Model-Driven Engineering of Component-based Distributed Real-time & Embedded Systems

Model-Driven Engineering of Component-based Distributed

Real-time & Embedded Systems

Vanderbilt University Nashville, Tennessee

Institute for Software Integrated Systems

STI Project 2007 Status Report

Krishnakumar Balasubramanian, Amogh

Kavimandan{kitty, amoghk}@dre.vanderbilt.edu

STI Project 2007 Status Report• Vanderbilt’s contributions

• Model-Driven Engineering tool-chain

• Current year’s focus on two areas

• Model-driven Application Specific Optimizations (Kitty)

• Model-driven QoS Mapping (Amogh)

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PICMLmodel

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OS KERNEL

OS I/O Subsystem

Network Interfaces

MIDDLEWARE

Physical Assembly Mapper

Deployment Plan

Configuration Files

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Required Interface

Provided Interface

Event Sink

Event SourceComponent Home

Component Assembly

Component Invocation

Creates

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Distributed Real-time & Embedded (DRE) Systems• Stringent Quality-of-Service (QoS)

demands, e.g., real-time constraints• Operate under limited resources

• e.g., avionics mission computing• Enterprise DRE Systems

• Simultaneous execution of multiple applications with varying importance

• Highly heterogeneous platform, language & tool environments

• e.g., Total Shipboard Computing Environment (TSCE)

• Use COTS middleware technologies• CORBA, RT-Java

• Use COTS Component/Service technologies

• CORBA Component Model (CCM), EJB, Web Services

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Motivation for Application Specific Optimizations• Middleware tries to optimize

execution for every application

• Collocated method invocations

• Optimize the (de-)marshaling costs by exploiting locality

• Specialization of request path by exploiting protocol properties

• Caching, Compression, various encoding schemes

• Reducing communication costs

• Moving data closer to the consumers by replication

• Reflection-based approaches

• Choosing appropriate alternate implementations at run-time

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Related ResearchCategory Related Research

Component Middleware

Wang, N. et al, Applying Reflective Techniques to Optimize a QoS-enabled CORBA Component Model Implementation, 24th Annual International Computer Software & Applications Conference Taipai, Taiwan, October 2000.

Teiniker, E. et al, Local Components & Reuse of Legacy Code in the CORBA Component Model, EUROMICRO 2002, Dortmund, Germany (2002)

Diaconescu, A. & Murphy, J., Automating the Performance Management of Component-based Enterprise Systems through the Use of Redundancy, Proceedings of the 20th IEEE/ACM international Conference on Automated Software Engineering (Long Beach, CA, USA, November 07 - 11, 2005).

Gurdip Singh & Sanghamitra Das, Customizing Event Ordering Middleware for Component-Based Systems, pp. 359-362, Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC'05), 2005.

Web Services Gao, L et al, 2003, Application specific Data Replication for Edge Services, In Proceedings of the 12th International Conference on World Wide Web (Budapest, Hungary, May 20 - 24, 2003). WWW '03. ACM Press, New York, NY, 449-460.

Mukhi, N. K., 2004, Cooperative Middleware Specialization for Service Oriented Architectures, Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters (New York, NY, USA, May 19 - 21, 2004).

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Related Research: What’s missing?• Lack of a high-level notation to

guide optimization frameworks

• Missing AST of application

N N N N

N

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Application Abstract Syntax Tree

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Related Research: What’s missing?• Lack of a high-level notation to

guide optimization frameworks

• Missing AST of application

• Detection at run-time (reflection)

• Additional overhead in the fast path

• Not suitable for DRE systems

• Intrusive optimizations, i.e., not completely application transparent

• Requires providing multiple implementations, e.g., EJB

• Optimization performed either

• Too early, or too late

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Node Application

Container

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Receptacle

Facet

Event Sink


Component Assembly

Component Remote Invocation

Collocated Invocation

Creates

1. Overhead of platform mappings

• Blind adherence to platform semantics

• Inefficient middleware glue code generation per component

• Example:

• Creation of a factory object per component

• Servant glue-code generation for every component

Need optimization techniques to build large-scale component systems!

