analysis of real-time multi-modal fp-scheduled systems with non-preemptible regions authors: masud...
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Analysis of Real-Time Multi-Modal FP-Scheduled Systems with Non-Preemptible Regions
Authors: Masud Ahmed (presenting)
Pradeep HettiarachchiNathan Fisher
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Department of Computer ScienceWayne State University.
This research was supported by NSF, Wayne State University, and MathWorks Inc
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Outline
Introduction: Adaptive Cruise Control (ACC) Systems Multi-Modal System (MMS)
Models: Sporadic Tasks Periodic Resources
Contributions: Protocol for a Mode-Change Determination of FP Schedulability Non-preemptive execution Usability
Future Work
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Motivation: Real-Time ACC
Automotive ACC Systems Alerts driver if front vehicle is too close Use 77GHz Radar Transmission/Receiving
Design constraints Non-preemptive radar sweep Max Sweep Time Number of sweep
7µs and 2µs for 200m and 50m respectivelyHigher number implies better accuracy
Radar Sweep
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Multi-Mode ACC
We consider Multi-Mode System for ACC Software mode
Exploit smaller sweep-time Use higher number of sweep Utilize low priority task to reclaim idle cycles Tasks (with non-preemptive region) scheduled
by FP Hardware mode
Enable shared platformFP-Schedulability analysis of a
MMS is computationally expensive.
No MMS support for non-preemption
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Related Work
Santinelli et al. (2011), Stoimenov et al. (2009), and Phan et al. (2009, 2010).
Fu et al. (2010a, 2010b), Timmons and Scanlon (2009), and Kim (2007).
Tindell et al. (1996), Pedro and Burns (1998), and Real and Crespo (2004).
Multi-Modal Systems
Control Systems
Dedicated Platform
High Computation Time
Soft real-time systems.
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Models: Sporadic Task
Sporadic Task
)(id
)(ie
)(ip
Execution
Period
Deadline
)()(
)()( )1,0max(),( i
i
ii e
p
dttdbf
DBF
t
Execution
RBF
)()(
)( ),( ii
i ep
ttrbf
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Models: Periodic Resources
Capacity
Period-of-repetition
)(iPeriodic Resource (i)
Supply Bound
Function (SBF)
t
Supply )(i
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Mode Definition
M(i)
),,(
}{
),(
)()()()(
1)()(
)()()(
iiii
nii
iii
pde
i
)(i
)(i
Hardware
Software
)(id
)(ie
)(ip
MMS Protocol for Mode-Change
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Mode Change
tk
M(i) M(j) M(k)
ij jk
)()( jjN
mcrk=(M(j), M(k),tk)
ModeChangeReques
t
ModeChangeRequest
Transition
time
time
Transition
time
Old Mode New
Mode
New Mode
Old Mode
mcrk-1=(M(i), M(j),tk-1)
tk-1
MMS Protocol for Mode-Change
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Different Tasks
tk-1 +
tkM(j) M(k)
ij
jk
Immediately Aborted Tasks A(ij)
Non-Aborted Tasks
Unchanged Tasks τ(ij)
X
X
MMS Protocol for Mode-Change
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Schedulability Analysis
Multi-modal FP-schedulability requires high computation time.
No support for non-preemptive execution.
Schedulability Analysis
Set of real-timemodes
Yes: All deadlines are met.
No: There could be a deadline miss.
Pseudo-polynomial-time FP Schedulability Analysis
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Problem Definition
Check FP-schedulability for any legal sequence of job arrivals and mode-change requests.
Given M1, M2, … … Mq, resources Ωij, transition duration δij, unchanged tasks τij, aborted tasks Aij :
Pseudo-polynomial-time FP Schedulability Analysis
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Schedulability Conditions
t
Execution
Request Bound
Function (RBF)
Supply Bound
Function (SBF)
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Schedulability Conditions
Mjijkt
Condition “SC5”
Condition “SC1”
Condition “SC2”
Condition “SC3”
Condition “SC4”
kt
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FP Schedulability ConditionsPseudo-polynomial-time FP Schedulability Analysis
No existing MMS supports non-preemptible execution
Non-preemptive and iterative FB-Schedulability
Goal
1. Find Largest Busy-Intervals (Davis et al.2007) for any task
2. Response time considering multiple jobs in the busy-interal
3. Evaluate vulnerable jobs in all busy interval
tk
All Higher Priority Tasks
Supply from Periodic
Resource
Initial Condition
All Higher Priority Tasks
Response
Time
Blocking
Factor
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FP Schedulability ConditionsPseudo-polynomial-time FP Schedulability Analysis
tk
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ijkt
x
t
)( jd
a b
Carry-In
New mode tasks
Supply
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Radar SimulationPerformance Evaluation
No loss of performan
ce Reclaimed cpu cycles could be used with low
criticality tasks.Figure Courtesy: Mathworks
FMCW Radar77 GHz
MATLAB Phased Array Toolbox
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Simulation Settings
Environment MATLAB
Unchanged Tasks Tasks 5
Aborted Tasks Task 1
Resource
Tasks Set
)()()( ,10 iii
Compared against state-of-the-art algorithm by Phan et al. (2010).
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Performance Evaluation
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Simulation Results
Phan approach:Schedulability
using reachability (SURG)
Our approach: Schedulability using Bounded
Iteration
SUBI requires 2
sec to finish
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Performance Evaluation
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Conclusion
Problem: Existing MMS cannot exploit features of a
control system. Goal:
Develop a multi-mode systems for a shared platform
Non-preemptive executions with FP Contributions:
Designed a protocol, developed schedulability analysis, and determined parameters of a MMS.
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Future Work
MMS Upon Multi-Core MMS Protocol to Exploit Multi-Core Schedulability Resource Allocation Thermal-Resilient Multicore Systems
Mixed Criticality Scheduling Exploit MMS Carry-In Concepts Exploit MMS Resource Allocation
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Multi-Modal Systems
Questions?