PhD Thesis Defense

Filip KřikavaPhD defense

November 22, 2013, Sophia Antipolis

CNRS, University of Nice Sophia Antipolis, I3S Laboratory, MODALIS research group

Advisors: Philippe Collet and Johan Montagnat

Domain-Specific Modeling Language for Self-Adaptive Software System

Architectures

Context and Motivation

Jeff Kephart - Autonomic Computing: The First Decade, ICAC’11 keynote

Electronic Retailer - Application Diagram

• Ever growing complexity of computing systems• Taming this complexity by skilled IT professionals does not scale• New operation modes are needed

2

Towards Self-Adaptive Software Systems

• Towards Self-Adaptive Software Systems• Realizing system that adjusts themselves in accordance with some higher-level goals

3

Monitoring Reconfiguration

Decision Making

sensors effectors

Target System

events actions

measures decisions

Feedback Control Loop (FCL)

Towards Self-Adaptive Software Systems

• Towards Self-Adaptive Software Systems• Realizing system that adjusts themselves in accordance with some higher-level goals

3

Challenges

4

Engineering of self-adaptive software systems is a challenge

Designing an adaptation engine

Controller Target System

Transducer

+ -

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Integrating the adaptation engine into the target system

API

LOGS

CONFIGURATIONS

COMMANDS

AOP

PROFILER

Challenges

4

Engineering of self-adaptive software systems is a challenge

Designing an adaptation engine


Transducer

+ -

� =mX

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API

LOGS

CONFIGURATIONS

COMMANDS

AOP

PROFILER

Challenges

5


Adaptation Engine Target System

• Extensive handcrafting of non-trivial code• Cumbersome and error-prone• Accidental complexities

Adhoc Implementations


Transducer

+ -

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API

LOGS

CONFIGURATIONS

COMMANDS

AOP

PROFILER

Challenges

6


Adaptation Engine Target SystemReusing and adapting existing work


Transducer

+ -

� =mX

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API

LOGS

CONFIGURATIONS

COMMANDS

AOP

PROFILER

• Often target specific types of adaptation problems (Bertran’12)• Require the use of certain adaptation mechanism (Garlan’04)• Applicable to single domain (Rouvoy’08) or technology (Asadollahi’09)• Do not support remoting or complex control schemes (Adamczyk’08, Gorton’06)• Limiting their applicability

Requirements

7

Identified requirements for integrating self-adaptation into software systems

Generality

• Domain-agnostic• Technology-agnostic

• Explicit FCLs, their process and interactions• Verification support

Visibility

• Reusable FCL parts across adaptation scenarios

Reusability

Requirements

7


Generality



Visibility


Reusability

• Remote distribution of FCL

Distribution

• Composition• Reflection

Complexcontrol

Requirements

7


Generality



Visibility


Reusability

• Remote distribution of FCL

Distribution

• Composition• Reflection

Complexcontrol

• Prototyping• Automating

Tooling

Lightweight approach

Objectives

Provide an approach to facilitate systematic integration of self-adaptive mechanisms into software systems through feedback control loops.

8

“”

Contributions

9

Feedback Control Definition Language

The ACTRESS Modeling Environment

The SIGMA Model Manipulation Languages


Decision Making

sensors effectors

Target System

events actions

measures decisions


Con

trib

utio

ns

Facilitates language

use

Facilitates tooling

implementation


Transducer

+ -

� =mX

i=1

1

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m

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md

µ

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API

LOGS

CONFIGURATIONS

COMMANDS

AOP

PROFILER

Generality Visibility Reusability Distribution Complex control

Tooling


Plan

10

1. Feedback Control Definition Language

2. The ACTRESS Modeling Environment

3. The Sigma Model Manipulation Languages

4. Conclusions and Perspectives

Plan

11






12

1. Raise the level of abstraction

2. Fine-grained decomposition of FCL elements

3. Explicit interactions

4. Provide reflection and composition capabilities

5. Embed remoting

Monitoring Decision Making Reconfiguration

measures

events

decisions

actions

General Purpose Language (GPL)

Generality

Visibility

Reusability

Distribution

Complexcontrol


13




4. Provide reflection capabilities

5. Embed remoting

Domain-Specific Modeling


13





5. Embed remoting



Decision Making

sensors effectorsevents actions

measures decisions


13





5. Embed remoting


• Feedback Control Loop• Sequence of interconnected processes• Inputs x State -> Output• Reactive• Concurrent• Dynamic