Application Specific Optimizations: Unresolved Challenges

Solution Approach: Physical Assembly Mapper (PAM)

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PICMLmodel

CH CH

CHCH

OS KERNEL

OS I/O Subsystem

Network Interfaces

MIDDLEWARE

Physical Assembly Mapper

Deployment Plan

Configuration Files

CH

Required Interface

Provided Interface

Event Sink


Component Assembly

Component Invocation

Creates

CH

CH

CH

CH

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CH

CH

CH CH

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• Physical Assembly Mapper

• Uses the application model as the input

• Exploits knowledge about platform semantics to rewrite the input model to a functionally equivalent output model

• Generates middleware glue-code

• Generates deployment configuration files

• Operates just before deployment

• Can be viewed as a “deployment compiler”

PAM Inputs• Set of 3 input graphs

• G1: (V1, E1),

V1 = { Application Components}

E1 = { Connections between application components}

• G2: (V2, E2),

V2 = V1 U {QoS configuration options on components}

E2 = { Connections between the components and the QoS options}

• G3: (V3, E3),

V3 = V1 U {nodes in the target deployment domain}

E3 = {Connections between the components and the nodes}

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PAM Output

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• Produces an output graph

• Go: (Vo, Eo),

• Vo = {Physical assemblies created on a single node}

• Eo = {Connections between the composites}

• Creation of physical assemblies subject to a number of constraints

• Average case

• |Vo| < |V1|

• Worst case

• |Vo| = |V1|, i.e., the optimizer couldn’t create any physical assemblies

• Equivalent to deployment of original application

Physical Assembly

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• Physical Assembly

• Created from the set of components that are deployed within a single process of a target node

• Subject to various constraints

• Example constraints include:

• No two ports of the set of components should have the same name

• No two components should have incompatible RT-CORBA policies

• No changes required to individual component implementations

Physical Assembly Generation

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• Given set of components deployed on a single process of a target node

• Compute pair-wise intersections of component port name sets

• If intersection is null, merge the two components into a physical assembly

• Additional RT-CCM constraint

• If intersection is null & RT-CORBA policies of the components are compatible

• e.g., PriorityPropagation Models, PriorityBands

• Physical Assembly indistinguishable to external clients

• All valid operations on individual components are still valid

Middleware Concepts Exploited by Physical Assemblies

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• Opacity of object references

• Components don’t rely on specific details of object references, e.g., location of type information

• Allows replacing references transparent to component implementations


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• Presence of a component context

• Components access ports of other components using a context object

• Allows replacing context transparent to component implementations

Container

Servant

ComponentSpecificContext

CCMContext

MainComponent

Executor

ExecutorsExecutorsExecutors

POA

EnterpriseComponent

CCMContext

Container

Servant

ComponentSpecificContext

CCMContext

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• Presence of a component context

• Components access ports of other components using a context object

• Allows replacing context transparent to component implementations

• Clean separation between glue-code & component implementation

• Allows modifications transparent to component implementations

Technique can be applied to other middleware with these properties

Stub

Executors

Skel

IDL Compiler

IDL

CIDL

CIDL Compiler

Executor IDL

Servants

ExecutorStubs

IDL Compiler

XMLComponentDescriptors

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Physical Assembly Evaluation Criteria • Footprint of physical assembly

components

• Compared to vanilla Component-Integrated ACE ORB (CIAO)

• Different scenarios

• Simple scenarios (<= 10 components)

• ARMS GateTest scenarios (150+ components)

• Reduce static & dynamic footprint

• Reduce no. of homes by (n – 1) / n

• Reduce no. of objects registered with POA by (n – 1) / n

• Reduce no. of context objects created by (n – 1) / n

n = no. of components deployed on a single process of a target node

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Assembly Optimizer

Factory

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Evaluating Physical Assemblies

• Boeing’s BoldStroke Basic Single Processor Scenario• Consists of 4 components

• Timer – Periodically sends refresh signal to the GPS• GPS – Calculates new co-ordinates of the aircraft in response

to Timer signal• Airframe – Processes new location inputs from GPS• Display – Updates the new location of the aircraft in the

navigation display18

Applying Physical Assemblies to BasicSP Scenario

• Assumption: GPS, Airframe and Display components are deployed on a single processor board

• Applying physical assemblies • Combines GPS, Airframe and Display components into a

single physical assembly (BasicSPAsm)• Maintains the same number of connections• Timer component not combined (due to clash in port names)

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Experimental Results: BasicSP Scenario• Testbed

• Linux 2.6.20 FC6

• Dual 2.4Mhz processor

• 1.5GB RAM

• Evaluation for larger applications (~150 components) is in progress

20Physical assembly mapping reduces the footprint significantly

Static footprint reduction of ~45%

Dynamic footprint reduction of ~10%

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Application Specific Optimizations: Unresolved Challenges2.Lack of application context

