darpa dr. douglas c. schmidt dschmidt@darpa.mil darpa/ito approved for public release, distribution...
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DARPADARPA
Dr. Douglas C. Schmidtdschmidt@darpa.mil
DARPA/ITO
Approved for Public Release, Distribution Unlimited
Adaptive and Reflective Middleware Systems
Tuesday, April 18, 2023
2 D. Schmidt
DARPADARPA
High-performance, real-time, fault-tolerant, and secure systems
Adaptive & reflective autonomous distributed embedded systems
Power-aware ad hoc, mobile, distributed, & embedded systems
Middleware, Frameworks, & Components
Patterns & Pattern Languages
Standards & Open-source
Addressing the COTS “Crisis”
However, this trend presents many vexing R&D challenges for mission-critical DoD systems, e.g., • Inflexibility and lack of QoS• Confidence woes & global competition
Distributed systems increasingly must reuse commercial-off-the-shelf (COTS) hardware & software• i.e., COTS is essential to R&D success
Why DARPA should care:
• Recent advances in COTS software technology can help to fundamentally reshape distributed embedded system R&D
• Despite IT commodization, progress in COTS hardware & software is often not applicable for mission-critical DoD distributed embedded systems
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DARPADARPA
There are multiple COTS layers & multiple research
opportunities
Historically, mission-critical apps were built directly atop hardware
The common middleware & domain-specific services layers are where many of the open R&D challenges reside
The common middleware & domain-specific services layers are where many of the open R&D challenges reside
The Evolution of COTS
Standards-based COTS middleware helps:•Manage distributed resources•Leverage HW/SW technology advances•Evolve to new environments & requirements
& OS•This was extremely tedious, error-prone, & costly over system life-cycles
•QoS specification & enforcement
•Real-time features & optimizations
•Layered resource management
•Transparent power management
Early COTS middleware lacked:
Advanced R&D has address some, but by no means all, of these issues
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DARPADARPA
•More emphasis on integration rather than programming
•Increased technology convergence & standardization
•Mass market economies of scale for technology & personnel
•More disruptive technologies & global competition
•Lower priced--but often lower quality--hardware & software components
•The decline of internally funded R&D•Potential for complexity cap in next-generation complex systems
Consequences of COTS & IT Commoditization
Not all trends bode well for long-term competitiveness of traditional R&D leaders
Ultimately, competitiveness will depend upon longer-term R&D efforts on complex distributed & embedded systems
5 D. Schmidt
DARPADARPAThe DARPA/ITO Embedded
Systems Family of Programs
SECSEC• Hybrid, adaptive, control & computationHybrid, adaptive, control & computation
QuorumQuorum• Quality-of-service & translucent layers
MoBIESMoBIES• Design technology & software CAD
ARMSARMS• Adaptive & reflective middlewareAdaptive & reflective middleware
PCESPCES• Composable embedded systems
NESTNEST• Deeply networked embedded systems
PCAPCA• Polymorphous computing architecture
6 D. Schmidt
DARPADARPAExample of DARPA ITO Impact: Real-time CORBA Specification
www.cs.wustl.edu/~schmidt/PDF/orc.pdf
Protocol Properties
Explicit Binding
Thread Pools
SchedulingService
StandardSynchronizers
Portable Priorities
7 D. Schmidt
DARPADARPAExample of ITO Impact:
COTS in Real-time Avionics
Key System Characteristics• Deterministic & statistical deadlines• Periodic & aperiodic processing• Complex dependencies• Low latency & jitter• Continuous platform upgrades
•Static scheduling & validation
•Small-scale network topology
•Static scheduling & validation
•Small-scale network topology
•Limited fault tolerance & security support
•Used some non-standard COTS features
•Limited fault tolerance & security support
•Used some non-standard COTS features
Limitations
• Test flown at China Lake NAWS by Boeing OSAT II ‘98, funded by OS-JTF
• Drove Real-time CORBA standardization
Key Results
GoalsDemo applicability of COTS & open systems for mission-critical RT avionics
8 D. Schmidt
DARPADARPAExample of ITO Impact:
Real-time Image ProcessingGoals•Examine glass bottles for defects in real-time
System Characteristics•Process 20 bottles per sec• i.e., ~50 msec per bottle
