Provisioning Dynamic QoS Adaptation for Enterprise DRE Systems March. 3, 2006 1 Provisioning Dynamic QoS Provisioning Dynamic QoS Adaptation Adaptation for Enterprise Distributed Real- for Enterprise Distributed Real- time Embedded (DRE) Systems time Embedded (DRE) Systems Vanderbilt University Nashville, Tennessee Institute for Software Integrated Systems Proposal Defense, March 3 rd , 2006 Gan Deng [email protected]www.dre.vanderbilt.edu/~dengg
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Provisioning Dynamic QoS Adaptation for Enterprise Distributed Real-time Embedded (DRE) Systems
Institute for Software Integrated Systems. Vanderbilt University Nashville, Tennessee. Provisioning Dynamic QoS Adaptation for Enterprise Distributed Real-time Embedded (DRE) Systems. Proposal Defense, March 3 rd , 2006 Gan Deng [email protected] www.dre.vanderbilt.edu/~dengg. - PowerPoint PPT Presentation
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Provisioning Dynamic QoS Adaptation for Enterprise DRE Systems
March. 3, 2006
1
Provisioning Dynamic QoS Adaptation Provisioning Dynamic QoS Adaptation for Enterprise Distributed Real-time for Enterprise Distributed Real-time
Goals of D&C Phase– Promote component reuse– Build complex applications by assembling
existing components– Automate common services configuration– Declaratively inject QoS policies into applications– Dynamically deploy components to target
heterogeneous domains – Optimize systems based on component
configuration & deployment settings
Provisioning Dynamic QoS Adaptation for Enterprise DRE Systems
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Component Deployment & Configuration (D&C)
SW DeployerDeployment
InfrastructureDeployment Tools (generic)
DeploymentInterfaces
InfrastructureInterfaces
Shipping
SWCreator2
A2A1
Deploymentrequirements
Implementations
SW Creator1
InterchangeFormats
D & CProfile
XMLSchemaGeneration
IDLGeneration
OMG D & C Spec(PIM & PSMs)
OMG Deployment & Configuration (D&C) specification (ptc/05-01-07)My research enhances the D&C spec to support dynamic QoS provisioning
Provisioning Dynamic QoS Adaptation for Enterprise DRE Systems
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Presentation Road Map
• Research Motivation • Taxonomy of Related Research • Research Challenges & Proposed Solutions• Evaluation of Success• Dissertation Timeline
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QoS Provisioning TimeDevelopment Time Deployment Time
Abs
trac
tion
Leve
lDO
CM
iddl
ewar
ePr
ogra
mm
ing
Lang
uage
s
Development-time QoS DOC
Middleware(RT-CORBA, RTSJ)
Deployment-time QoS DOC
Middleware(QuO,
dynamicTAO)
Development-time Aspect-Oriented
Languages(AspectJ,
AspectC++)
Com
pone
ntM
iddl
ewar
e
Runtime
Development-time QoS Component
Middleware(CIAO, Real-time EJB)
Deployment-timeQoS Component
Middleware(Qosket)
Runtime QoS Component
Middleware(SOFA, BARK,DynamicCIAO)
Deployment-time QoS Languages
(Java Adaptation Class, Specialization Class)
Runtime QoS DOC
Middleware(ACT Generic
CORBA Proxy)
Runtime QoS Languages
(AspectWerkz,PROSE)
Taxonomy of Research Continuum for Real-time QoS Provisioning
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Development-time QoS Provisioning• Key Ideas:
– Raise the level of abstraction by viewing QoS policies as first-class entities
– Use specialized languages features (e.g., AspectJ or AspectC++), middleware programming interfaces (e.g., Real-time CORBA), or QoS composing techniques (e.g., configuration metadata) to provision QoS statically
Component A Component B
Code Scattering Code tangling
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Related Research: Development-time QoS ProvisioningResearch Category
Related Research
Programming Language
• AspectC++ Gal et al. "On Aspect-Orientation in Distributed Real-time Dependable Systems," IEEE WORDS 2002.