• Optimization decisions relegated to run-time

• e.g., every invocation performs check for locality

• Settle for near-optimal solutions

• Missed middleware optimization opportunities

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Application Specific Optimizations: Unresolved Challenges2.Lack of application context

• Optimization decisions relegated to run-time

• e.g., every invocation performs check for locality

• Settle for near-optimal solutions

• Missed middleware optimization opportunities

• e.g., allocation of RT-CORBA thread pools and lanes

• Large overhead compared to collocated calls

Cannot be solved efficiently at middleware level alone!

Invocation within the same pool/lane (12µs)

Invocation to a different pool/lane (150µs)

Solution Approach: Use Application Context from Models• Use application structure &

context available in models• Create fast path within

middleware for physical assemblies

• Cross-pool/lane proxy

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• Cross-pool/lane proxy• Utilize available fast path in PAM

• e.g., Use matching real-time policies as additional constraint when creating physical assemblies

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• Cross-pool/lane proxy• Utilize available fast path in PAM

• e.g., Use matching real-time policies as additional constraint when creating physical assemblies

• Configure middleware resources efficiently

• e.g., allocate physical assemblies with matching policies in the same thread pool or thread pool with lanes

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Context-driven Optimization Evaluation Criteria • Improve performance

• t = no. of cross-pool/lane interactions between components within an assembly

• Transform t remote calls to t cross-pool/lane calls

• Eliminate mis-optimizations• Check incompatible POA

policies• Incompatible invocation

semantics (oneway or twoway)

• No changes to individual component implementations• Eliminate need for a local

vs. remote version

• Customizable & application transparent

Experimental Results: Cross pool/lane proxy

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Standard deviation <= 3µs

Max latency improved by ~50%

99% latency improved by 60-66%

Average latency improved by 60 – 66%

Significant performance benefits with cross pool/lane proxy!

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Summary of Research ContributionsCategory Benefits

System Optimization Technologies

• Novel mechanism for mapping an assembly as a component to both reduce application footprint and increase performance

• Automatic discovery & realization of optimizations from models in an application transparent fashion

• Performs optimizations that are impossible to perform if operating at the middleware layer alone

• Optimized allocation & configuration of middleware resources using application context derived from models

QoS Mapping Context

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• Benefits of QoS-enabled middleware technologies

• Raise the level of abstraction

• Support many quality of service (QoS) configuration knobs

Container

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ORB

End-to-End PriorityPropagation

Portable PrioritiesProtocol Properties

Priority Band

Publisher(Subscriber) multi-threaded

access

Event Filtering

Event Dispatching

QoS Mapping Context

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Container

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Drawbacks of QoS-enabled middleware technologiesAchieving desired QoS increasingly a system QoS configuration problem, not just

an initial system functional design problem

• Benefits of QoS-enabled middleware technologies

• Raise the level of abstraction

• Support many quality of service (QoS) configuration knobs

Lack of effective QoS configuration tools result in QoS policy mis-configurartions that are hard to analyze & debug

ORB

Example Application: Satellite Mission• Satellite mission consists of four

identically instrumented spacecraft & a ground control system

• Collect mission data

• Send it to ground control at appropriate time instances

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Example Application: Satellite Mission• Satellite mission QoS requirements span

two dimensions

• Multiple modes of operation

• Varying importance of data collection activity of satellite sensors based on mission phase

• Need to translate QoS policies into QoS configuration options & resolve QoS dependencies

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Slow Survey

Fast Survey

Burst

Challenge 1: Translating QoS Policies to QoS Options

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Prioritized service invocations (QoS Policy) must be mapped to Real-time CORBA Banded Connection (QoS configuration)

• Large gap between application QoS policies & middleware QoS configuration options• Bridging this gap is necessary to realize the desired QoS policies

• The mapping between application-specific QoS policies & middleware-specific QoS configuration options is non-trivial, particular for large systems

Policy for handling of dangling(ill-behaved) publishers(subscribers) (QoS Policy) must be mapped to control policy and control period (QoS configuration)

Challenge 1: Translating QoS Policies to QoS Options

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• Conventional mapping approach requires deep understanding of the middleware configuration space

• e.g., multiple types/levels of QoS policies require configuring appropriate number of thread pools, threadpool lanes (server) & banded connections (client)