•Networked configuration
•~10 camerasKey Software Solution Characteristics
•Affordable, flexible, & COTS•Embedded Linux (Lem)•Compact PCI bus + Celeron processors
•Affordable, flexible, & COTS•Embedded Linux (Lem)•Compact PCI bus + Celeron processors
•Remote booted by DHCP/TFTP•Real-time CORBA
www.krones.com
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DARPADARPA
Example of R&D Impact:
Hot Rolling Mill Control FrameworkGoals•Control the processing of molten steel moving through a hot rolling mill in real-time
System Characteristics•Hard real-time process automation requirements• i.e., 250 ms real-time cycles
•System acquires values representing plant’s current state, tracks material flow, calculates new settings for the rolls & devices, & submits new settings back to plant
Key Software Solution Characteristics
•Affordable, flexible, & COTS•Product-line architecture•Design guided by patterns & frameworks
•Affordable, flexible, & COTS•Product-line architecture•Design guided by patterns & frameworks
•Windows NT/2000 (+ VMS!)•Real-time CORBA
www.siemens.de
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DARPADARPA
Time-critical targets require immediate response because:•They pose a clear and present danger to friendly forces &•Are highly lucrative, fleeting targets of opportunity
Example of ITO Impact:
WSOA/QuoTE Time-Critical Target Prosecution
A d a p t e d f r o m “ T h e F u t u r e o f A W A C S ” ,b y L t C o l J o e C h a p a
J o i n t F o r c e sG l o b a l I n f o G r i d
J o i n t F o r c e sG l o b a l I n f o G r i d C h a l le n g e
is t o m a k e t h is p o s s ib le !
Challenges are also relevant to TBMD & NMD
WSOA Goals• Detect, identify, track, & destroy time-critical targets
Key Solution Characteristics• Affordable & flexible• COTS-based• Affordable & flexible• COTS-based
• High confidence • Safety critical• High confidence • Safety critical
• Real-time mission-critical sensor-to-shooter needs
• Highly dynamic QoS requirements & environmental conditions
• Multi-service & asset coordination via Link 16
Key System Characteristics
D. Schmidt
DARPADARPAExample of ITO Impact: Large-scale Switching Systems
IOM
IOM
IOM
IOM
IOM
IOM
IOM
IOM
IOM
IOM
IOM
IOM
IOM
IOM
IOM
IOM
IOM
IOM
BSE
BSE
BSE
BSE
BSE
BSE
BSEBSE
BSEGoal•Switch ATM cells + IP packets at terabit rates
Key Software Solution Characteristics
•High confidence & scalable computing architecture•Networked embedded processors•Distribution middleware•FT & load sharing•Distributed & layered resource management
•Affordable, flexible, & COTS
•High confidence & scalable computing architecture•Networked embedded processors•Distribution middleware•FT & load sharing•Distributed & layered resource management
•Affordable, flexible, & COTS
Key System Characteristics•Very high-speed WDM links
•102/103 line cards•Stringent requirements for availability
•Multi-layer load balancing, e.g.:•Layer 3+4•Layer 5
www.arl.wustl.edu
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DARPADARPA
• Large-scale (~5-250 miles)• Integrated QoS properties• Highly dynamic environments
• Semi-autonomous & re-configurable distributed systems
Network
• Broader scope• Hybrid scheduling & RT ARM
• Heterogeneous middleware & languages
• Hard real-time deadlines (~40hz)
• Low jitter & latency (~300us)
• Very hard real-time deadlines (>= 100hz)
• Very low latency (~10us)
• Highly autonomous & re-configurable hybrid & embedded systems
• Address fundamental QoS properties of distributed embedded & hybrid systems
•Enhance confidence of open-source R&D processes and V&V techniques
• Devise middleware-centric methods & tools to develop, optimize, & manage systems with multiple QoS properties
Research Goals
RTP
DNS
HTTP
UDP TCP
IP
TELNET
ETHERNET ATM FDDI
FIBRE CHANNEL
FTP
INTERNETWORKING ARCH
TFTP
Advance the state-of-the-art in adaptive & reflective middleware to coordinate multiple QoS properties of mission-critical distributed embedded & hybrid systems
Advance the state-of-the-art in adaptive & reflective middleware to coordinate multiple QoS properties of mission-critical distributed embedded & hybrid systems
Why the “waist” works:1.Decouples hardware from software so they
can evolve separately2.Decouples low-level capabilities from higher-
level capabilities to enhance innovation3.Decouples fast changing layers from slower
changing layersHowever, the waist can also restrict choices…
What Are We Trying to Do?
