Automated Middleware QoS Configuration Techniques using Model Transformations

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Automated Middleware QoS Configuration Techniques
using Model Transformations
Amogh Kavimandan Aniruddha Gokhale amoghk,gokh
ale_at_dre.vanderbilt.edu www.dre.vanderbilt.edu
Research supported by Lockheed Martin STI Project
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Distributed Real-time Embedded (DRE) Systems

• Network-centric and large-scale systems of
  systems
• e.g., industrial automation, emergency response
• Different communication semantics
• e.g., pub-sub
• Satisfying tradeoffs between multiple (often
  conflicting) QoS demands
• e.g., secure, real-time, reliable, etc.
• Regulating adapting to (dis)continuous changes
  in runtime environments
• e.g., online prognostics, dependable upgrades,
  keep mission critical tasks operational, dynamic
  resource mgmt

DRE systems increasingly adopting component-based
architectures
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Challenges in Realizing DRE Systems

• Variability in the problem space (domain expert
  role)
• Functional diversity
• Composition, deployment and configuration
  diversity
• QoS requirements diversity

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Technology Enablers for DRE systems Component
Middleware
Write Code That Reuses Code

• Components encapsulate application business
  logic
• Components interact via ports
• Provided interfaces, e.g., facets
• Required connection points, e.g., receptacles
• Event sinks sources
• Attributes
• Containers provide execution environment for
  components with common operating requirements
• Components/containers can also
• Communicate via a middleware bus and
• Reuse common middleware services

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Challenges in Component-based DRE Systems
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Our Solution CoSMIC MDE Tool Chain

• CoSMIC tools e.g., PICML used to model
  application components, CQML for QoS
• Captures the data model of the OMG DC
  specification
• Synthesis of static deployment plans for DRE
  applications
• Capabilities being added for QoS provisioning
  (real-time, fault tolerance, security)

CoSMIC can be downloaded at www.dre.vanderbilt.edu
/cosmic
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Middleware Configuration (1/2)

• Benefits of QoS-enabled middleware technologies
• Raise the level of abstraction
• Support many quality of service (QoS)
  configuration knobs

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Component
Reference
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COMPONENT SERVER 1
COMPONENT SERVER 2
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Middleware Configuration (2/2)

• Benefits of QoS-enabled middleware technologies
• Raise the level of abstraction
• Support many quality of service (QoS)
  configuration knobs

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COMPONENT SERVER

• Drawbacks of QoS-enabled middleware technologies
• Achieving desired QoS increasingly a system QoS
  configuration problem, not just an initial system
  functional design problem

Lack of effective QoS configuration tools result
in QoS policy mis-configurartions that are hard
to analyze debug
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Solution Approach Model Driven Engineering (MDE)

• Develop, validate, standardize generative
  software technologies that
• Model
• Analyze
• Synthesize
• Provision
• multiple layers of middleware application
  components that require simultaneous control of
  multiple quality of service properties end-to-end
• Specialization is essential for
  inter-/intra-layer optimization advanced
  product-line architectures

ltCONFIGURATION_PASSgt ltHOMEgt ltgt
ltCOMPONENTgt ltIDgt ltgtlt/IDgt
ltEVENT_SUPPLIERgt ltevents this
component suppliesgt lt/EVENT_SUPPLIERgt
lt/COMPONENTgt lt/HOMEgt lt/CONFIGURATION_PAS
Sgt
Goal is not to replace programmers per se it is
to provide higher-level domain-specific languages
for middleware/application developers users
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Motivating Application NASA MMS Mission

• NASAs Magnetospheric MultiScale (MMS) space
  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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Motivating Application NASA MMS Mission

• MMS mission QoS requirements span two dimensions
• Multiple modes of operation
• Varying importance of data collection activity of
  satellite sensors based on mission phase

Slow Survey Fast Survey Burst
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Motivating Application NASA MMS Mission

• MMS 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

Slow Survey Fast Survey Burst
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Component-based MMS Mission Prototype
Spacecraft 1

• MMS application components are bundled together
  into hierarchical assemblies
• Assembly package metadata conveys component
  interconnections implementation alternatives

Bus Processor (VxWorks Node)
Payload Processor (Linux Node)
Sensor Suite (Linux node)

Science Agent
Gizmo Agent
Algorithm
Comm Agent
Gizmo Agent
Sensor 1
Gizmo Agent
Algorithm
GNC Agent
Gizmo Agent
Sensor 2
Exec Agent
Exec Agent
Sensor 3
Comm Agent
Sensor 4
Ethernet (802.3)
Ethernet (802.3)

