1 Research Approach: the Kokyu Flexible Middleware - - PDF document

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1 Research Approach: the Kokyu Flexible Middleware - - PDF document

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Motivation for Studying Adaptive Middleware Flexible Scheduling in Middleware for Trends Distributed Rate-Based Real-Time Hardware keeps getting smaller, faster, & cheaper Applications Software keeps getting larger, slower, &

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SLIDE 1

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Flexible Scheduling in Middleware for Distributed Rate-Based Real-Time Applications

Christopher D. Gill

Dissertation Supervisors: Dr. Ron K. Cytron, Dr. Douglas C. Schmidt Department of Computer Science Washington University, St. Louis, MO cdgill@cs.wustl.edu

Tuesday, December 18, 2001

Historical Challenges

Motivation for Studying Adaptive Middleware

Trends

  • Many mission-critical distributed applications

require real-time QoS guarantees – e.g., combat systems, online trading, telecom

  • Building QoS-enabled applications manually is

tedious, error-prone, & expensive

  • Conventional middleware does not support real-

time QoS requirements effectively

  • Building distributed systems is hard
  • Building them on-time & under budget

is even harder

  • Hardware keeps getting smaller, faster, & cheaper

1 1 Proxy service Service service AbstractService service Client

  • Software keeps getting larger, slower, & more expensive

New Challenges

Overview of Research Areas

Performance Awareness Nimble Adaptation Customizable Middleware

Research Impact Research Approach Technical Challenge

Inclusive Systems Approach Efficient and Safe Systems These technical challenges raise crucial systems issues in both theoretical and empirical dimensions These technical challenges raise crucial systems issues in both theoretical and empirical dimensions

Motivating Applications

Boeing Bold Stroke Middleware Infrastructure Platform

  • Operations well defined
  • Event-mediated middleware solution
  • Previous-generation systems static
  • Next-generation systems dynamic and

adaptive

Company Domain

Krones AG Beverage Bottling Automation Siemens ATD Steel Manufacturing LMCO Sanders, Raytheon Missile & Radar Systems Motorola, Lucent, Nortel, Cisco, Siemens Telecommunications LMCO COMSAT Satellite Control Siemens, GE Medical Information Systems SUTMYN, StorTek Mass storage devices Boeing, Raytheon Avionics mission computing

Limitations With Existing Approaches

Historically, each application has solved scheduling on its own

  • Tedious, error-prone
  • Costly over system lifecycles
  • Single-paradigm approaches

Current middleware lacks hooks for key domain-specific features, e.g.:

  • Optimized integration with higher level managers
  • Hybrid static-dynamic scheduling strategies
  • Strategies built from primitive elements
  • Adaptive domain-specific optimizations

Research Contributions

Systems Architecture and Framework

  • Extends open-source middleware

Patterns for Adaptive Scheduling

  • Capture design experience and solutions

Empirical Evaluation of Adaptive Scheduling

  • Practical benefits for real-world applications
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Integration with Event Service

  • Separates dispatching

mechanism from scheduling policy

  • Dispatcher consults run-

time scheduler for priorities

  • Flexibility for different

scheduling policies

Research Approach: the Kokyu Flexible Middleware Scheduling/Dispatching Framework

Application specifies characteristics

  • e.g., criticality, periods, dependencies

Application specifies characteristics

  • e.g., criticality, periods, dependencies

Dispatcher is (re)configurable

  • Multiple priority lanes
  • Queue, thread, timers per lane
  • Starts repetitive timers once
  • Looks up lane on each arrival

Dispatcher is (re)configurable

  • Multiple priority lanes
  • Queue, thread, timers per lane
  • Starts repetitive timers once
  • Looks up lane on each arrival

Scheduler

sub-graph rate tuples WCET propagation selected rates rate propagation propagated rates tuple visitor

  • peration

visitors

Rate and priority assignment policy

Dispatcher

Dispatching configuration

RMS LLF

mandatory

  • ptional

laxity static static timers

Scheduler assigns rates & priorities per topology, scheduling policy

  • Defines necessary dispatch configuration

Scheduler assigns rates & priorities per topology, scheduling policy

  • Defines necessary dispatch configuration

Implicit projection

  • Of specific scheduling policy into

generic dispatch infrastructure

Implicit projection

  • Of specific scheduling policy into

generic dispatch infrastructure

Greater Utilization with Criticality Isolation

Performance Awareness Nimble Adaptation Customizable Middleware

Research Impact Research Approach Technical Challenge

Inclusive Systems Approach Increased utilization, critical operations still meet their deadlines Arbitrary strategies that hybridize static/dynamic scheduling/dispatching Efficient and Safe Systems