VIRTUAL MACHINE ARCH
Ix86 TI DSP 68K
PA/RISC PowerPC
Java VM Interpreter
Java Ada C/C++
Java Bytecode
WINNT LINUX LYNXOS
SOLARIS VXWORKS
CORBA
CORBASERVICES
CORBAAPPLICATIONS
MIDDLEWARE ARCH
13 D. Schmidt
DARPADARPANew Challenges: Theater Ballistic Missile Defense
Goal•Detect, identify, track, & destroy multiple theater ballistic missiles
•Meeting hard real-time sensor-to-shooter needs in highly dynamic environment
•Providing load-invariant performance
•Supporting QoS for distributed weapons coordination
Key Research Challenges
-4 -2 0 42Relative Range (km)
Alt
itu
de
(k
m)
260
230
180
Re
lati
ve
Tim
e (
s)
112
56
0
RVTank
50 m/s DispersionVelocity
Req
uir
ed C
om
pu
te P
ow
er
Required Processing
TBMD Phases
Radar Control Program Sizing EstimatesRadar Control Program Sizing Estimates
Estimated data, based on 6 Ph3 & 7 Ph1 design & projections of Block I & Block II NTW requirements
Estimated data, based on 6 Ph3 & 7 Ph1 design & projections of Block I & Block II NTW requirements
AREA 6 Ph 3AREA 7 Ph 1NTW BLK 1A
NTW BLK 1BNTW BLK 1CNTW BLK 2
TBMD Phases:
SMP Load Scale Limits
Key Solution Characteristics
•Highly dependable & scalable computing architecture•High-speed networked clusters of multi-processor computers
•Distribution middleware•Fault tolerance & load sharing•Distributed & layered global resource management
•Affordable, flexible, & COTS
•Highly dependable & scalable computing architecture•High-speed networked clusters of multi-processor computers
•Distribution middleware•Fault tolerance & load sharing•Distributed & layered global resource management
•Affordable, flexible, & COTS
14 D. Schmidt
DARPADARPAExample: Real-time Retargeting of UAVs
Key System Characteristics•Autonomous behavior•e.g., battlespace loitering of UAVs
•Coordinated strikes•e.g., C4ISR integration & UAV swarming for SEAD missions
• Real-time sensor-to-shooter QoS scheduling of sea/land/air assets
• Developing efficient, predictable, & safe adaptive HW/SW systems for autonomous UAV processing
• Real-time sensor-to-shooter QoS scheduling of sea/land/air assets
• Developing efficient, predictable, & safe adaptive HW/SW systems for autonomous UAV processing
•Secure integration to C4ISR infosphere• e.g., proof-carrying code & automated policy-based control
•Transition to COTS HW/SW to control costs & leverage technology advances
•Secure integration to C4ISR infosphere• e.g., proof-carrying code & automated policy-based control
•Transition to COTS HW/SW to control costs & leverage technology advances
Key Research Challenges
Goal•Reconfigure & retarget unmanned air vehicles (UAVs) in real-time
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DARPADARPANew Challenge:
Situational Awareness Systems
Key System Characteristics• Mission-critical, sensor-rich• Multi-asset coordination
• e.g., UAVs, MEMs, SUOs• Ad hoc wireless/mobile
infrastructure• Highly non-linear and dynamic• Non-uniform resource constraints
• Integrate multiple QoS properties simultaneously• e.g., dependability, security, &
bandwidth management• Tele-immersion situation monitoring
• e.g., QoS-based event fusion
• Integrate multiple QoS properties simultaneously• e.g., dependability, security, &
bandwidth management• Tele-immersion situation monitoring
• e.g., QoS-based event fusion
•Adaptive & reflective peer-to-peer information system coordination
• e.g., power-aware systems•COTS-based to control costs & to leverage rapid technology advances
• e.g., wireless tracking & local info
•Adaptive & reflective peer-to-peer information system coordination
• e.g., power-aware systems•COTS-based to control costs & to leverage rapid technology advances
• e.g., wireless tracking & local info
Key Solution Characteristics
Goal• Support dispersed & rapidly
deployable teams
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DARPADARPAExample:
Coordinated Disaster Response
Key System Characteristics• Mission-critical & time-critical systems• Multi-agency coordination
• e.g., police, fire, FBI, medical• Ad hoc wireless/mobile infrastructure• Highly non-linear and dynamic• Non-uniform resource constraints
Goal• Support rapidly deployable & dis-
persed emergency response teams
• Integrate multiple QoS properties simultaneously• e.g., dependability, security, &
bandwidth management• Tele-immersion situation monitoring
• e.g., QoS-based event fusion
• Integrate multiple QoS properties simultaneously• e.g., dependability, security, &
bandwidth management• Tele-immersion situation monitoring
• e.g., QoS-based event fusion
Key Solution Characteristics•Adaptive & reflective peer-to-peer information system coordination
• e.g., power-aware systems•COTS-based to control costs & to leverage rapid technology advances
• e.g., wireless tracking & local info
•Adaptive & reflective peer-to-peer information system coordination
• e.g., power-aware systems•COTS-based to control costs & to leverage rapid technology advances
• e.g., wireless tracking & local info
17 D. Schmidt
DARPADARPATechnology Enablersand Business Drivers
Enablers/Drivers
Effect
Increasing budget & schedule pressures
Motivates search for effective COTS solutions
Recent advances in standards - OMG Real-time CORBA
- DISA Real-time DII COE
- DMSO HLA + RTI
Improved “market” - More options & competition - Reduce “COTS refresh” costs - 3rd party component providers
Recent successes for mission- critical system
- Both DoD & commercial
- Increased credibility of R&D community - Integrators willing to try COTS
Maturation of the field, i.e., COTS & middleware are “crossing the chasm” in research & industry
- Increased quality & quantify of R&D activities - Growth of technologically skilled labor pool
18 D. Schmidt
DARPADARPAProblem Abstraction
Distributed Environment• Heterogeneous hardware/software• Mixed COTS & non-COTS components• Mixed RT and non-RT requirements• Wireline & wireless interconnects
Distributed Environment• Heterogeneous hardware/software• Mixed COTS & non-COTS components• Mixed RT and non-RT requirements• Wireline & wireless interconnects
Adaptivity & Reflection Targets• Dynamic component distribution & reconfiguration• Changing interconnection topology• Changing power-levels, CPU/network bandwidth,
latency, security, & dependability requirements
Adaptivity & Reflection Targets• Dynamic component distribution & reconfiguration• Changing interconnection topology• Changing power-levels, CPU/network bandwidth,
latency, security, & dependability requirements
19 D. Schmidt
DARPADARPAThe Hard Problems
Decoupling functional aspects from QoS aspects
Automatically generating & optimizing multiple QoS properties adaptively & reflectively
Articulating pattern languages & reifying them into QoS-enabled frameworks & components
Leveraging, customizing, enhancing, & validating open-source COTS components
20 D. Schmidt
DARPADARPAConventional COTS Limitations
Many hardware & software APIs and protocols are now standardized, e.g.:
Inflexible COTS negatively affects researchers & developersInflexible COTS negatively affects researchers & developers
While COTS standards promote reuse, they limit design choices, e.g.:• Networking protocols• Concurrency & scheduling• Caching• Fault tolerance• Security
Historically, COTS tightly couples functional & QoS aspects• e.g., due to lack of “hooks”
• TCP/IP, ATM• POSIX & JVMs• CORBA ORBs & components
• Intel x86 & Power PC chipsets
• Ada, C, C++, RT Java
21 D. Schmidt
DARPADARPAPromising New Solution: Adaptive & Reflective Middleware
Research Challenges
MECHANISM/PROPERTYMANAGER
SYS COND SYS COND SYS COND SYS COND
DELEGATE DELEGATE
CONTRACT CONTRACT
LOCALRESOURCEMANAGERS
LOCALRESOURCEMANAGERS