Decision Making


measures decisions


13





5. Embed remoting




Decision Making


measures decisions

• The Actor Model• Network of actors communicating via

message passing• Message x State -> Message(s)• Reactive• Concurrent• Dynamic

actor

actor

actor


14


• The Actor Model• Network of actors communicating via

message passing• Message x State -> Message(s)• Reactive• Concurrent• Dynamic• Thread safe• Scalable• Remoting support• Programming support


Decision Making


measures decisions

actor

actor

actor

Feedback Control Definition Language - Adaptive Element

15


Decision Making


measures decisions

Processor

in input

out output

Effector

in input

Sensor

out output

Target System

in input

Controller

in input

out output

Processor

out output

Feedback Control Definition Language - Adaptive Element

15


Decision Making


measures decisions

Processor

in input

out output

Effector

in input

Sensor

out output

Target System

in input

Controller

in input

out output

Processor

out output

in input

providedEffectorprovidedSensor

out output

Feedback Control Definition Language - Illustration

• Typical example of a control-based solution to a well-known and well-scoped problem• Design of a sophisticated control mechanisms• Integration left for adhoc implementation

16

QoS management control of web servers by content delivery adaptation


17

Idea: service time = fixed overhead + data-size dependent overhead

Abdelzaher et al., 1999, 2002

QoS management control of web servers by content delivery adaptationGoal: maintain server load around some pre-set value

Prerequisite: preprocessed content (different quality and size)


17




/1/photo.jpg

/2/photo.jpg

/photo.jpgfull quality

degraded quality



17




/1/photo.jpg

/2/photo.jpg


degraded quality

normal load

overload



17




...

serve fromtree #2

serve fromtree #1

Rejection Level

Minimum Content Full Content

Goal: maintain server load around some pre-set value

/1/photo.jpg

/2/photo.jpg


degraded quality

normal load

overload



17




...

serve fromtree #2

serve fromtree #1

Rejection Level



/1/photo.jpg

/2/photo.jpg


degraded quality

normal load

overload



17




...

serve fromtree #2

serve fromtree #1

Rejection Level


Generality Visibility Reusability

Using FCDL


/1/photo.jpg

/2/photo.jpg


degraded quality

normal load

overload



17




...

serve fromtree #2

serve fromtree #1

Rejection Level


Distribution Complex control


Using FCDL


/1/photo.jpg

/2/photo.jpg


degraded quality

normal load

overload



18

3. Compute severity of adaptation (G)

2. Compute the requests rate (R), bandwidth (W) and utilization (U)

1. Compute the number of requests (r) and size of responses (w)

1. Compute the number of requests (r) and size of responses (w)

accessLog: FileTailer

out lines

file=/var/log/apache2/access.log


access_log

19

active sensor FileTailer { push out port lines: String

property file: String}

processor Accumulator { push in port input: long pull out port sum: long}

processor AccessLogParser { push in port lines: String push out port size: int push out port requests: int}

Generality

Visibility

Reusability

requestCounter: Accumulator

responseSizeCounter: Accumulator

in input

out sum out sum

in lines

out sizeout requests

accessLogParser: AccessLogParser

access_log


20

active processor PeriodTrigger<T> { pull in port input: T push out port output: T

property initialPeriod = 10.seconds}

Generality

Visibility

Reusability



scheduler: PeriodTrigger

loadMonitor: LoadMonitor

in input

out sum out sum

in requests in size

out utilization

in input

out output

initialPeriod=10s


in lines

out lines

file=/var/log/apache2/access_log



2. Compute the requests rate (R), bandwidth (W) and utilization (U)

content_tree


access_log

21

Generality

Visibility

Reusability

utilController: UtilizationController





in input

out sum out sum

in requests in size

out utilization

in input

out output

in utilization

out contentTreeinitialPeriod=10s

adaptor: ContentAdaptor

in contentTree


in lines

out lines




k=?U*=?

