast#R1;

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SLIDE 25

Contract Negotiation by MOILP

17.07.2013 Slide 24

Variables Objective 1 Objective 2 Objective 3 Constraints Variables Objective 1 Objective 2 Objective 3 Constraints Solution Pareto Front Derived Constraints Variables Objective 1 Objective 2 Objective 3 Constraints Derived Constraints Solution Solution

Klein und Hannan ´82

Multi-Quality Auto-Tuning by Contract Negotiation

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SLIDE 26

Contract Negotiation by MOILP

17.07.2013 Slide 25

Pareto Front

. . .

Multi-Quality Auto-Tuning by Contract Negotiation Quadradic Growth until Termination

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SLIDE 27

Scalability Analysis [GWR+13]

  • Performed on data-flow graphs (pipe-and-filter style)
  • Measurements taken for C x S systems from C = [2..100] and S = [2..100]
  • All measurements made on Alienware X51 (Win7 64bit, SSD HDD, 8GB

DDR1600 RAM, Intel Core i7-2600 with 4 physical cores at 3.4GHz)

  • Concrete numbers will differ on other machines, solvers, etc.
  • Focus on principle findings.

C components S servers

Slide 26 17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation

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SLIDE 28

Scalability Analysis: ILP [GWR+13]

Slide 27 17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation

Predictable up to 25 Components Reason: Worst-case situations

Solving Time [ms]

Timeout: 2min 3rd Quartile: 26,58s

Feasible up to 100x100

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SLIDE 29

Scalability Analysis: MOILP

Slide 28

Solving Time for 2 Objective Functions

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation

3rd Quantile: 62,92 s The jump is due to heuristics in solver.

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SLIDE 30

Scalability Analysis: MOILP

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 29

Size of Pareto Front for 2 Objective Functions Large Pareto-fronts even for small systems

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SLIDE 31

Scalability Analysis: MOILP

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 30

Solving Time for 3 Objective Functions Infeasible due to quadratic explosion.

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SLIDE 32

Part 2: Runtime System Developer Part 1: Development

Contributions

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 31

Users

Request Multiple Objectives Runtime Model Running Components Multiple Objective Function Computation Runtime Optimization

(Contract Negotiation)

Genericity / Reuse QoS Dependencies Runtime MOO Models CCM

[GWS+10]

QoS Contracts Code Bench- marks QCL Refine- ment

[GWC+12a] [GWC+12b]

ILP PBO ACO MOILP

[GWC+11] [GWR+13]

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SLIDE 33

Part 2: Runtime System Developer Part 1: Development

Contributions

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 32

Users

Request Multiple Objectives Runtime Model Running Components Multiple Objective Function Computation Runtime Optimization

(Contract Negotiation)

Genericity / Reuse QoS Dependencies Runtime MOO Models CCM

[GWS+10]

QoS Contracts Code Bench- marks QCL Refine- ment

[GWC+12a] [GWC+12b]

ILP PBO ACO MOILP

[GWC+11] [GWR+13]

Developers are not restricted to prescribed non- functional properties and architectural elements. Context-dependent interdependencies of multiple qualities are supported. Realized and analyzed four runtime MOO techniques as technology bridges.

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SLIDE 34

Future Work

  • Bootstrapping: MQuAT for Monitoring, Optimization and Reconfiguration
  • Both are components with different implementations, too.
  • Scalability analysis is a first step for the optimization component
  • Collaboration planned with Prof. Fischer (Numerical Optimization Group)
  • Green Software Engineering (CRC 912: HAEC, NFG ZESSY)

[WGR+11, WRP+12, WRP+13, WGR13, GMT+13, WRG+13a, WRG+13b]

  • Open Challenges: Sustainability, Negotiation of Energy-Sources (Solar,

Battery, Provider, etc.)

  • Software Engineering for Robotic and Cyber-Physical Systems

[GLR+11, GLP+12, PRG+12]

  • Open Challenge: Optimization across discrete and continuous system

parts

17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 33

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SLIDE 35

Own Related Publications

[GWS+10] S. Götz, C. Wilke, M. Schmidt, S. Cech and U. Assmann. Towards Energy Auto

  • Tuning. In: Proceedings of First Annual International Conference on Green Information

Technology, GREEN IT 2010, GSTF (2010) p. 122-129. [GWC+11] S. Götz, C. Wilke, S. Cech and U. Assmann. Runtime Variability Management for Energy-efficient Software by Contract Negotiation. In Proceedings of the 6th International Workshop on Models@run.time, ACM/IEEE (2011) p. 61-72. [GWC+12a] S. Götz, C. Wilke, S. Cech and U. Assmann. Architecture and Mechanisms of Energy Auto Tuning. In Sustainable ICTs and Management Systems for Green Computing. IGI Global (2012) p. 45-73. [GWC+12b] S. Götz, C. Wilke, S. Richly and U. Assmann. Approximating Quality Contracts for Energy Auto-Tuning Software. In Proceedings of First International Workshop on Green and Sustainable Software (GREENS'12), IEEE (2012) p. 8-14. [GWR+13] S. Götz, C. Wilke, S. Richly and U. Aßmann. Model-driven Self-Optimization using Integer Linear Programming and Pseudo-Boolean Optimization. In Proceedings of the Fifth International Conference on Adaptive and Self-Adaptive Systems and Applications (ADAPTIVE), XPS Press (2013) p. 55-64. [AGJ+13] U. Assmann, S. Götz, J.-M. Jezequel, B. Morin and M. Trapp. Uses and Purposes of M@RT Systems. To appear in State-of-the-Art Survey Volume on Models@run.time. Springer LNCS, 2013. 17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 34