•Preserve critical set of application QoS properties end-to-end• e.g., efficiency, predictability, scalability, dependability, & security
•Achieve load invariant performance & system stability
•Preserve critical set of application QoS properties end-to-end• e.g., efficiency, predictability, scalability, dependability, & security
•Achieve load invariant performance & system stability
•Maximize longevity in wireless & mobile environments• e.g., control power-aware hardware via power-aware middleware
•Automatically generate & integrate multiple QoS properties
•Maximize longevity in wireless & mobile environments• e.g., control power-aware hardware via power-aware middleware
•Automatically generate & integrate multiple QoS properties
Adaptive & reflective middleware is middleware whose functional or QoS-related properties can be modified either •Statically, e.g., to better allocate resources that can optimized a priori or
•Dynamically, e.g., in response to changes in environment conditions or requirements
22 D. Schmidt
DARPADARPAThe Hard Problems
Decoupling functional aspects from QoS aspects
Automatically generating & optimizing multiple QoS properties adaptively & reflectively
Articulating pattern languages & reifying them into QoS-enabled frameworks & components
Leveraging, customizing, enhancing, & validating open-source COTS components
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DARPADARPA
Network
Key Themes of WSOA• Real-time mission replanning & collaboration
• e.g., C2 node & F-15 share data imagery & annotations
• Shows adaptive QoS behavior is feasible within demanding real-world constraints
• Showcase academic & industry synergy
Limitations• “Stove-pipe” architectures• Only “opportunistic” integration• Lack of multi-property QoS integration• Not fully autonomous
Limitations• “Stove-pipe” architectures• Only “opportunistic” integration• Lack of multi-property QoS integration• Not fully autonomous
State-of-the-Art in QoS Demos
DARPA, AFRL, & Boeing test flight in ‘01
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DARPADARPA
Key Themes•Handle variation translucently
• QoS aspect languages• Smart proxies & interceptors• Pluggable protocols & adapters• Middleware gateways/bridges
•Ideally, implementations should be generated from higher-level specifications
Promising New Solution: Middleware Frameworks for Integrating Multiple QoS Properties
Research Challenges•Model, compose, analyze, & optimize QoS framework component properties
•Leverage configurable & adaptive hardware capabilities
• e.g., power management, high-speed QoS-enabled bus & network interconnects
Research Challenges•Model, compose, analyze, & optimize QoS framework component properties
•Leverage configurable & adaptive hardware capabilities
• e.g., power management, high-speed QoS-enabled bus & network interconnects
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DARPADARPA
•Early compilers required •Separate internal representations hand-written for each programming language and
•Separate hand-written optimizers for each target backend
•Developing, verifying, validating, & evolving all these components separately is costly, time-consuming, tedious, & error-prone
•The problem only gets worse as more languages & target backends emerge
Applying Reflection as an Optimization Technique
C Compiler
Internal Rep.
Ix86Opt.
Ix86
PPCOpt.
PPC
68KOpt.
68K
C Program
To illustrate the benefits of reflection as an optimization technique, consider the evolution of compiler technology:
C++ Compiler
Internal Rep.
Ix86Opt.
PPCOpt.
68KOpt.
Ix86 PPC 68K
C++ Program
Ada Compiler
Internal Rep.
Ix86Opt.
PPCOpt.
68KOpt.
Ix86 PPC 68K
Ada Program
26 D. Schmidt
DARPADARPAApplying Reflection as an
Optimization Technique
C/C++/Ada Compiler
Common Internal Rep.
Ix86Opt.
PPCOpt.
68KOpt.
