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Daniel M. Lorts University of Texas – Dallas and Mercury Computer Systems, Inc. What’s Keeping Hard Real-Time Scheduling from Being a Mainstream Technology in the Embedded Multiprocessing Domain Space HPEC - September 2002 Assumption Model

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slide1

Daniel M. Lorts

University of Texas – Dallas and Mercury Computer Systems, Inc.

What’s Keeping HardReal-Time Scheduling from Being a Mainstream Technology in the Embedded Multiprocessing Domain Space

HPEC - September 2002

assumption model
Assumption Model
  • The choices and assumptions made in the development of real-time systems affect many areas.
  • In this research, we look at six individual, but closely related components of a system architecture.
  • To make the scheduling problem simpler, various assumptions, or boundary conditions, in one or more of the models are typically made.
  • Why?
    • No-hard/complete problem
    • Single semester projects
    • Limited tenureship of research
    • Focused interest/purpose

Application Model

Scheduling Model

Fault Model

Communication

Model

System Model

Technology Model

system communication current
System & Communication (current)

Cache

Cache

Cache

CPU

CPU

CPU

  • System model assumptions
    • Heterogeneous processing assets
    • High-level processing capabilities
    • Fictitious architectures and topologies
    • Assets fully connected
  • Communication model assumptions
    • Negligible communication costs
    • Uniform communication costs
    • Non-contentious communications
    • No priority discernment

Memory

Memory

Memory

Interconnection Network

multi level system graph

S = (R, L) top level system definition

R = (r1, r2 ,...,rN) N resources of system

L = {l<ŕ,c> | ŕ  R  c > 0  Adj(ŕ ) = 1}

  • l represents a link connecting two resources
  • ŕ is a subset of the resources of system (R)
  • c represents the number of links between the resources
  • Adj() is a binary function testing for presence of links
Multi-Level System Graph
  • Advantages of the MGS:
    • Multi-path capability between resources
    • Total # of communication links bounded by I/O ports
    • Ability to model all multiprocessing topologies
    • Scalable to account for resources that have multiple functional units

r1={c1, c2,…, cM}

where cj is a unique capability of resource r

an msg example
An MSG Example

4

S=(R,L) where

R={R1,R2,R3,R4}

and L={l<ŕ1,c1>, l< ŕ2,c2>, l< ŕ3,c3>, l< ŕ4,c4>}

with ŕ1 = {R1} and c1=1

ŕ2 = {R1,R2} and c2=1

ŕ3 = {R2,R4} and c3=2

ŕ4 = {R2,R3,R4} and c4=1

1

R1

R2

2

3

R3

R4

R1

R2

R3

R4

Alternate Representation

The system model can also be defined mathematically within a table. Each cell represents the paths that exist between each resource : l<id,count>.

R1

l(4,1)

l(1,1)

R2

l(1,1)

l(2,1)

l(2,1)

l(3,2)

R3

l(2,1)

l(2,1)

l(2,1)

l(2,1)

R4

l(3,2)

scheduling model

Scheduling

Global

Local

Dynamic

Static

Optimal

Sub-optimal

Physically distributed

Physically centralized

Cooperative

Non-cooperative

Approximate

Heuristic

Sub-optimal

Optimal

Approximate

Heuristic

Scheduling Model
  • Dynamic scheduling
    • Transfer policy
    • Selection policy
    • Location policy
    • Information policy
  • Heuristics include:
    • Heavy Node First (HNF)
    • Critical Path Method (CPM)
    • Weighted Length (WL)
    • Earliest Deadline First (EDF)
    • Least Laxity First (LLF)
dynamic framework
Dynamic Framework

p0

p1

p3

  • Allocation stage: statically assigning processes to resources
  • Scheduling stage: dynamically based on allocation and application
  • Provides run-time analysis of loading and balance
  • Scalable solution for all multiprocessing system applications
  • Possible multicomputer processor configurations
    • Round-robin/next available
    • Single parallel cluster
    • Pipeline of parallel clusters
    • Hybrid

p0

p5

p4

p2

p4

p5

p6

p3

p5

p1

Interconnect Network

application model

W

X

Y

Z

*

+

/

A

Application Model
  • Non-realistic program models (DAGs)
  • Task model limited
    • Uniform temporal metrics
    • Preemptability
    • Entry points into nodes
    • Typically unary dependencies
    • Limited methods of prioritization
    • Acyclic models limited

Directed Acyclic Graph

  • Task Variables
    • Computational times
    • Communications times
    • Deadlines (laxity)
    • Precedence
      • Number of children
      • Number of parents
technology model
Technology Model

Programming

Difficulties

CPU Performance

Memory Bandwidth

Relative Performance

I/O Bandwidth

Time

  • Other technological issues
    • Compiler optimization techniques
    • Multifunctional resources
    • Size, weight, and power considerations
fault modeling
Fault Modeling

Drivers

  • Visibility
  • Cost
  • Affects

CAUSES

Fault Avoidance

Specification

Implementation

Externals

Defects

TYPES

Fault Masking

Hardware

Software

ERRORS

  • Fault model
    • Too simplistic
    • Single fault assumption
    • Fault isolated to task or processor
    • Limited recovery techniques
    • Inconsistent QoS issues

Fault

Tolerance

FAILURES

the scheduling framework
The Scheduling Framework

Temporal

Characteristics Hard, Soft, Fuzzy, Real-Time

Static Plus Dynamic Adaptive

Structured

Development

A Real-Time Hybrid Scheduling Framework for Fault Tolerant Polymorphic Computing

Plan for Detection, Correction, and Recovery

Quality of Service (QoS)

Runtime Performance

Development Efficiency

Application Awareness

Reconfigurability

framework details
Framework Details
  • Structured development
    • Provides foundation
    • Validated by mapping know architecture
  • Hybrid scheduling
    • Focal point of research
    • Static and dynamic approaches
  • Real-Time
    • Formal temporal methods
  • Fault tolerance
    • Dynamic detection, correction, and recovery
  • Polymorphism
    • Reactive environments
    • Efficient ‘morphing’
  • Computing
    • Quality of Service (QoS)
    • Usability and generality