• AspectJ Tsang et al. "An Evaluation of Aspect-Oriented Programming for Java-Based Real-Time Systems Development," IEEE ISORC 2004.
provisioning can not address the problem because all real-time QoS settings are fixed
Solution: Deployment-time QoS Provisioning
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QoS Provisioning TimeDevelopment Time Deployment Time
Abs
trac
tion
Leve
lD
OC
Mid
dlew
are
Prog
ram
min
gLa
ngua
ges
Development-time QoS DOC
Middleware(RT-CORBA, RTSJ)
Deployment-time QoS DOC
Middleware(QuO,
dynamicTAO)
Development-time Aspect-Oriented
Languages(AspectJ,
AspectC++)
Com
pone
ntM
iddl
ewar
e
Runtime
Development-time QoS Component
Middleware(CIAO, Real-time EJB)
Deployment-timeQoS Component
Middleware(Qosket)
Runtime QoS Component
Middleware(SOFA, BARK,DynamicCIAO)
Deployment-time QoS Languages
(Java Adaptation Class, Specialization Class)
Runtime QoS DOC
Middleware(ACT Generic
CORBA Proxy)
Runtime QoS Languages
(AspectWerkz,PROSE)
Taxonomy of Research Continuum for Real-time QoS Provisioning
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Systemic(QoS) Aspects
Analyzer
AspectCompiler/Weaver
Constraints,Hints
Context (UAV OEP)
AdaptivemiddlewareMiddlewareservices
Application CodeWovenapplicationcode
ConnectivityAspects
FunctionalAspects
Deployment-time QoS Provisioning• Key Ideas:
– Use declarative approach to describe service contracts that capture adaptation rules & policies to specify when & how
– Use special compiler or aspect weaving tools to synthesize or weave code to provision specified adaptive behavior
– Generated application can be “reflective” to allow runtime adaptation
Specify QoS Service
Contracts
Woven adaptation
behavior code
Specialized Compiler or
Aspect weaver
Runtime PhaseDeployment-time Phase
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Related Research: Deployment-time QoS ProvisioningResearch Category
Related Research
Programming Language
• Specialization Class Volanschi et al. "Declarative Specialization of Object-oriented Programs," ACM OOPSLA 1997.
• Java Adaptation Class Boinot et al. “Declarative Approach for Designing & Developing Adaptive Components”, IEEE ASE 2000
DOC Middleware • QuO Zinky et al. “Architectural Support for Quality of Service for CORBA Objects”, Theory & Practice of Object Systems, April 1997
• ControlWare Zhang et al. “ControlWare: A Middleware Architecture for Feedback Control of Software Performance”, IEEE ICDCS 2002
• FCS/nORB Lu et al. “Feedback Control Real-Time Scheduling in ORB Middleware”, IEEE RTAS 2003
Component Middleware
• Qosket Schantz et al. "Packaging Quality of Service Control Behaviors for Reuse", IEEE ISORC 1999.
• Qosket/CIAO Wang et al. “Total Quality of Service Provisioning in Middleware & Applications”, Microprocessors & Microsystems, special issue on Middleware Solutions for QoS-enabled Multimedia Provisioning over the Internet March 2003.
Suitable for DRE systems where QoS adaptation rules are known a priori
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Deployment-time QoS Provisioning: What is Missing?• Unresolved Challenges
– Adaptation rules or utility control models still must be known a priori– It’s hard to handle dynamically changing operating conditions since new
application behavior & adaptations are needed after systems are deployed
Emergency response required!
Solution: Runtime QoS Provisioning
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QoS Provisioning TimeDevelopment Time Deployment Time
Abs
trac
tion
Leve
lD
OC
Mid
dlew
are
Prog
ram
min
gLa
ngua
ges
Development-time QoS DOC
Middleware(RT-CORBA, RTSJ)
Deployment-time QoS DOC
Middleware(QuO,
dynamicTAO)
Development-time Aspect-Oriented
Languages(AspectJ,
AspectC++)
Com
pone
ntM
iddl
ewar
e
Runtime
Development-time QoS Component
Middleware(CIAO, Real-time EJB)
Deployment-timeQoS Component
Middleware(Qosket)
Runtime QoS Component
Middleware(SOFA, BARK,DynamicCIAO)
Deployment-time QoS Languages
(Java Adaptation Class, Specialization Class)
Runtime QoS DOC
Middleware(ACT Generic
CORBA Proxy)
Runtime QoS Languages
(AspectWerkz,PROSE)
Taxonomy of Research Continuum for Real-time QoS Provisioning
