Physical Assembly Mapper:A Model-driven Optimization Tool for QoS-enabled Component Middleware

Vanderbilt University Nashville, Tennessee

Institute for Software Integrated Systems

RTAS 2008, April 22, 2008

Krishnakumar Balasubramanian, Douglas C. Schmidt

{kitty,schmidt}@dre.vanderbilt.edu

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

demands, e.g., real-time constraints• Simultaneous execution of multiple

applications with varying importance • Operate under limited resources

• e.g., avionics mission computing• Highly heterogeneous platform, language

& tool environments• e.g., Total Shipboard Computing

Environment (TSCE)• Use COTS middleware technologies

• CORBA, RT-Java• Use COTS Component/Service-oriented

technologies• CORBA Component Model (CCM),

EJB, Web Services

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Research Challenge : System Optimization (1/2)Context

• Component middleware allows designing systems that are

• Hierarchical, i.e., individual components easily combined to form assemblies

• Reusable, i.e., each component can be used in multiple composition contexts

• Consequences of hierarchy & reusability

• Systems with large number of components

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Research Challenge : System Optimization (2/2)Problem

• Systems with large number of components tend to exhibit

• Footprint overhead due to auxiliary middleware infrastructure in certain composition contexts

• e.g., component factories/ contexts when the components are collocated

• Latency overhead due to sub-optimal configuration of middleware

• e.g., latency between components placed in different containers

Node Application

Container

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CHCH

CHCH

CHCH

CHCH

CHCH

CHCH

CHCH

CH

Receptacle

Facet

Event Sink

Event SourceComponent Home

Component Assembly

Component Remote Invocation

Collocated Invocation

Creates

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Component Middleware Optimization Landscape• 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

Design-time approaches

Venkita Subramonian et. al., The design and performance of configurable component middleware for DRE systems. In Proceedings of RTSS 2004.Arvind Krishna et. al., Context-Specific Middleware Specialization Techniques for Optimizing Software Product-line Architectures. In Proceedings of EuroSys 2006Frank Hunleth et. al., Footprint and Feature Management Using Aspect-oriented Programming Techniques. In Proceedings of LCTES 02, pages 38–45. ACMÖmer Erdem Demir et. al., An aspect-oriented approach to bypassing middleware layers. In Proceedings of AOSD 2007, pages 25–35, ACM.Charles Zhang et.al.,Towards just-in-time middleware architectures. In Proceedings of AOSD 2005, pages 63–74, ACM.

Runtime approaches

Ada Diaconescu et. al., Automatic performance management in component based software systems. In Proceedings of ICAC 2004, pages 214–221, IEEE.John A. Zinky et. al., Architectural Support for Quality of Service for CORBA Objects. Theory and Practice of Object Systems, 3(1):1–20, 1997.Christopher D. Gill et. al., Middleware Scheduling Optimization Techniques for DRE Systems. In Proceedings of WORDS 2002. IEEE Ronghua Zhang et. al., Controlware: A middleware architecture for feedback control of software performance. In Proceedings of ICDCS 2002,IEEE.Chenyang Lu et. al., Feedback control real-time scheduling: Framework, modeling, and algorithms. Real-Time Syst., 23(1-2):85–126, 2002.Lei Gao et. al., Application specific data replication for edge services. In Proceedings of WWW 2003, pages 449–460, ACM Press.Nirmal K. Mukhi et. al., Cooperative middleware specialization for service oriented architectures. In Proceedings of WWW 2004, pages 206–215, ACM Press.Gurdip Singh et. al., Customizing event ordering middleware for component-based systems. In Proceedings of ISORC’05, pages 359–362, IEEE

Deployment-time approaches

Sang Jeong Lee et.al., ISE01-4: Deployment time performance optimization of internet services. In Proceedings of GLOBECOM 2006. pages 1–6, IEEE.