• Several different assemblies tailored to deliver
  different end-to-end QoS behaviors and/or
  algorithms can be part of the package
• e.g., full-scale MMS has 100s of components
  10s of assemblies
• Packages describing the components assemblies
  can be scripted via XML descriptors generated by
  automated model-driven tools

MMS prototype developed using Component-Integrated
ACE ORB (CIAO)
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Challenge 1 Translating QoS Policies to QoS
Options
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, particularly for large
  systems

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Challenge 1 Translating QoS Policies to QoS
Options

• 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)

Request Buffering
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Challenge 2 Choosing Appropriate QoS Option
Values

• Individually configuring component QoS options is
  tedious error-prone
• e.g., 10 distinct QoS options per component
  140 total QoS options for entire NASA MMS
  mission prototype
• Manually choosing valid values for QoS options
  does not scale as size complexity of
  applications increase

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Challenge 3 Validating QoS Options

• 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

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Challenge 4 Resolving QoS Option Dependencies
ThreadPool priorities of Comm should match
priority bands defined at Gizmo

• 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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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

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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

• 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

CQML Model Interpreter
Bogor Input Representation

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

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QUICKER Concepts Transformation of QoS
policies(1/2)

• Platform-Independent Modeling Language (PICML)
  represents application QoS policies
• PICML captures policies in a platform-independent
  manner
• Representation at multiple levels
• e.g., component- or assembly-level

RequirementProxy can be per component or assembly
instance
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QUICKER Concepts Transformation of QoS
policies(1/2)

• Platform-Independent Modeling Language (PICML)
  represents application QoS policies
• PICML captures policies in a platform-independent
  manner
• Representation at multiple levels
• e.g., component- or assembly-level
• Component QoS Modeling Language (CQML) represents
  QoS options
• CQML captures QoS configuration options in a
  platform-specific manner

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QUICKER Concepts Transformations of QoS
policies(2/2)

• Translation of application QoS policies into
  middleware QoS options
• Semantic translation rules specified in terms of
  input (PICML) output (CQML) type graph
• e.g., rules that translate multiple application
  service requests service level policies to
  corresponding middle-ware QoS options
• QUICKER transformation engine maps QoS policies
  (in PICML) to QoS configuration options (in CQML)

Level 1
Level 2
Provider
Service Request
Level 3
Provider Service Levels
Multiple Service Requests
Service Levels
Priority Model Policy
Lanes
Thread Pool
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QUICKER Concepts Validation of QoS Options (1/2)

• Representation of middleware QoS options in Bogor
  model-checker
• BIR extensions allow representing domain-level
  concepts in a system model
• QUICKER defines new BIR extensions for QoS
  options
• Allows representing QoS options domain entities
  directly in a Bogor input model
• e.g., CCM components, Real-time CORBA lanes/bands
  are first-class Bogor data types
• Reduces size of system model by avoiding multiple
  low-level variables to represent domain concepts
  QoS options

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QUICKER Concepts Validation of QoS Options (2/2)

• Representation of properties (that a system
  should satisfy) in Bogor
• BIR primitives define language constructs to
  access manipulate domain-level data types,
  e.g.
• Used to define rules that validate QoS options
  check if property is satisfied
• Automatic generation of BIR of DRE system from
  CQML models

Rule determines if ThreadPool priorities at Comm
match priority bands at Analysis
Model interpreters auto-generate Bogor Input
Representation of a system from its CQML model
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Resolving Challenge 1 Translating Policies to
Options (1/2)

• 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
• Reduces modeling effort
• e.g., 25 QoS policy elements for MMS mission vs.
  140 QoS options

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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

QUICKER
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Resolving Challenge 4 Resolving QoS Option
Dependencies

• 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 MMS Mission Components

• 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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Academic Related Work

• Functional Specification Analysis Tools
• Hatcliff, J. et. al. (2003). Cadena
• QoS Adaptation Modeling Tools
• Ye, J. et. al. (2004). DQME
• Zinky, J., (1997). QuO
• QoS Specification Tools
• Ritter, T. et. al. (2003). CCM QoS MetaModel
• Ahluwalia, J. et. al. (2005). Model-based
  Run-time
• Monitoring
• Frolund, S. et. al. (1998). QML
• Schedulability Analysis Tools
• Madl, G. et. al. (2004). Automatic
  Component-based system verification
• Kodase, S. et. al. (2003). AIRES

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Concluding Remarks

• 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

QUICKER MDE tools CIAO QoS-enabled component
middleware available as open-source at
www.dre.vanderbilt.edu/CoSMIC
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