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Task Mapping and Bandwidth Reservation for Mixed Hard/Soft Fault-Tolerant Embedded Systems

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Task Mapping and Bandwidth Reservation for Mixed Hard/Soft Fault-Tolerant Embedded Systems Prabhat Kumar Saraswat Paul Pop Jan Madsen 16th IEEE Real-Time and Embedded ... – PowerPoint PPT presentation

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Title: Task Mapping and Bandwidth Reservation for Mixed Hard/Soft Fault-Tolerant Embedded Systems


1
Task Mapping and Bandwidth Reservation for Mixed
Hard/Soft Fault-Tolerant Embedded Systems
  • Prabhat Kumar Saraswat
  • Paul Pop
  • Jan Madsen

16th IEEE Real-Time and Embedded Technology and
Applications Symposium April 14, 2010,
Stockholm, Sweden
2
Introduction
  • Trend Integration of different applications on
    the same platform
  • Critical (e.g., ABS)
  • Deadline miss catastrophe
  • Based on worst-case assumptions
  • Best effort (e.g., multimedia)
  • Deadline miss performance degradation
  • Variability in execution times
  • Worst-case leads to overdesign
  • Bridging the gap partitioned architectures
  • Fault tolerance

3
Problem Description
Utilization?
?
?
?
?
?
k transient faults
Soft Task
Hard Task
Application
Platform
  • Given A mixed hard/soft fault-tolerant
    application and a distributed platform
  • Determine Mapping and Utilization
  • Such that
  • Deadlines for all hard real-time tasks are
    satisfied (Even in case of faults)
  • Probability of meeting of deadline for soft tasks
    is maximized

4
Application Model
  • Set of tasks
  • Mixed task set Hard and Soft tasks
  • All tasks are periodic
  • Tasks can tolerate transient or no faults

5
Constant Bandwidth Server
  • Temporal partitioning of hard/soft tasks.
  • Each soft task is assigned a CBS with parameters
  • Qi maximum server budget (bandwidth)
  • Ti server period (period of the soft task)
  • A soft task is allowed to execute for only Qi
    units of time every period Ti
  • Hard tasks and CBS servers execute under EDF
  • Probability of meeting the deadline (QoS) depends
    on Qi

Soft
Hard
Processor
Util.
6
CBS Example Abeni 98
Hard WCET2 Period3
Soft task
CBS Bandwidth 2 Period 7
18
20
2
4
10
12
22
6
8
14
16
7
Platform Model
Fault model
  • Equidistant checkpointing with rollback recovery
  • Execution of task is divided into segments
  • After each segment checkpoints (state of a task)
    are stored in a stable storage
  • In case of fault, the state is restored from the
    stored checkpoint

Without checkpointing
8
Schedulability analysis
  • Utilization based test is used to check if the
    task set mapped on a processor is schedulable
  • Sum of utilizations of the following
  • Hard tasks
  • Considering checkpointing overheads
  • Soft tasks
  • CBS parameters Server budget and the period
  • Recovery Utilization
  • Utilization needed to recover the hard tasks
    incase of faults considering worst case scenario

9
Stochastic Analysis
Qi
Probability
Vk max0,Vk-1 Qi cj
Execution time
  • CBS server is modeled as a queue
  • A request of Ck units arrives every Ti units of
    time.
  • At most Qi units can be served every Ti units
  • The probability that a job Jk finishes before its
    deadline is related to Vk
  • Vk (the length of queue at kTi) is a Markov Chain
    describing the system
  • A stationary solution for the state probability
    vector of Vk is calculated
  • QoS is calculated from this stationary solution

10
Bandwidth Allocation using PDFs
0.6
Util. 1
0.4
  • Naive approach
  • Allocate Q proportional to their AETs
  • For ?1
  • Util 11/(1117) x 0.6
  • For ?2
  • Util 17/(1117) x 0.6

Initial
AET
Spare Utilization
Utilization for Hard tasks
period 100
(60)
(80)
(40)
(11)
(17)
Using PDFs better design decisions can be taken
11
Mapping Example
N1
N2
?1
?5
?6
?3
9 55
19 55
Task AET N1 N2 AET N1 N2
?1 5 8
?2 10 12
?3 14 15
?4 17 20
P5
P5
23 45
10 65
?2
?4
15 55
24 55
?i
Q Period
QoS 72.10
?i
Optimal solution using AETs
WCET Period
N1
N2
Task WCET N1 N2 WCET N1 N2
?5 23 25
?6 8 10
?1
?5
?2
?6
6 55
17 55
P5
P5
?4
?3
10 65
23 45
17 55
26 55
QoS 94.22
Optimal solution using PDFs
12
Tabu Search Mapping and Bandwidth Allocation
(TSMBA)
  • Iterative exploration of design space
  • Use of Tabu List to avoid revisiting of already
    explored solutions
  • TSMBA
  • Takes as input, the application and the
    architecture model
  • An initial solution, can be unschedulable
  • Produces a solution containing
  • Mapping for all tasks
  • Set of bandwidth values for all soft tasks
  • Solutions are evaluated on the basis of this Cost
    function
  • Minimize cost function Schedulable solutions
    and Maximized QoS

13
Tabu Search - Moves
N1
N2
t1 6 t2 11 t3 16 t4 21 QoS 63
t1
t3
t4
t6
t2
t5
mapping move
bandwidth move
N1
N2
N1
N2
t1 6 t2 11 t3 16 t4 21 QoS 48
t1
t3
t1
t1 6 t2 15 t3 16 t4 21 QoS 71
t3
t1
t2
t5
t4
t6
t2
t5
t4
t6
  • Diversification move mapping and bandwidth for
    all tasks are changed

14
Experimental Setup
  • Proposed optimizing strategy (TSMBA) vs
    straightforward (SF) strategy
  • SF strategy
  • Used when only AETs are available, not PDFs
  • Maximizing the difference between allocated Q
    value and AET for all soft tasks
  • Cost Function ?avg / ?dev
  • Generated synthetic benchmarks
  • PDFs to match the shape of real-life benchmarks
  • Messages (bus utilization should not be greater
    than 1, non preemptive EDF)
  • Assume that all half of the hard tasks are safety
    critical

Allocated Q
AET
WCET
?avg
15
Experimental results
Synthetic benchmarks
  • QoS resulted by TSMBA is better than SF on an
    average of 29.60
  • TSMBA finds schedulable solutions much earlier
    than SF approach

16
Experimental results
Real-life benchmarks
  • QoS resulted by TSMBA is better than SF on an
    average of 28.04

17
Conclusions
  • A Tabu Search based heuristic is proposed to
    perform design optimizations
  • Results in implementation where deadlines of hard
    tasks are satisfied (even in case of faults) and
    QoS for soft tasks is maximized
  • Better design choices can be made by taking
    stochastic execution times of soft tasks into
    consideration.

18
Thanks Questions?
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