Safety: Meet Critical Deadlines

Dynamic Static same point in execution

Solution: Support for Hybridizing Static & Dynamic Scheduling Heuristics

Abstract mapping: operation characteristics → OS & middleware primitives

  • Generalizes RMS, EDF, LLF, MUF,

RMS+LLF, RMS+EDF, …

  • Arbitrary composition of primitives
  • Drives factory-driven dispatching

module (re)configuration at run-time (work in progress)

Dispatcher

Dispatching configuration

laxity laxity critical non-critical

MUF

No One Strategy is Optimal

Performance Awareness Nimble Adaptation Supports tailored “fit” of scheduling/dispatching Dispatching composed from primitive elements Customizable Middleware

Research Impact Research Approach Technical Challenge

Inclusive Systems Approach Increased utilization, critical operations still meet their deadlines Arbitrary strategies that hybridize static/dynamic scheduling/dispatching Efficient and Safe Systems

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SLIDE 3

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Case for Multi-Paradigm Scheduling

Dynamic1 Dynamic2 Static

)

Solution: Compose Scheduling Heuristics from Dispatching Primitives

Dispatcher

laxity laxity mandatory

  • ptional

Gives Fine Grain Control over Feasibility / Performance Trade-Off

laxity static static static static

feasibility performance

MUF RMS +LLF

Unobtrusive Monitoring and Control Feedback

Provides run-time

  • bservable info for

control, post-analysis Time and space efficient data collection and storage framework Performance Awareness O(n2)/O(n log n) → O(n log n)/O(n) bound

  • n adaptation

Integrated rate/priority selection mechanisms Nimble Adaptation Supports tailored “fit” of scheduling/dispatching Dispatching composed from primitive elements Customizable Middleware

Research Impact Research Approach Technical Challenge

Inclusive Systems Approach Increased utilization, critical operations still meet their deadlines Arbitrary strategies that hybridize static/dynamic scheduling/dispatching Efficient and Safe Systems

Solution: Kokyu Real-Time Metrics Monitoring Framework

EMBEDDED BOARDS REMOTE WORKSTATION

SHARED MEMORY METRICS CACHE REMOTE LOGGER METRICS MONITOR RTARM DOVE Browser (Java)

STORAGE

QuO

OPERATIONS

PROBES

DISPATCHER

P R O B E S FRAME MANAGER

Feedback to Higher-Level Resource Managers Logging and Visualization Customized Data Collection Consistent View of Time

Experimental Test-Bed

Application

  • Research Version of Operational Flight Program for AV-8B Aircraft
  • Added navigation route computations to ramp non-critical load
  • Added critical and non-critical computations to inject execution time jitter

Middleware

  • The ACE ORB (TAO)
  • TAO Real-Time Event Channel
  • Kokyu Framework: Scheduling, Dispatching, and Metrics

Operating System and Hardware

  • VxWorks RTOS on the PPC boards
  • 200 MHz Motorola PPC 604 card
  • two 100 MHz Dy4-177 PPC 603 cards
  • Dy4-783 memory mapped display processor
  • Commercial VME-64 chassis with all boards
  • Switched Ethernet, MIL-STD-1553 MUX Bus on one Dy4-177 card

Popular Scheduling Strategies

Rate Monotonic Scheduling (RMS)

  • Assigns thread priorities by rate
  • Operations at each priority handled in FIFO order

RMS + Minimum Laxity First (MLF)

  • Critical operations managed as in RMS
  • Non-critical operations managed in single lowest priority
  • Non-critical operations handled in minimum laxity (slack time) order

Maximum Urgency First (MUF)

  • Thread priority per criticality level
  • Operations in each priority level handled in laxity order
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Experimental Benchmarks

Measure Performance of Heuristics over Execution Time Jitter & Load

  • Each numbered operating region is stable
  • Transitions between operating regions: changes in SRT load, HRT+SRT jitter
  • Performed using realistic hardware, OS, middleware, OFP application

Region 7 Performance (MUF does Better) Region 8 Performance (RMS+MLF does Better) Adaptation over Scheduling Heuristics

A Map of the Best Performing Heuristic over Execution Jitter & Load

  • RMS performs best if system is under-loaded (theory predicts this)
  • RMS+MLF performs best in overload if jitter is very high or very low
  • MUF performs best if jitter is moderate

A Basis for Adaptive Control

  • Run-time observable measure correlates with performance: operation latency
  • A simple automaton could be constructed

My Contribution: A Unified Middleware Approach

Real QoS problems require both theoretical and empirical perspectives

  • Scheduling theory generalized over OS/middleware primitives → heuristic space
  • Empirical study of specific heuristic (sub-)spaces is crucial
  • Analogy: theory/studies of Ethernet behavior: bin-exp-backoff vs. congestion collapse

Provides run-time

  • bservable info for

control, post-analysis Time and space efficient data collection and storage framework