3. Compute severity of adaptation (G)


22

composite ApacheQOS {

feature accessLog = new FileTailer { file = “/var/log/apache2/access_log” }

feature accessLogParser = new AccessLogParser feature requestCounter = new Accumulator feature responseSizeCounter = new Accumulator feature loadMonitor = new LoadMonitor feature scheduler = new PeriodTrigger<Double> feature utilController = new UtilizationController feature adaptor = new ContentAdaptor

connect accessLog.lines to accessLogParser.lines connect accessLogParser.size to responseSizeCounter.input connect accessLogParser.requests to requestsCounter.input connect requestCounter.output to loadMonitor.requests connect responseSizeCounter.output to loadMonitor.size connect loadMonitor.utilization to scheduler.input connect scheduler.output to utilController.utilization connect utilController.contentTree to adaptor.contentTree }

Complete model of the first prototype


ApacheQOSutilController

: UtilizationController

in input





in input

out sum out sum

in requests in size

out utilization

in input

out output

in utilization

out contentTree

syst

em

laye

rco

ntro

l la

yer

initialPeriod=10s


in contentTree


in lines

out lines




k=?U*=?

Feedback Control Definition Language - Composition

23

ApacheQOS utilController: UtilizationController

in input





in input

out sum out sum

in requests in size

out utilization

in input

out output

in utilization

out contentTree

out requests

out size

syst

em

laye

rco

ntro

l la

yer

composite name

initialPeriod=10s


out

size

in contentTree

out size

out

requ

ests out requests in contentTree

port promotion

in contentTree

ApacheWebServer

server: ApacheWebServer



in lines

out lines


k=?U*=?

composite ApacheWebServer {

property accessLogFile: String! feature accessLog = new FileTailer { file = accessLogFile }

feature accessLogParser = new AccessLogParser feature adaptor = new ContentAdaptor! connect accessLog.lines to accessLogParser.lines

promote accessLogParser.size promote accessLogParser.requests promote adaptor.contentTree!

}

Hierarchical organization of Adaptive Elements using composites


ApacheQOS

control: QOSControl

in contentTree

apache: ApacheWebServer

in requests

in size

out requests

out size

out contentTree

cont

rol

laye

rsy

stem

la

yer

QOSControl


utilization: UtilizationMonitor

out contentTree


out utilizationin input out output

in utilization

out contentTreein requests in size

in requests in size

Feedback Control Definition Language - Composition

24

Generality

Visibility

Reusability

LighttpdQOS

control: QOSControl

in contentTree

lighttpd: LighttpdWebServer

in requests

in size

out requests

out size

out contentTree

cont

rol

laye

rsy

stem

la

yer

QOSControl


utilization: UtilizationMonitor

out contentTree


out utilizationin input out output

in utilization

out contentTreein requests in size

in requests in size

Feedback Control Definition Language - Distribution

remote-main remote-apache

network

ApacheQOS

in contentTree

in requests

in size

out requests

out size

out contentTree

Apache.apacheendpoint= akka.tcp://actress@remote-apache/user/Apache/apache

referenced feature

Apache

in contentTree

in requests

in size

out requests

out size

out contentTree

ApacheQOS.control

endpoint=akka.tcp://actress@remote-main/user/ApacheQOS/control


control: QOSControl

composite feature

25

// deployed at remote-apachecomposite Apache { feature apache = new ApacheWebServer { ... } feature control = ref ApacheQOS.control @ "akka://remote-main/user/ApacheQOS/control"}

// deployed at host remote-maincomposite ApacheQOS { feature control = new QOSControl { ... } feature apache = ref Apache.apache @ "akka://remote-apache/user/Apache/apache" // ...}

Remote Distribution of Adaptive ElementsDistribution

Visibility

Feedback Control Definition Language - Reflection

26

ApacheQOS

cont

rol

laye

r

sysLoad: SystemLoad

met

a-co

ntro

l la

yer

periodController: PeriodController

out output

in load out period

syst

em

laye

r

sysLoadTrigger: PeriodTrigger

in input

out output

in contentTree


in requests

in size

out requests

out size

out contentTreeQOSControl


in input out output

setPeriod

provided in setPeriod

... ...

provided effectorpromotion


control: QOSControl

Adaptive Element Reflection

active processor PeriodicTrigger<T> { pull in port input: T push out port output: T

provided effector setPeriod: Long

property initialPeriod = 10.seconds}

composite QOSControl { // ... promote scheduler.setPeriod // ...}

Visibility

Complexcontrol

Feedback Control Definition Language - Reflection

27

Adaptive Monitoring Pattern

ApacheQOS

cont

rol

laye

r

sysLoad: SystemLoad

met

a-co

ntro

l la

yer

out output

in load out period

syst

em

laye

r

in contentTree


in requests

in size

out requests

out size

out contentTreeQOSControl


in input out output

setPeriod


... ...