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SLIDE 36

Own Future Work Publications

[WRG+13b] C. Wilke, S. Richly, S. Götz, C. Piechnick and U. Aßmann. Energy Consumption and Efficiency in Mobile Applications: A User Feedback Study. To appear in Proceedings of the IEEE International Conference on Green Computing and Communications (GreenCom), 2013. [WRG+13a] C. Wilke, S. Richly, S. Götz, and U. Aßmann. Energy Profiling as a Service. To appear in GI Proceedings of Workshop "Umweltinformatik zwischen Nachhaltigkeit und Wandel" (UINW), 2013. [GMT+13] S. Götz, J. Mendez, V. Thost and A.-Y. Turhan. OWL 2 Reasoning To Detect Energy- Efficient Software Variants From Context. To appear in Proceedings of the 10th OWL: Experiences and Directions Workshop (OWLED), 2013. [PRG+13] G. Püschel, S. Götz, C. Wilke and U. Aßmann. Towards Systematic Model-based Testing of Self-adaptive Systems. In Proceedings of The Fifth International Conference on Adaptive and Self-Adaptive Systems and Applications (ADAPTIVE), XPS Press (2013), p. 65-70. [WGR13] C. Wilke, S. Götz and S. Richly. JouleUnit – A Generic Framework for Software Energy Profiling and Testing. In Proceedings of the 1st Workshop "Green In Software Engineering Green By Software Engineering" (GIBSE), ACM/IEEE (2013) p. 9-13. [WRP+13] C. Wilke, S. Richly, C. Piechnick, S. Götz, G. Püschel and U. Aßmann. Comparing Mobile Applications’ Energy Consumption. In Proceedings of The 28th Annual ACM Symposium on Applied Computing (SAC 2013), ACM (2013) p. 1177-1179. 17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 35

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SLIDE 37

Own Future Work Publications

[WRP+12] C. Wilke, S. Richly, G. Püschel, C. Piechnick, S. Götz and Uwe Assmann. Energy Labels for Mobile Applications. To appear in Proceedings of 1. Workshop zur Entwicklung energiebewusster Software / First Workshop for the Development of Energy-aware Software (EEbS 2012), 2012. [WGR+11] C. Wilke, S. Götz, J. Reimann and U. Assmann. Vision Paper: Towards Model-Based Energy Testing. In Proceedings of 14th International Conference on Model Driven Engineering Languages and Systems (MODELS 2011), Springer (2011) p. 480-489 [PRG+12] C. Piechnick, S. Richly, S. Götz, C. Wilke and U. Aßmann. Using Role-Based Composition to Support Unanticipated, Dynamic Adaptation - Smart Application Grids. (Best Paper Award) In Proceedings of The Fourth International Conference on Adaptive and Self-Adaptive Systems and Applications (ADAPTIVE), XPS Press (2012) pp. 93-102 [GLP+12] S. Götz, M. Leuthäuser, C. Piechnick, J. Reimann, S. Richly, J. Schroeter, C. Wilke und U. Aßmann. Entwicklung cyber-physikalischer Systeme am Beispiel des NAO Roboters. In Proceedings of Chemnitz Linux-Days, Universitätsverlag Chemnitz (2012) p. 42-52 [GLR+11] S. Götz, M. Leuthäuser, J. Reimann, J. Schroeter, C. Wende, C. Wilke and U.

  • Assmann. A Role-based Language for Collaborative Robot Applications. In Proceedings of

1st International ISoLA Workshop on Software Aspects of Robotic Systems (ISOLA SARS 2011), Springer (2011) p. 1-15 17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 36

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SLIDE 38

Literature

[KC03] J. O. Kephart and D. M. Chess. The vision of autonomic computing. In: IEEE Computer, 36:41-50, January 2003. [L97] R. Laddaga. DARPA self adaptive software broad agency announcement (baa) 98-12 proposer information pamphlet - excerpt. http://people.csail.mit.edu/rladdaga/BAA98- 12excerpt.html, December 1998. [NW99] G. L. Nemhauser and L. A. Wolsey. Integer and Combinatorial Optimization. Wiley Interscience, 1999. [RZ03] S. Röttger and S. Zschaler. CQML+: Enhancements to CQML. In Proceedings of the 1st International Workshop on Quality of Service in Component-Based Software Engineering, pages 43-56. Cepadues-Editions, 2003. [ST09] M. Salehie and L. Tahvildari. Self-adaptive software: Landscape and research

  • challenges. ACM Trans. Auton. Adapt. Syst., 4:14:1-14:42, May 2009.

[S10] Josef Spillner: Methodik und Referenzarchitektur zur inkrementellen Verbesserung der Metaqualität einer vertragsgebundenen, heterogenen und verteilten Dienstausführung.

  • Dissertation. TU Dresden. 2010.

[ZM03] S. Zschaler and M. Meyerhöfer. Explicit Modelling of QoS-Dependencies. In Proceedings

  • f the 1st International Workshop on Quality of Service in Component-Based Software

Engineering, p. 57-66. Cepadues-Editions, 2003. 17.07.2013 Multi-Quality Auto-Tuning by Contract Negotiation Slide 37

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