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• Key Ideas– Decouple system adaptation policy from system application code & allow
them to be changed independently from each other– Decouple system deployment framework & middleware from core system
infrastructure to allow enterprise DRE systems dynamically reconfigurable
Runtime QoS Provisioning
System ObserversSystem ObserversSystem Condition Observers
System Deployment AgentsSystem D&C Actors
& Middleware
Adaptation Planner
My Research Focus Area
ControlAlgorithmControlAlgorithm
AdaptationPlan
SystemConditions
Running Systems
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Related Research: Runtime QoS ProvisioningResearch Category
Related Research
Programming Languages
• PROSE Popovici et al. “Efficient Dynamic Weaving for Java”, ACM AOSD 2003
DOC Middleware
• Adaptive CORBA Template Sadjadi et al. “Transparent Self-optimization in Existing CORBA Applications”, IEEE ICAC 2004
• XRMI Chen et al. “Extending RMI to Support Dynamic Reconfiguration of Distributed Systems”, IEEE ICDCS 2002
Component Middleware
• SOFA Frantisek et al. “SOFA/DCUP: Architecture for Component Trading & Dynamic Updating.”, IEEE ICCDS 1998
• BARK Rutherford et al. “Reconfiguration in the Enterprise Component Model”, ACM/IFIP CD 2002
• Adaptive .NET Rasche et al. “Configuration & Dynamic Reconfiguration of Component-based Applications with Microsoft .NET”, IEEE ISORC 2003
• OpenCOM Coulson et al. “A Component-based Middleware Framework for Configurable & Reconfigurable Grid Computing”, Concurrency & Computation: Practice & Experience, 2006
• JAsCo (Dynamic AOP) Vanderperren et al. “Adaptive Programming in JAsCo”, AOSD 2005
Runtime QoS provisioning is essential for enterprise DRE systems
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Runtime QoS Provisioning: What is Missing?• Unresolved Challenges
1. How to dynamically reconfigure enterprise DRE systems & real-time policies from the perspective of different end-users?
2. How to make reconfiguration process more predictable & time-bounded?3. How to simplify the planning of reconfiguration process workflow?
Provisioning Dynamic QoS Adaptation for Enterprise DRE Systems
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Presentation Road Map
• Research Motivation • Taxonomy of Related Research • Research Challenges & Proposed Solutions• Evaluation of Success• Dissertation Timeline
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Research Goals• Computational model –
Develop dynamic reconfiguration techniques for enterprise DRE systems
• Execution platform – Map the dynamic reconfiguration techniques to a more predictable & time-bounded execution platform
• Programming model – Develop a domain-specific modeling language to simplify dynamic reconfiguration workflow
Constraint is to support standards-based component & D&C model
Operational string
App App App App
App
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Limitations with OMG D&C Model
• The existing D&C model cannot change the configuration once an application is deployed
–Must shutdown the entire application & redeploy, which is not feasible for enterprise DRE systems
Applicationsalways static
No QoS Assurance
HypothesisExisting CCM components
& applications could be enhanced with dynamic
capabilities w/out breakingstandard component
programming model & D&C model
Baseline: DAnCE Deployment And Configuration Engine (D&C Spec)
SW Deployer Target EnvironmentD&C Tools
D & CProfile
RunningApplications
• The existing D&C model cannot ensure real-time QoS when performing initial D&C & reconfiguration
–Enterprise DRE systems have stringent QoS requirements for dynamic redeployment & reconfiguration
G. Deng et al, “DAnCE: A QoS-enabled Component Deployment & Configuration Engine”, ACM/IFIP Component Deployment’ 05, Grenoble, France, November 28-29, 2005.
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• Context– Enterprise DRE systems
may have thousands of components distributed across hundreds of nodes
– Components are often grouped together by system architect in the form of operational strings
• Problem– How to make enterprise
DRE systems manageable from end-users (e.g., software architect, software engineer) perspective?