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Related Research: What’s missing?• Design-time approaches

• Lack high-level notation to guide optimization frameworks• Missing AST of application

• Lack application context information available only at deployment-time• Optimizations restricted to

information known at design-time

• Require inputs from designers, i.e., requires changes to applications and/or middleware

N N N N

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N

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Application Abstract Syntax Tree

Related Research: What’s missing?• Runtime approaches

• Reflective approaches, dynamic reconfiguration

• Add additional overhead in the critical path

• Not suitable for all DRE systems

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

• e.g., requires providing multiple implementations

• Deployment-time approaches• Focus on only one dimension,

e.g., performance effects of binding selection

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Node Application

Container

CHCH

CHCH

CHCH

CHCH

CHCH

CHCH

CHCH

CHCH

CH

Receptacle

Facet

Event Sink

Event SourceComponent Home

Component Assembly

Component Remote Invocation

Collocated Invocation

Creates

1.Composition overhead in large-scale systems• Blind adherence to

platform semantics• Inefficient middleware

glue code generation per component

• Examples: • Creation of a

factory object & component context per component

• Increase in overhead with increase in number of components

Component System Optimizations: Unresolved Challenges

Solution Approach: Deployment-time Fusion• New class of optimization

techniques – deployment-time fusion

• Merges multiple elements, e.g., components, QoS policies, into a semantically equivalent element

• Differences in fusion techniques

• Type of elements fused• Scope of fusion• Rules governing fusion

• e.g., Component Fusion • Merges multiple

components into a single component subject to fusion constraints

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Deployment-Time Fusion

Component

Required Interface Provided Interface Event SinkEvent Source

Physical Assembly

Collocation Group Application AssemblyDeployment Plan

Characteristics of Deployment-time Fusion (1/2)

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• If n = no. of candidate elements for fusion, k = no. of elements resulting from fusion, savings due to fusion will be (n – k ) / n

• Best case if k = 1, i.e., fusion creates a single element

• Given an undirected graph • G = (V,E) (fusion graph)

• V = {Candidate elements}• E = {(u,v) | u, v are elements

and CanMerge (u, v) is true}

Characteristics of Deployment-time Fusion (2/2)• If n = no. of candidate elements

for fusion, k = no. of elements resulting from fusion, savings due to fusion will be (n – k ) / n

• Best case if k = 1, i.e., fusion creates a single element

• Given an undirected graph

G = (V,E) (fusion graph)• V = {Candidate elements}• E = {(u,v) | u, v are elements

and CanMerge (u, v) is true}• Finding largest set of elements

that can be fused together = Finding maximum clique in G

• Well-known NP-Complete problem

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Deployment-time Fusion Approach• Enumerate all maximal cliques

• NP-Hard; O(3n/3) time complexity• Our approach

• Use modified Bron-Kerbosch (BK) algorithm to enumerate maximal cliques

• Fastest known algorithm• Use domain-specific heuristics

• Stop enumeration after first maximal clique

• Remove vertices & repeat (safe due to characteristics of BK)

• Only use elements which occur equal number of times as candidates (for component fusion only)

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Maximum Clique

Maximal Clique

Motivating Application

• US Navy Shipboard Computing System• Consists of 150 components – 10 “operational strings” with 15 components

each; deployed across 5 nodes• Sensors – Periodically sends information to the planners• System Monitors – Publish information about health of system• Planners – Process sensor & system monitor input• Effectors – Carry out planner-specified actions

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Candidate Elements

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Fusion Graph (Instance Level)

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Fusion Graph (Type Level)

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Fusion Graph (PAM)

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Output Cliques

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Component Fusion Algorithms (1/2)• Two variants for component

fusion• Local Component Fusion• Global Component Fusion

• Local Component Fusion• Operates at the scope of a

single deployment plan

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Component Fusion Algorithms (1/2)• Two variants for component

fusion• Local Component Fusion• Global Component Fusion

• Local Component Fusion• Operates at the scope of a

single deployment plan• Input

• Set of components deployed into each collocation group on every node of a single deployment plan