provided effectorpromotion


control: QOSControl

periodControl: AdaptiveMonitoring

Reusability

Visibility

Complexcontrol

Feedback Control Definition Language - Interaction Contracts

28

in input

: Accumulator

out sum

• Activated on input push request• Activated on sum pull request

in input

: Accumulator

out sumin reset

out output


29

• Activated on input push request• Activated on reset push request• Activated on input / reset request• Activated on sum pull request• Activated on input push request always pushing data on output• Activated on reset push request always pushing data on output• ....

in input

: Accumulator

out sumin reset

out output


30

• Behavior not explicitly stated in the architecture - Black Box• Limits verification support• Limits code-generation support

When it receives data on its input port, it pushes to its output port the input value plus the sum of all the input values it has received since the last time the reset port was triggered, similarly, when pulled on the sum port, it returns the sum of all the input values since the last reset, and finally receiving any data on its reset port, sets the current accumulated value back to 0.

if (!input.isEmpty()) { value += input.get output.send(value) } else if (!reset.isEmpty()) { reset.get() value = 0 } else if (!sum.isEmpty()) { sum.send(value) } else { throw new IllegalStateException("Invalid execution") }

in input

: Accumulator

out sumin reset

out output


31

Interaction ContractsDescribe allowed interactions among components

Visibility

processor Accumulator { push in port reset: int push in port input: long pull out port sum: long push out port output: long

act onInput(input; ; output) act onSum(sum; ; ) act onReset(reset; ; )}

in input

: Accumulator

out sumin reset

out output


31

Interaction ContractsDescribe allowed interactions among components

Visibility

• Our extensions• Elements with multiple output ports• Multiple port connections• Composites• Composite interaction contract inference algorithm • Optional contracts• Completion verification algorithm

Originally developed by Cassou et al. for SCC systems

processor Accumulator { push in port reset: int push in port input: long pull out port sum: long push out port output: long

act onInput(input; ; output) act onSum(sum; ; ) act onReset(reset; ; )}

Plan

32





ACTRESS Overview

33

The ACTRESS Modeling Environment

Modeling

VerificationCode Generation

Tooling

• Reference implementation of FCDL based on Eclipse Modeling Framework• Facilitate the use of FCDL

ACTRESS - Modeling Support

34

controller UtilizationController { // ...

// interaction contract act activate(utilization; ; contentTree) // beginning of Xbase implementation implementation xbase { var G = M

// interaction contract act activate { // computes the error val E = targetUtilization - utilization // computes new extend of adaptation G = G + k * E

// correct bounds if (G < 0) G = 0 if (G > M) G = M // returns the result G }}

• Textual DSL for authoring FCDL models• Additionally supports

• Modularity• Java interoperability• Implementation specification using Xbase

• Eclipse IDE support

xFCDL (Extended FCDL)


in utilization

out contentTree

k=?U*=?

ACTRESS - Code Generation Support

35

accessLogParser accessLog adaptor

apache control

requestCounter

responseSizeCounter

loadMonitor

utilization

scheduler utilController

ApacheQOS

ACTRESS Runtime

actor

actor with event listener

compositeactor

containment

messagepassing

ApacheQOS utilController: UtilizationController

in input





in input

out sum out sum

in requests in size

out utilization

in input

out output

in utilization

out contentTree

out requests

out size

initialPeriod=10s

in contentTree

server: ApacheWebServer

k=?U*=?

Code generator

Model-to-Texttransformation

ACTRESS Domain

Framework

FCDL Model

Executable System

Xbase compiled to JavaSkeleton implementation

ACTRESS - Code Generation Support

36

@SuppressWarnings("all")public class UtilizationControllerAct extends AdaptiveElementAct {

// ...

protected double activate(final double utilization) { // TODO: compute and output value for contentTree port }

// ...

}


in utilization

out contentTree

k=?U*=?