Challenge 1: Reconfigure Enterprise DRE Systems from End-User Perspective
Time-critical end-to-end path through operational string
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• Develop a ReDaC computational model:– Introduce operational strings as first-class entities– Provide a rich set of services to manage enterprise DRE systems &
system resources at different levels of granularity• Components
Solution ReDaC Computational Model
MLRMInfrastructure
Resource Pool
Resources
MLRMInfrastructure
Resource Pool
Resources
Add_Instance <Plan ID> <Node ID> <Component Type> <Policy Set>Remove_Instance <Plan ID> <Component ID>Bind <Plan ID> <Source Component ID : Port Name> <Dest Component ID : Port Name>Remove_Binding <Plan ID> <Source Component ID : Port Name> <Dest Component ID : Port Name>
App App
Real-time policy set
Provisioning Dynamic QoS Adaptation for Enterprise DRE Systems
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MLRMInfrastructure
Resource Pool
Resources
MLRMInfrastructure
Resource Pool
Resources
• Develop a ReDaC computational model:– Introduce operational strings as first-class entities– Provide a rich set of services to manage enterprise DRE systems &
system resources at different levels of granularity• Components• Operational strings
Solution ReDaC Computational Model
App AppOperational string
App App App App
Add_OpString <Plan ID> <OpString Descriptor>Remove_OpString <Plan ID> <OpString ID>Update_OpString <Plan ID> <OpString Descriptor>Bind_OpString_OpString <Source OpString ID : Port ID> <Dest OpString ID : Port ID>
Mission
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MLRMInfrastructure
Resource Pool
Resources
MLRMInfrastructure
Resource Pool
Resources
• Develop a ReDaC computational model :– Introduce operational strings as first-class entities– Provide a rich set of services to manage enterprise DRE systems &
system resources at different levels of granularity• Components• Operational strings• Entire system deployment plan
Solution ReDaC Computational Model
App AppOperational string
App App App App
Mission
Deploy_Plan < Deployment Plan Descriptor>Teardown_Plan < Deployment Plan ID>Update_Plan < Plan ID> <Deployment Plan Descriptor>
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MLRMInfrastructure
Resource Pool
Resources
MLRMInfrastructure
Resource Pool
Resources
• Develop a ReDaC computational model :– Introduce operational strings as first-class entities– Provide a rich set of services to manage enterprise DRE systems &
system resources at different levels of granularity• Components• Operational strings• Entire system assemblies• Interactions among these entities through well-defined interfaces• Components shared by independently designed assemblies
Solution ReDaC Computational Model
App AppOperational string
App App App App
C1
Bind_OpString_Component <Plan ID> <OpString ID : Port ID> <Dest Component ID : Port ID>
Mission
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Current Status of ReDaCDesigned & implemented the baseline & integrated it with DAnCEEnhanced DAnCE to support configuring CCM components with RT policiesDesigned & implemented the initial ReDaC computational model• Adding a component is on a per-instance basis
– Specify component configuration & binding information – Specify what QoS policy the component should be configured– Container placement is transparent to clients for optimization
• Removing a component is on a per-instance basis– Reconfigurations are transparent to peer components
• Components could be shared across assemblies
Instance & binding configuration & QoS
Instance information
G. Deng et al, “Modularizing Variability & Scalability Concerns in DRE Systems with Modeling Tools & Component Middleware: A Case Study”, IEEE ISORC '06, Korea
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Criteria for EvaluationPerformance Criteria For Reconfiguration (Hypothesis)• Baseline – Existing DARPA ARMS GT4 Testbed, 3 operational strings,
with mission effectiveness values (MEV) of 3, 2, 2• Metric M1 – Average mission effective value loss of all operational strings
Goal: Reduce M1 value by 20-30%
1. Divide the total down time of each operational string by the total experiment operational time.
2. Multiply it by the MEV of that operational string to produce the average MEV loss for that string.
3. Sum up all average MEV loss.
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Research Contributions & Related Publications• Identified key D&C complexities for enterprise DRE systems• Propose to enhance standards-based component middleware to provision dynamic
capability for enterprise DRE systems to ensure QoS• Propose a ReDaC computational model to simplify the enterprise DRE system
Embedded Systems with Modeling Tools & Component Middleware: A Case Study”, IEEE ISORC '06, April 24-26, 2006, Gyeongju, Korea.
2. “DAnCE: A QoS-enabled Component Deployment & Configuration Engine”, ACM/IFIP CD’ 05, Grenoble, France, November 28-29, 2005.
3. “Addressing Domain Evolution Challenges for Software Product-line Architectures (PLAs)”, ACM/IEEE MoDELS 2005 workshop October 2, 2005, Jamaica.
4. “Concern-based Composition & Reuse of Distributed Systems”, ACM/IEEE ICSR8, Madrid, Spain, July 2004.
1st Author 3rd Author
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• Context– Multiple reconfiguration service requests might arrive simultaneously
• Problems– How to differentiate services from different requests based on their
priorities?