• Output• Physical assemblies • Modified assembly &

deployment plan

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Component Fusion Algorithms (2/2)• Global Component Fusion

• Operates at the scope of all deployment plans of a single application

• Set of components that are fused together spans multiple deployment plans

• Merges the individual deployment plans into a unified deployment plan

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Global Component Fusion

Component

Required Interface Provided Interface Event SinkEvent Source

Physical Assembly

Collocation Group Application AssemblyDeployment Plan

Component Fusion Algorithms (2/2)• Global Component Fusion

• Operates at the scope of all deployment plans of a single application

• Set of components that are fused together spans multiple deployment plans

• Merges the individual deployment plans into a unified deployment plan

• Global vs. Local• Increased scope increases

chances of creating larger physical assemblies

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Key Artifact of Component Fusion: 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, e.g., • No two ports of the set of

components should have the same name

• No changes to individual component implementations• Physical Assembly

indistinguishable to external clients• All valid operations on

individual components are still valid

Prototype Implementation

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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

Event SourceComponent Home

Component Assembly

Component Invocation

Creates

CH

CH

CH

CH

CH

CH

CH

CH

CH CH

CHCH

• Physical Assembly Mapper (PAM)

• Uses the application model as the input

• Exploits knowledge of 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-time compiler optimizer”

Applying Component Fusion to Shipboard Computing

• Creates multiple physical assemblies• Creates multiple component attributes corresponding to individual

component attributes• Maintains the same number of connections

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Footprint Experiments Setup• Experiments were conducted

using ISISlab• Five nodes running Windows

XP SP2• CIAO Version 0.5.10 used as

baseline for comparison• Two kinds of footprint

measurements• Static – Code & Static

Data• Dynamic – Heap Memory

used • Use vadump.exe to take a

snapshot of working set of process hosting components

• Measure number of private & shareable pages

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Footprint Results (1/2)

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Node Specific Static Footprint

Node Specific Dynamic Footprint

Total Static Footprint Total Dynamic Footprint31% reduction

49% reduction

18% reduction

45% reduction

Footprint Results (2/2)

• Increased footprint reduction with Global vs. Local component fusion due to• More opportunities for merging components• Creation of consolidated deployment plan• Applicable to more than the internal components of an assembly• Reduces the overhead due to factory objects as well as components

29Component Fusion reduces the footprint significantly

Total Footprint18% reduction

45% reduction

Applicability of Component Fusion Techniques

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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

• e.g., both EJB & Web Services share notion of opaque object/service references

Applicability of Component Fusion Techniques

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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

• Presence of a component context• Components access ports of other

components using a context object• Allows replacing context

transparent to component implementations

• e.g., EJB has a EJBContext which is very similar to CCM’s Context Container

Servant

ComponentSpecificContext

CCMContext

MainComponent

Executor

ExecutorsExecutorsExecutors

POA

EnterpriseComponent

CCMContext

Container

Servant

ComponentSpecificContext

CCMContext

MainComponent

Executor

ExecutorsExecutorsExecutors

POA

EnterpriseComponent

CCMContext

user implemented

code

Container

CORBAComponen

t

POA

Ext

erna

lIn

terf

ace

s

InternalInterface

s

Applicability of Component Fusion Techniques

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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

• 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

Techniques are broadly applicable across different middleware

Stub

Executors

Skel

IDL Compiler

IDL

CIDL

CIDL Compiler

Executor IDL

Servants

Executor Stubs

IDL Compiler

XML ComponentDescriptors

Hand-WrittenGenerated

Inherits

Concluding Remarks

33Tools can be downloaded from www.dre.vanderbilt.edu/CoSMIC/

• Our research • Describes a model-driven

approach to deployment-time optimizations

• Two algorithms• Local and Global

component fusion • Implemented via the

Physical Assembly Mapper (PAM)

• PAM’s deployment-time optimization techniques

• Resulted in a 45% decrease in footprint compared to conventional middleware technologies

Thank you!

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