Skeleton Implementation

PrescriptiveDescriptive

Visibility

ACTRESS - Verification Support

37

FCDL Verification


37

FCDL Verification

Model Well-Formedness

• Data-type compatibility• Port connections• Required properties• and others

meta-model constraints(SIGMA)


37

FCDL Verification




Architecture

interaction contracts verification

(SIGMA)

• Consistency• Determinacy• Completeness


37

FCDL Verification




Architecture


(SIGMA)


@OCL(invDifferentSource="self.ports->select(p | p.name = 'size' || p.name = 'requests') // select ports ->collect(p | p.connections) // select their connects->collect(p | p.parent) // select owning instances->asSet()->size() == 2 // there must be two different ones")processor LoadMonitor {

User Structural Constraints

xFCDL OCL Annotations


37

FCDL Verification




Architecture


(SIGMA)


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User Temporal Constraints

FCDL to PROMELAtransformation

SPIN model checker

@OCL(invDifferentSource="self.ports->select(p | p.name = 'size' || p.name = 'requests') // select ports ->collect(p | p.connections) // select their connects->collect(p | p.parent) // select owning instances->asSet()->size() == 2 // there must be two different ones")processor LoadMonitor {

User Structural Constraints

xFCDL OCL Annotations

ACTRESS Evaluation

38

HTCondor Client Host

schedd

DAGMan 1

DAGMan 2

DAGMan N

User Interface

Submitworkflows

...

Executes

HTCondor clusterof worker nodes

Users

Spawns

Submit jobs

Case Study 1

ACTRESS Evaluation

38

HTCondor Client Host

schedd

DAGMan 1

DAGMan 2

DAGMan N

User Interface

Submitworkflows

...

Executes


Users

Spawns

Submit jobs

Case Study 1

User Interface 1

User Interface 2

User Interface N

Executes


Users

Users

Users

Submit

workflows

Submit jobs

...Central schedd

Case Study 2

FCDL / ACTRESS Evaluation

39

Generality Visibility

Reusability• Reused elements across scenarios

• Explicit FCLs• Explicit AE • Explicit AE interactions

• Potentially any self-* property• FCDL is technology agnostic• xFCDL with Xbase depends on Java• ACTRESS runtime depends on Java


39





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N! = 1000, Nc = 5000, p = 5, !0 = 10

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1

2

1 2

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39





Distribution

Complex control

• Case Study 2 deployed on 10 Grid5000 hosts

• All case studies use hierarchical control • Eclipse based modeling environment

Tooling


N! = 1000, Nc = 5000, p = 5, !0 = 10

/RDG&RQWUROOHU

1

2

1 2

/RFDO&RQWURO


Reuse


40


Reusability Distribution

Complex control

• Reused elements across scenarios

• All case studies use hierarchical control



N! = 1000, Nc = 5000, p = 5, !0 = 10

/RDG&RQWUROOHU

1

2

1 2

/RFDO&RQWURO


Overall implementation effort

• Eclipse based modeling environment

Tooling




41


Reusability Distribution

Complex control

• Reused elements across scenarios

• All case studies use hierarchical control



N! = 1000, Nc = 5000, p = 5, !0 = 10

/RDG&RQWUROOHU

1

2

1 2

/RFDO&RQWURO


Overall implementation effort

• Eclipse based modeling environment

Tooling

Separation of concerns



Plan

42





SIGMA

43

Eclipse Modeling Framework (EMF)

ACTRESS Modeling Environment

Model Manipulation

Model Consistency Checking

Model Transformations

SIGMA

43



Model Manipulation



GPL• Low level of abstraction• Limited expressiveness• Accidental complexities

SIGMA

43



Model Manipulation




• Limited tool support• Limited versatility• Limited interoperability• Limited testability• Accidental complexities

External DSLs

SIGMA

43



Model Manipulation




• Limited tool support• Limited versatility• Limited interoperability• Limited testability• Accidental complexities

External DSLs

• Internal DSLs in Scala for model manipulation• Embeds domain-specific constructs into a GPL• Small API• Efficient• Directly reuse the host language infrastructure

Internal DSLs

SIGMA


SIGMA

Quick Example

Check that all instances within a composite have unique names

SIGMA

Quick Example


invariant UniqueNamesWithinType: parent.allFeatures->forAll(e | e <> self implies e.name <> self.name);

OCL

SIGMA

Quick Example



OCL

def invUniqueNamesWithinType = self.parent.allFeatures forall (e => e != self implies e.name != self.name)

