Surviving the Tornado The Best Kept “Secrets” of R&D Success in the Internet Age

1. Surviving the TornadoThe Best Kept “Secrets” of R&D Success in the Internet Age Dr. Douglas C. Schmidt schmidt@uci.edu Electrical & Computing Engineering Department The Henry Samueli School of Engineering University of California, Irvine

2. High-performance, real-time, fault-tolerant, & secure systems Middleware, Frameworks, & Components • Distributed systems increasingly must reuse commercial-off-the-shelf (COTS) hardware & software • i.e., COTS is essential to R&D success BSE IOM BSE BSE IOM IOM IOM IOM IOM IOM BSE BSE BSE IOM IOM IOM IOM IOM Patterns & Pattern Languages IOM BSE BSE BSE IOM Adaptive & reflective autonomous distributed embedded systems IOM IOM IOM IOM Standards & Open-source Power-aware ad hoc, mobile, distributed, & embedded systems Addressing the COTS “Crisis” • However, this trend presents many vexing R&D challenges for mission-critical systems, e.g., • Inflexibility & lack of QoS • Confidence woes & global competition Why we should care: • Despite IT commodization, progress in COTS hardware & software is often not applicable for mission-critical distributed systems • Recent advances in COTS software technology can help to fundamentally reshape distributed system R&D

3. Historically, mission-critical apps were built directly atop hardware & OS The domain-specific services layer is where system integrators can provide the most value & derive the most benefits Early COTS middleware lacked: • QoS specification & enforcement • Real-time features & optimizations • Layered resource management • Transparent power management The Evolution of COTS • This was extremely tedious, error-prone, & costly over system life-cycles • Standards-based COTS middleware helps: • Leverage hardware/software technology advances • Evolve to new environments & requirements There are multiple COTS layers & research/ business opportunities Advanced R&D has address some, but by no means all, of these issues

4. Consequences of COTS & IT Commoditization • 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 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. Example of R&D Impact:Real-time CORBA & The ACE ORB (TAO) Scheduling Service Standard Synchronizers Thread Pools Protocol Properties Explicit Binding Portable Priorities www.cs.wustl.edu/~schmidt/TAO.html

6. Key Results • Test flown at China Lake NAWS by Boeing OSAT II ‘98, funded by OS-JTF • www.cs.wustl.edu/~schmidt/TAO-boeing.html • Also used on SOFIA project by Raytheon • sofia.arc.nasa.gov • First use of RT CORBA in mission computing • Drove Real-time CORBA standardization Example of R&D Impact:Applying COTS in Real-time Avionics • Goals • Apply COTS & open systems to mission-critical real-time avionics • Key System Characteristics • Deterministic & statistical deadlines • ~20 Hz • Low latency & jitter • ~250 usecs • Periodic & aperiodic processing • Complex dependencies • Continuous platform upgrades

7. Key Software Solution Characteristics • Affordable, flexible, & COTS • Real-time CORBA communication • Compact PCI bus + Celeron processors • Embedded Linux (Lem) • Remote booted by DHCP/TFTP Example of R&D Impact:Applying COTS to Real-time Image Processing www.krones.com • Goals • Examine glass bottles for defects in real-time • System Characteristics • Process 20 bottles per sec • i.e., ~50 msec per bottle • Networked configuration • ~10 cameras

8. Intel x86 & Power PC chipsets • TCP/IP, ATM • POSIX & JVMs • CORBA ORBs & components • Ada, C, C++, RT Java Conventional COTS Limitations Many hardware & software APIs and protocols are now standardized, e.g.: • 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” Inflexible COTS negatively affects researchers & developers

9. LOCAL RESOURCE MANAGERS LOCAL RESOURCE MANAGERS Promising New Solution:Adaptive & Reflective Middleware • Adaptive & reflectivemiddleware 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 CONTRACT CONTRACT DELEGATE DELEGATE SYS COND SYS COND SYS COND SYS COND MECHANISM/PROPERTY MANAGER Research Challenges • 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

10. 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 Network State-of-the-Art in QoS Demos • Limitations • “Stove-pipe” architectures • Only “opportunistic” integration • Lack of multi-property QoS • integration • Not fully autonomous DARPA, AFRL, & Boeing test flight in ‘01

11. Promising New Solution:Frameworks for Integrating QoS Properties • 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 • 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

12. Promising New R&D Strategy:Pattern Languages for QoS Key Theme Patterns & pattern languages codify expert knowledge to help generate software architectures by capturing recurring structures & dynamics and resolving common design forces 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 • Formal semantics • Articulate QoS properties of core architectures • Automation • i.e., auto-generate portions of frameworks & components from pattern languages

13. Recent synergistic advances in fundamentals: Standards-based QoS-enabled Middleware:Pluggable service & micro-protocol components & reusable “semi-complete” application frameworks Patterns and Pattern Languages: Generate software architectures by capturing recurring structures & dynamics & by resolving design forces Revolutionary changes in software process:Open-source, refactoring, extreme programming (XP), advanced V&V techniques Why Can We Make a Difference Now? • Why middleware-centric reuse works • Hardware advances • e.g., faster CPUs & networks • Software/system architecture advances • e.g., inter-layer optimizations & meta-programming mechanisms • Economic necessity • e.g., global competition for customers & engineers

14. App Reqs Standard COTS Concluding Remarks • “Secrets” to R&D success: • Embrace COTS standards • But lead, rather than follow, ignore, or resist • Leverage open-source • i.e., build upon and expand the community • Be entrepreneurial • e.g., use the Web to “market” R&D, help spawn commercial spin-offs • Get “real” • i.e., be relevant, solve the hard problems, partner with industry strategically • Leave an enduring legacy • i.e., be willing to see good R&D ideas all the way through R&D R&D R&D Synergies