Challenge 2: Ensure Reconfiguration QoS
Non-Critical Critical
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Proposed Solution ReDaC Execution Platform• ReDaC Execution Platform for
Service Execution – A novel approach to integrate Real-
time CORBA (RT CORBA) features to build a predictable platform model
– All standards-based deployment agents (e.g., ExecutionManager, NodeApplicationManager, & NodeManager) are configured with appropriate RT CORBA policies & run atop RT CORBA middleware
− When external clients request setting up & modifying services, the priority level could also be specified as part of the request
Deployer
Deployer
Deployer
Deployer
Deploy_OpString <OpString Descriptor>
[Service Priority]
Execution Manager
DomainApplication
Manager
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• RT-CORBA Features Leveraged by ReDaC Execution Platform– Priority Model
• Use “ClientPropagated” policy to propagate client request priorities
– ThreadPool Model• Use “ThreadPoolWithLanes”
policy serve client requests concurrently
• The “lane” feature offers priority partitioning to avoid priority inversion
– Connection Model• Use PriorityBanded policy to
differentiate service request based on priority
Proposed Solution ReDaC Execution Platform
Thread Pool with Lanes
PRIORITY35
PRIORITY50
PRIORITY10
C L I E N TO R B C O R E
P 1 -5 P 1 0 -2 0 P 21 -1 0 0
S E R V E RO R B C O R E
_bind_ priority_ban d()
P 1- 5 P 1 0 -2 0 P 2 1-1 0 0
_bind_ priority_ban d()
Solaris Priority136
LynxOS Priority128
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Current Status of ReDaC Execution Platform• Initial Design Phase
Deploy_OpString <OpString Descriptor>
[Service Priority] Execution Manager
DomainApplication
Manager
Node ManagerNode
ApplicationManager
Node ManagerNode
ApplicationManager
NodeApplication
NodeApplication
Thread Pool with Lanes
PRIORITY35
PRIORITY50
PRIORITY10
Thread Pool with Lanes
PRIORITY35
PRIORITY50
PRIORITY10
Thread Pool with Lanes
PRIORITY35
PRIORITY50
PRIORITY10
Deployer
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Criteria for EvaluationPerformance Criteria For Reconfiguration (Hypothesis)• Baseline – Current DARPA ARMS GT4 Testbed (6 operational strings, with
priority values of 3, 2, 2, 2, 1, 1, & system workloads are high)• Metric M2 – Highest priority operational strings mission effective loss
Goal: Reduce M2 value by 20-30%
1. Determine when the first operational string with the highest MEV goes down and obtain that timestamp.
2. Subtract that from the timestamp when all operational strings with the highest MEV were first up.
3. Repeat if another operational string with the highest MEV goes down.
4. Sum up all highest MEV operational down time.5. Divide it by the total experimental measurement time
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Research Contributions & Related Publications
• Identified key complexities in provisioning predictable dynamic reconfiguration service for enterprise DRE systems
• Proposed a novel solution to address the above challenges by leveraging real-time CORBA features
1. “Supporting Configuration & Deployment of Component-based DRE Systems Using Frameworks & Aspects”, Poster paper of OOPSLA 2005, San Diego, CA, October 2005.
2. “Resolving Component Deployment & Configuration Challenges for DRE Systems via Frameworks & Generative Techniques”, Doctoral Symposium ACM ICSE 2006, Shanghai , China, May 20-28, 2006
1st Author 2nd Author
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• Context: – Enterprise DRE system reconfiguration process is usually
composed of a number of subtasks
Operational string
App App App App
App
App
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Ad Hoc Techniques for Planning the Reconfiguration Process• Problem:
– How to allow the reconfiguration process to managed from end-users' perspective?
– How to specify the causal relationship among various subtasks?
Operational string
App App App App
App
App
1
2
3
4
5
5’
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Solution Reconfiguration Modeling Language (ReML)• Develop a ReDaC Programming Model called ReML:
– Allow end-users to declaratively specify the workflow of the reconfiguration process through ReML, a visual DSML
– Combine the ReML with ReDaC to provision full-fledged enterprise DRE system reconfiguration capability
AddOpString (XYZ)
BindOpString (X to Y)
RemoveInstance (W)
UpdatePlan (P1, P.cdp)
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Current Status of ReML• Initial Design Phase of ReML• Significant experiences gained before in developing DSMLs:
– Developed an Event QoS Aspect Modeling Language (EQAL), used in DARPA PCES program
• “Model-driven Configuration & Deployment of Component Middleware Publisher/Subscriber Services”, ACM GPCE ‘04, Vancouver, Canada, Oct 2004.
• “Model Driven Middleware: A New Paradigm for Deploying & Provisioning Distributed Real-time & Embedded Applications”, Elsevier Journal of Science of Computer Programming: Special Issue on Model Driven Architecture, 2006 (to appear).