Performance Awareness

O(n2)/O(n log n) → O(n log n)/O(n) bound

  • n adaptation

Integrated rate/priority selection mechanisms

Nimble Adaptation

Supports tailored “fit” of scheduling/dispatching Dispatching composed from primitive elements

Customizable Middleware

Research Impact Research Approach Technical Challenge

Towards run-time reflective and adaptive policy selection Decision lattice joining a priori analysis with empirical measurement

Inclusive Systems Approach

Increased utilization, critical operations still meet their deadlines Arbitrary strategies that hybridize static/dynamic scheduling/dispatching

Efficient and Safe Systems

Research Impacts and Collaborations

Publications, Systems, & Middleware Topics

  • DASC, 1999
  • DASC, 2000
  • Boeing: ASFD, WSOA, ASTD II, Bold Stroke (in progress)

Real-Time Metrics & Visualization Infrastructure

  • With Boeing, Honeywell: DASC, 1999
  • With Boeing, BBN: ICDCS, 2001
  • With Boeing: DASC, 2001
  • WORDS 2002
  • Boeing: ASTD, WSOA, Bold Stroke

Integrated Middleware Resource Management

  • Journal of Real-Time Systems, 2001
  • IEEE Proceedings special issue (submission in progress)
  • Boeing: ASTD, ASFD, WSOA, Bold Stroke (SEC, MoBIES)
  • Distinct open-source framework (Kokyu) – early 2002

Kokyu Middleware Framework

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Future Research Objectives

Problems, Approaches, & Investigations Topics

  • Integration, cooperation and co-design
  • Resource managers, schedulers, dispatchers, feature sets
  • Across hardware, firmware, OS, middleware, application layers
  • Toward generalized techniques, patterns, and a “complete” theory

and practice of QoS composition for real-world systems

  • E.g., real-time + anytime + adaptive control + decision-aiding +

power-awareness + footprint-awareness + …

Coordinated Multi-Layer Multi-Agent QoS Management

  • Composing middleware scheduling/dispatching points end-to-end
  • Heterogeneous: multiple layers and paths
  • Multi-dimensional resource management: memory, network, CPU
  • Discover/apply good heuristics and domain-specific optimizations
  • Empirical/theoretical study/construction of adaptive decision lattices
  • AOP, domain-specific type systems

Advanced Techniques for QoS Mechanism Instantiation

  • Dispatch/comm/addressing in micro-niches (downward scalability)
  • Interesting design tensions between time/space/power/…
  • “Just enough” middleware: e.g., from Jini-like backbone to an ORB
  • DARPA ITO NEST Program: OEP middleware

Extremely Small Footprint DRE Firm/Soft/ Middleware

Concluding Remarks

Empirical Evaluation

  • Validates adaptive/hybrid scheduling approach
  • Quantifies costs/benefits of discrete alternatives
  • Powerful when combined with theoretical view

–“Mining” technique for problems & properties

Composable Scheduling/Dispatching

  • Enables domain-specific optimizations,

especially when design decisions are aided by empirical data

Heuristic Space Experiments

  • Offer a quantitative blueprint for adaptation

Open-Source Code

  • All software described here that is uniquely a

part of my research will be made available in the ACE_wrappers distribution –Kokyu framework (early 2002) –Dispatching for new TAO Event Channel

Thanks

Mentors

  • Dr. Douglas C. Schmidt, Dr. David L. Levine, and Dr. Ron K. Cytron

Colleagues and Collaborators

  • Faculty and Staff of WU CS and CoE
  • Dr. Douglas Niehaus
  • Mr. David Sharp, Mr. Bryan Doerr, Mr. Don Winter, Dr. David Corman, Dr. Doug

Stuart, Mr. Brian Mendel, Mr. Greg Holtmeyer, Mr. Pat Goertzen, Ms. Jeanna Gossett, Ms. Amy Wright, Mr. Jim Urness, Mr. Tom Venturella, Mr. Russ Wolter

  • Mr. Kenneth Littlejohn (AFRL), Dr. Gary Koob (DARPA ITO)
  • Dr. Rakesh Jha, Mr. John Shackleton, Mr. Nigel Birch
  • Dr. Joseph Loyall, Dr. Richard Schantz, Dr. John Zinky, Dr. David Bakken
  • Dr. Ebrahim Moshiri, Mr. Malcolm Spence, Mr. Kevin Stanley

Friends and Family

  • Members of the DOC Group
  • WU CS Graduate Students
  • My wife Barb and son Paul
  • My Mom Dr. Helen Gill, Dad Mr. David Gill and sister Ms. Sarah E. Gill
  • My parents and siblings-in-law

Additional Questions ?

These slides: http://www.cs.wustl.edu/~cdgill/cdgill_defense.ppt http://www.cs.wustl.edu/~cdgill/cdgill_defense.pdf Contact Information: http://www.cs.wustl.edu/~cdgill cdgill@cs.wustl.edu