C/C++/Ada Programs
Ix86
Ix86.md
PPC
PPC.md
68K
68K.md
•Modern compilers, such as GNU GCC, support •A common internal representation (still hand-written) for each programming language •Based on generalizing the language semantics
1. Read the target machine description
Optimizer Generator
2. Use discrimination network to analyze the optimization rules & opportunities
3. Generate an optimizer that is customized for the particular platform/language
•A generated optimizer that is customized automatically for each target backend•Based on reflective assessment of algebraic target machine description
Key Benefit of “Static” Reflection•New targets can be supported by writing a new machine description, rather than writing a new code generator/optimizer
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DARPADARPA
•Developing, verifying, validating, & evolving all these components separately is costly, time-consuming, tedious, & error-prone
•Moreover, it is even harder to hand-configure support for dynamic platform variations & complex application use-cases
•The problem only gets worse as more middleware, target platforms, & complex applications emerge
•Separate hand-written & hand-optimized implementations for each embedded target platform•e.g., various OS/network/HW configurations
•Conventional middleware require•Separate tools and interfaces hand-written for each ORB middleware specification •e.g., CORBA, Java RMI, COM+
Applying Reflection to Optimize Middleware Statically
CORBA ORB & Assorted Tools
CORBA Application
Conventional middleware for embedded systems is developed & optimized in a manner similar to early compiler technologies:
WinNT
WinNTImpl
Solaris
SolarisImpl
VxWorks
VxWorksImpl
Java RMI & Assorted Tools
WinNT Solaris
Linux
Java Application
WinNTImpl
SolarisImpl
LinuxImpl
COM+ ORB & Assorted Tools
WinNT Win98
WinCE
COM+ Application
WinNTImpl
Win98Impl
WinCEImpl
28 D. Schmidt
DARPADARPAApplying Reflection to
Optimize Middleware Statically
Common ORB + Assorted Tools
Common Semantic Representation
Plat1
Impl
•The functional and QoS-related aspects of middleware can be improved greatly by advanced R&D on the following topics:•A common internal representation (ideally auto-generated) for each middleware specification •Based on generalizing the middleware semantics
Middleware Generator
2. Use discrimination network to analyze the optimization rules & opportunities
3. Generate middleware that is customized for a particular platform & application use-case
•A generated implementation that is optimized automatically for each target platform & application use-case•Based on reflective assessment of platform descriptions & application use-case
Plat2
Plat2
.pd
Plat2
ImplPlat3
Impl
1. Read the target platform description & application requirements
Ap
plic
atio
n R
equ
irem
ents
CORBA/Java/COM+ Applications
Plat3
Plat3
.pd
Plat1
Plat1
.pd
29 D. Schmidt
DARPADARPA
Client Object
ORB endsystem
ORB endsystem
ResourceResource
Resource
Applying Reflection to Optimize Middleware Dynamically
Key System Characteristics
• Integrate observing & predicting of current status & delivered QoS to inform the meta-layer
•Meta-layer applies reflection to adapt system policies & mechanisms to enhance delivered QoS
Delegate Delegate
QuOContracts
Applying reflection as an optimization is even more relevant to middleware than compilers due to dynamism & global resources:
Probes Probes
Probes Probes
PiggybackedMeasurements
Status
ExpectedQoS
MeasuredQoS
CorrelateProbes
Resource Status Service
CollectTranslateIntegrate
Infer/AdaptFeedback
Loop
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DARPADARPA
Key Research Challenge:
Providing QoS Guarantees for Multiple Adaptive Feedback Loops
Goals•Ensuring stable QoS support at varying granularity & scope levels for integrated, multi-property feedback paths across different locations & time scales
•Determining patterns, protocols, and architectures necessary to integrate COTS components
Client Object
CombinedSystem-level & Application-level
Management Feedback
End-to-EndApplication-centric
Feedback
End-to-EndApplication-centric
Feedback
LocalResource-
centricFeedback
LocalResource-
centricFeedback
31 D. Schmidt
DARPADARPAThe Hard Problems
Decoupling functional aspects from QoS aspects
Automatically generating & optimizing multiple QoS properties adaptively & reflectively
Articulating pattern languages & reifying them into QoS-enabled frameworks & components
Leveraging, customizing, enhancing, & validating open-source COTS components
32 D. Schmidt
DARPADARPANew Idea: Pattern Languages for QoS
Research Challenges•Identifying QoS pattern languages
• Broaden the focus of conventional pattern-related tools and pattern languages, which focus on simple structural & functional behavior
•Model QoS-enabled middleware via pattern languages• Must understand how to build high-confidence
systems before we can automate V&V
•Identifying QoS pattern languages• Broaden the focus of conventional pattern-related
tools and pattern languages, which focus on simple structural & functional behavior
•Model QoS-enabled middleware via pattern languages• Must understand how to build high-confidence