• “Model-Driven Integration of Federated Event Services in Real-Time Component Middleware”, ACMSE ‘04, Huntsville, AL, Apr, 2004.
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Criteria for Evaluation• The Integration of ReDaC Programming Model & Computational Model
could reduce enterprise DRE systems reconfiguration effort (Hypothesis)– Metrics Lines of Code (LOCs) reduced– M3 = LOC Saved Per Instance (Estimated ~200 LOC)– M4 = LOC Saved Per Connection (Estimated ~200 LOC)– M5 = LOC Saved Per Operational String (Estimated ~5,000 LOC for a
size of 10 components & 15 connections)
Goal: Maximize the LOC SavingsOperational string
App App App App
App
App
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Research Contributions & Related Publications• Identified key complexities in provisioning reconfiguration workflow for
enterprise DRE systems
• Proposed a model-based solution to address the above challenge by leveraging model driven development (MDD) & domain-specific modeling language (DSML) technologies
1. “Model-driven Configuration & Deployment of Component Middleware Publisher/Subscriber Services”, GPCE ‘04, Vancouver, Canada, October 2004
2. “Model-Driven Integration of Federated Event Services in Real-Time Component Middleware”, ACMSE ‘04, Huntsville, AL, April 2-3, 2004
1st Author 2nd Author
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Summary of Research ContributionsTopic Benefits
Reconfiguration ComputationalModel
Identified key complexities in enterprise DRE system reconfiguration process, & proposed a novel computational model to provide a rich set of services to allow end-users to manage enterprise DRE systems & system resources at different levels of granularity
ReconfigurationExecution Platform
Identified key performance bottlenecks in enterprise DRE system reconfiguration process, & proposed a novel execution platform model to address reconfiguration predictability & scalability challenges
Reconfiguration Programming Model
Proposed a DSML-based solution to address key complexities in design and plan the reconfiguration workflow
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Presentation Road Map
• Research Motivation • Taxonomy of Related Research • Research Challenges & Proposed Solutions• Evaluation of Success• Dissertation Timeline
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Dissertation Timeline
ReDaC FrameworkDesign &
Implementation
May 2003 Dec 2005 Dec 2006
ReDaC RT EnhancementsReML Design
Methodology Validation & Architectural Design,
DAnCE Baseline
Initial ReDaCFramework
Implementation
ICSR 2004OOPSLA 2003 Workshop
OOPSLA 2004 PosterGPCE 2004
ACMSE 2004
CD 2005OOPSLA 2005 Poster
MoDELS 2005 WorkshopJournal of Sci of Comp
Programming 2006
ISORC 2006ICSE 2006 Doctoral Sym
ReDaC & ReML Implementation& Experiments
Dec 2004 March 2007
DAnCEArchitectural Design &Initial Implementation
I’m here
March 3, 2006
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Summary of Publications1. Modularizing Variability & Scalability
Concerns in Distributed Real-time & Embedded Systems with Modeling Tools & Component Middleware: A Case Study”, IEEE ISORC '06, Apr, 2006, Gyeongju, Korea.
2. DAnCE: A QoS-enabled Component Deployment & Conguration Engine, ACM/IFIP CD’ 05, Grenoble, France, Nov, 2005.
6. “Model Driven Development of Inventory Tracking System”, ACM OOPSLA 2003 Workshop on DSML, Anaheim, CA, Oct 2003.
First Author 2nd Author Others
7. Supporting Configuration & Deployment of Component-based DRE Systems Using Frameworks, Models, & Aspects, Poster paper of ACM OOPSLA 2005, San Diego, CA, Oct 2005.
8. Model-driven Configuration & Deployment of Component Middleware Publisher/Subscriber Services, ACM GPCE ‘04, Vancouver, Canada, Oct 2004.
9. Model Driven Middleware: A New Paradigm for Deploying & Provisioning Distributed Real-time & Embedded Applications, Elsevier Journal of Science of Computer Programming: Special Issue on Model Driven Architecture, 2006 (to appear).
10. Model-Driven Integration of Federated Event Services in Real-Time Component Middleware, ACMSE ‘04, Huntsville, AL, Apr, 2004.
11. Resolving Component Deployment & Configuration Challenges for DRE Systems via Frameworks & Generative Techniques, Doctoral Symposium ACM ICSE 2006, Shanghai , China, May 2006
Provisioning Dynamic QoS Adaptation for Enterprise DRE Systems