ProtocolProperties

Explicit Binding

Client Propagation & Server Declared Priority Models

Portable Priorities

Thread Pools

Static Scheduling Service

StandardSynchonizers

Request Buffering

Challenge 2: Choosing Appropriate QoS Option Values

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Individually configuring component QoS options is tedious & error-prone

e.g., ~10 distinct QoS options per component & ~140 total QoS options for entire satellite mission

Manually choosing valid values for QoS options does not scale as size & complexity of applications increase

Challenge 3: Validating QoS Options

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Each QoS option value chosen should be validated

e.g., Filter priority model is CLIENT_PROPAGATED, whereas Comm priority model is SERVER_DECLARED

Each system reconfiguration (at design time) should be validated

e.g., reconfiguration of bands of Analysis should be validated such that the modified value corresponds to (some) lane priority of the Comm

Challenge 4: Resolving QoS Option Dependencies

• “QoS option dependency” is defined as:

• Dependency between QoS options of different components

• Manually tracking dependencies is hard – or in some cases infeasible

• Dependent components may belong to more than one assembly

• Dependency may span beyond immediate neighbors

–e.g., dependency between Gizmo & Comm components

• Empirically validating configuration changes by hand is tedious, error-prone, & slows down development & QA process considerably

• Several iterations before desired QoS is achieved (if at all)

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ThreadPool priorities of Comm should match priority bands defined at Gizmo

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Solution Approach: Model-Driven QoS Mapping• QUality of service

pICKER (QUICKER)• Model-driven

engineering (MDE) tools model application QoS policies

• Provides automatic mapping of QoS policies to QoS configuration options

• Validates the generated QoS options

Automated QoS mapping & validation tools can be used iteratively throughout the development process

Enhanced Platform Independent Component Modeling Language (PICML), a DSML for modeling component-based applications

QoS mapping uses Graph Rewriting & Transformation (GReAT) model transformation tool

Customized Bogor model-checker used to define new types & primitives to validate QoS options

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QUICKER Enabling MDE Technologies

Enhanced Platform Independent Component Modeling Language (PICML), a DSML for modeling component-based applications

QoS mapping uses Graph Rewriting & Transformation (GReAT) model transformation tool

Customized Bogor model-checker used to define new types & primitives to validate QoS options

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QUICKER Enabling MDE Technologies

CQML Model interpreter generates Bogor Input Representation (BIR) of DRE system from its CQML model

CQML Model Interpreter

Bogor Input Representation

Resolving Challenge 1: Translating Policies to Options (1/2)

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Expressing QoS policies

PICML modes application-level QoS policies at high-level of abstraction

e.g., multiple service levels support for Comm component, service execution at varying priority for Analysis component, prioritizing event dispatching at Analysis component

Reduces modeling effort

e.g., ~25 QoS policy elements for satellite mission vs. ~140 QoS options

Resolving Challenge 1: Translating Policies to Options (2/2)• Mapping QoS policies to

QoS options

• GReAT model transformations automate the tedious & error-prone translation process

• Transformations generate QoS configuration options as CQML models

• Allow further transformation by other tools

• e.g., code optimizers & generators

• Simplifies application development & enhances traceability

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Resolving Challenges 2 & 3: Ensuring QoS Option Validity• CQML model

interpreter generates BIR specification from CQML models

• BIR primitives used to check whether a given set of QoS options satisfies a system property

• e.g., fixed priority service execution, a property of Comm component

• Supports iterative validation of QoS options during QoS configuration process

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QUICKER

Resolving Challenge 4: Resolving QoS Option Dependencies

• Change(s) in QoS options of dependent component(s) triggers detection of potential mismatches• e.g., dependency between Gizmo invocation priority & Comm lane priority

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Dependency structure maintained in Bogor used to track dependencies between QoS options of components, e.g.:

Analysis & Comm are connected

Gizmo & Comm are dependent

Detect mismatch if either values change

Dependency Structure of Satellite Mission Components

Summary of Research Contributions

• QUICKER provides Model-Driven Engineering (MDE) for QoS-enabled component middleware

• Maps application-level QoS policies to middleware-specific QoS configuration options

• Model transformations automatically generate QoS options

• Model-checking extensions ensure validity of QoS options at component- & application-level

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Documents

Model-Driven Engineering of Component-based Distributed Real-time & Embedded Systems