systems before we can automate V&V
• Formal semantics• Articulate QoS properties of core architectures
• Automation• i.e., auto-generate portions of frameworks & components from pattern languages
• Formal semantics• Articulate QoS properties of core architectures
• Automation• i.e., auto-generate portions of frameworks & components from pattern languages
Key ThemePatterns & pattern languages codify expert knowledge to help generate software architectures by capturing recurring structures & dynamics and resolving common design forces
33 D. Schmidt
DARPADARPAThe Hard Problems
Decoupling functional aspects from QoS aspects
Automatically generating & optimizing multiple QoS properties adaptively & reflectively
Articulating pattern languages & reifying them into QoS-enabled frameworks & components
Leveraging, customizing, enhancing, & validating open-source COTS components
34 D. Schmidt
DARPADARPA
“Everything gets cheaper forever”• John Chambers, CEO Cisco Systems
Quality team
Teamwork
Drive Change
No technology religion
Empowerment
Frugality
Market Transitions
Stretch Goals
Trust/Fair/ Integity
Open Communication
Cisco CultureWhy COTS?•Commercial & military suppliers are increasingly driven by competitive forces•e.g., time-to-market/mission pressures & heavy competition for engineering talent
•COTS can contribute systematic reuse, continuous innovation, & cost reduction via 3rd party life-cycle management
•COTS can potentially reduce V&V costs•Lack of realistic alternatives…
Key COTS R&D Challenges
Key Technology Inhibitors to Success
•Integration woes•COTS components are often not designed for composition
•V&V and security woes•COTS components rarely designed for certification or high assurance
•Integration woes•COTS components are often not designed for composition
•V&V and security woes•COTS components rarely designed for certification or high assurance
•Inefficient feedback loops•e.g., “binary-only,” closed-source deployment hampers usability
•COTS is not always standard•Non-standard COTS can greatly increase “refresh” costs
•Inefficient feedback loops•e.g., “binary-only,” closed-source deployment hampers usability
•COTS is not always standard•Non-standard COTS can greatly increase “refresh” costs
35 D. Schmidt
DARPADARPAEmerging trend in commodity IT market: Standard Open-source COTS
Open-source is a highly scalable and cost effective software process based on the following observations: • Validation scales, development does not• “End-to-end argument” applies to software
• i.e., more resources at the “edges”
Benefits for Developers •Standards help to ensure longer-term viability of technology investments
•Standard COTS helps control life-cycle costs
•Standard open-source COTS helps to focus expertise, e.g.:•Leverage “everyone’s a beta-tester” syndrome
•Resolves “COTS vs. ownership” conundrum in system acquisition
Benefits of Open-source for Researchers:•Leverage existing technology base for rapid prototyping of new technologies
•Promote broad visibility of novel R&D activities
•Accelerate the pace & impact of technology transfer
•Lead, rather than follow, COTS software trends
36 D. Schmidt
DARPADARPA
New opportunity: High Confidence Open-source Software Systems
EMACS, ACE, & USENET servers
COTS desktop productivity tools
Linux, Apache, GNU tools, & TAO
Solaris & Windows NT
Next-generationmiddleware
Flight critical software
Open-sourceClosed-source
No Spec
InformalSpec
FormalSpec
Open-source standard COTS is now mainstream at certain layersOpen-source standard COTS is now mainstream at certain layers
Bold Stroke Key Themes•We know how to build open-source software quickly and cheaply•Quality and security remain key challenges, however…
•Open-source enables whitebox V&V techniques•e.g., analysis methods can extend across layers & thru components
•Reuse of middleware components can help amortize V&V efforts•No need to (re)start from scratch
•Middleware is often written in relatively “civilized” languages • cf. operating system kernels
•Middleware defines natural module boundaries for specification & testing•e.g., define QoS properties via QDLs
37 D. Schmidt
DARPADARPAConcluding Remarks•Researchers & developers of distributed systems face common challenges, e.g.:
•The application of formal methods along with adaptive & reflective patterns, frameworks, & components can help to resolve these challenges
•Carefully applying these techniques can yield efficient, scalable, predictable, & flexible middleware & applications
•Connection management, service initialization, error handling, flow control, event demuxing, distribution, concurrency control, fault tolerance synchronization, scheduling, & persistence
Summary of Research Themes in the ARMS Program•Decouple functional aspects & QoS aspects •Specify & apply component QoS as meta-data
•Devise adaptive & reflective methods,optimizations, & tools that can provide scalable, multi-property QoS guarantees end-to-end
•Enable high-confidence autonomous
system capabilites •Leverage standard COTS APIs
• But not necessarily COTS implementations or protocols
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