SIGMA

SIGMA

Quick Example


• Similar expressiveness• Easy to edit• Easy to test

• Easy to debug• Easy to integrate into model

manipulation workflow


OCL


SIGMA

SIGMA

Quick Example


• Similar expressiveness• Easy to edit• Easy to test

• Easy to debug• Easy to integrate into model

manipulation workflow

def invUniqueNamesWithinType = self.parent.allFeatures find (e => e != self && e.name == self.name) match { case None => Passed case Some(e) => Error(s”Element $e has the name”) .quickFix(“Rename ‘${self.name}’ to ‘${self.name}’_2”) { self.name += “_2” } }

• Different levels of severity• User feedback

• Inconsistency repair• and more


OCL


SIGMA

SIGMA

45

Architecture

SIGMA


Scala

SIGMA EMF bridge

Model Navigation

Model-to-Model Transformation


Model Modification

Model-to-Text Transformation

CommonInfrastructure

Task-specificDSLs

ANR EMERGENCEANR-09-SEGI-012

Requirements

• Tool support

• Expressiveness

• Usability

• Testability

Plan

46





Summary

• Combining self-adaptive software systems with principles of MDE to provide systematic and tooled approach for integrating control mechanisms into software applications.

• Design of a domain-specific modeling language for defining complex feedback control loop architectures on a flexible, high level of abstraction.

• A reference implementation and tools facilitating the language use including modeling, code synthesis and verification support.

• A family of internal DSLs in Scala for lightweight model manipulations including model consistency checking, model transformations.

47

Summary

• Combining self-adaptive software systems with principles of MDE to provide systematic and tooled approach for integrating control mechanisms into software applications.

• Design of a domain-specific modeling language for defining complex feedback control loop architectures on a flexible, high level of abstraction.

• A reference implementation and tools facilitating the language use including modeling, code synthesis and verification support.

• A family of internal DSLs in Scala for lightweight model manipulations including model consistency checking, model transformations.

47

• Materialized in 2 software stacks• The ACTRESS Modeling Environment

http://fikovnik.github.io/Actress• The SIGMA Model Manipulation DSLs

http://fikovnik.github.io/Sigma





Perspectives

• On Self-Adaptive Software Systems• Towards Higher-Level Abstractions

• Towards [email protected]

• Towards DSL composition

• On Model Manipulations• Towards deep DSL embedding

• Towards domain-specific optimization and error checking

• Taming the leaking abstraction of internal DSLs

48

Further Research Directions

• Improvements in both ACTRESS and SIGMA implementations• Extend the validation on more use cases, gather more experience

Short-term further work

mailto:[email protected]


Merci!

• P. Collet, F. Křikava, J. Montagnat, M. Blay-Fornarino, D. Manset. Issues and Scenarios for Self-Managing Grid Middleware, Workshop on Grids meets autonomic computing (GMAC '10), 2010

• F. Křikava, P. Collet, M. Blay-Fornarino. Uniform and Model-Driven Engineering of Feedback Control Systems, International Conference on Autonomic Computing (ICAC'11), 2011 short paper

• F. Křikava, P. Collet. A Reflective Model for Architecting Feedback Control Systems, International Conference on Software Engineering and Knowledge Engineering (SEKE'11), 2011

• F. Křikava, P. Collet. On the Use of an Internal DSL for Enriching EMF Models, Workshop on OCL and Textual Modelling (OCL'12), 2012

• F. Křikava, P. Collet, R. France. Actor-based Runtime Model of Adaptable Feedback Control Loops, Workshop on [email protected] (MRT'12), 2012

• F. Křikava, P. Collet, R. France. ACTRESS: Domain-Specific Modeling of Self-Adaptive Software Architectures, International Conference on Dependable and Adaptive Distributed Systems, SAC’14 (to appear)

• F. Křikava, P. Collet, R. France. Exploring Internal Domain-Specific Languages for Model Manipulations, International Conference on Programing Languages, SAC’14, short paper (to appear)

49

• EclipseCon Europe 2012 Modeling Symposium − Enriching EMF Models with Scala, Ludwigsburg, Germany• Scala Workshop 2013 − Model Manipulation Using Embedded DSLs in Scala, Montpellier, France

SIGMA model manipulation DSLs have been also presented at:



PhD Thesis Defense

Technology

adaptation engineintegrating

target system4

feedback control loops

n profilermonitoringm

challengeconfigurationsapi

d dlogsdesigning

d dintegrating

actress modeling environment3