1. Quasi-static fault-tolerant scheduling schemes for
energy-efficient hard real-time systems
• Wei Tongquan, CS Department of East China Normal University, China
• Piyush Mishra, GE Global Research, Niskayuna, NY 12309, USA
• Kaijie Wu, ECE Department of University of Illinois, Chicago, IL 60607, USA
• Junlong Zhou, CS Department of East China Normal University, China
Journal of Systems and Software
2012
Reza Ramezani
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2. A Unified Approach for Fault Tolerance and Dynamic
Power Management in Fixed-Priority Real-Time
Embedded Systems
• Ying Zhang
– a Senior Software engineer with the Research and Development
Department, Guidant Corporation, St. Paul, MN, USA
• Krishnendu Chakrabarty
– Department of Electrical and Computer Engineering, Duke University,
Durham, USA
Computer-Aided Design of Integrated Circuits and Systems,
IEEE Transactions on 25, no. 1 (2006): 111-125.
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3. Overview
 Primaries
 Checkpointing & Response Time
 Reliability, The best fault tolerance count?
 Feasibility Analysis
 Offline Application Level Voltage Scaling
 Offline Task Level Voltage Scaling
 Online DVS by Using Slacks
 Previous Work (Ying Zhang, Krishnendu Chakrabarty, 2006)
 Results
 Suggestion
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4. Primaries
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5. Features
• Fault Tolerance Scheduling
 Transient Faults
 Fast Detection
 Fault occurrences at runtime, checkpointing and state restoration.
• Dynamic Voltage Scaling (DVS)
• Offline Scheduling
 Application Level Voltage Scaling (A-DVS)
 Task Level Voltage Scaling (T-DVS)
• Online Scheduling
 Using Slacks
• Exact Rate-Monotonic Characterization
 Instead of iteratively deriving the response time of each task for
feasibility analysis. 5

6. Online DVS Outline
• The adaptation of the offline task schedules to the
runtime behavior of fault occurrences is implemented:
 (1) Pre-computing and saving in a lookup table the maximum slack
requirements for the processor to dynamically slow down.
 (2) Retrieving and comparing the stored slack time requirements with
the generated cumulative slack in the runtime.
 (3) Dynamically scaling down processor speed when the generated
slack time is equal to or greater than the stored slack requirements.
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7. System Architecture
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8. System Architecture (2)
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9. Checkpointing
&
Response Time
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10. Checkpoint count
 Fault-tolerant computing refers to the correct execution of user
programs and system software in the presence of faults.
 Fault tolerance is typically achieved in real-time systems through
online fault detection, checkpointing, and rollback recovery .
 Checkpointing increases the task execution time, and in the absence
of faults, it might cause a missed deadline for a task that completes
on time without checkpointing.
 Frequent checkpointing reduces re-execution time due to faults but
increases task execution time and vice versa.
 Therefore, the checkpointing interval, i.e., the duration between two
consecutive checkpoints, must be carefully chosen to balance
checkpointing cost with the re-execution time.
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11. Fault occurrences count
• Relation between fault occurrences count and fault
arrival rate
 k is the fault occurrences count to be tolerated.
 a fault arrival rate λ and a task execution interval t, the mean number
of faults that arrive during the interval is λt.
o If k is much smaller than λt, a sophisticated fault-tolerant scheme with its
associated overhead is not appropriate.
o if k is much larger than λt, a fault-tolerant scheme that provides deterministic
real-time guarantee may not exist.
 In order to target a system with reasonable real-time performance with
fault tolerance, the value of k can be taken to be a small multiple of λt,
e.g., 2λt ≤ k ≤ 3λt.
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12. Checkpointing
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13. Fault placement
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14. Fault placement
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15. Task response time
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16. Task response time
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17. Reliability
The best fault tolerance count?
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18. Reliability
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19. Task Reliability
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20. Task Reliability (2)
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21. Task Reliability (3)
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22. Feasibility Analysis
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23. Exact Characterization of RMA (ECRMA)
• Critical Instant
 The worst case behavior of RMA occurs when all tasks in a task set are
instantiated simultaneously and are ready for execution immediately after
initiation.
 It has been shown that a schedule of independent periodic tasks is
feasible if the first instance of each task is schedulable when it is
instantiated at a critical instant Lehoczky et al. (1989) .
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24. Exact Characterization of RMA (ECRMA) (2)
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25. Exact Characterization of RMA (ECRMA) (3)
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26. Offline Application Level Voltage Scaling
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27. Application level voltage scaling (A-DVS)
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28. A-DVS algorithm
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29. A-DVS algorithm (2)
• Some Considerations
 The binary search based A-DVS algorithm is valid only if the energy
consumption is monotonic with respect to frequency/voltage changes.
 When the processor static power consumption as well as context
switching overhead is considered, the monotonicity does not hold.
 In this case, there exists a critical processor speed below which scaling
down the processor speed will instead increase the energy consumption.
 The minimum voltage level low is initialized to the level corresponding
to the processor critical speed.
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30. Feasibility Checking Algorithm (FCA)
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31. 31
Feasibility
Checking
Algorithm
(FCA)

32. Offline Task Level Voltage Scaling
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33. Task level voltage scaling (T-DVS)
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34. T-DVS algorithm
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35. T-DVS algorithm (2)
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36. T-DVS algorithm (3)
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37. T-DVS Consideration
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38. Schedulability Checking Algorithm (SCA)
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39. Online DVS by Using Slacks
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40. Online reevaluation of DVS policies
 Offline scheduling assumes that all tasks exhibit the worst case execution
time and all faults occur during the checkpointing.
 The runtime behavior of task execution and fault occurrences can vary
significantly.
 In the runtime, not all tasks execute up to their worst case execution times
and not all faults occur during task executions.
 Hence, the slack generated in the runtime could be used to dynamically scale
down the processor speed to save energy.
 The online reevaluation of DVS policies can save significant energy by using
generated slacks due to uncertainties in fault occurrence.
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41. Reevaluation of DVS at application level
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42. Reevaluation of DVS at application level (2)
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43. Reevaluation of DVS at application level (3)
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44. 44

45. 45

46. Dynamic ADVS Algorithm
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47. Dynamic ADVS Algorithm (2)
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48. Example
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49. Reevaluation of DVS policies at task level
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50. Reevaluation of T-DVS (D-TDVS)
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51. Previous Work (Ying Zhang, Krishnendu Chakrabarty, 2006)
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52. Feasibility Analysis
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53. Feasibility of a task set under fault-free conditions
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Fault Free

54. Tolerating k Faults in Each Task
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55. Fault Tolerance With DVS
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56. Fault Tolerance With DVS (2)
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57. Fault Tolerance With DVS (3)
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58. Heuristic Method Based on GA
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59. Heuristic Method Based on GA (2)
• Init function
 Initializes the search space (chromosome population).
 One chromosome is initially generated using the computationally
feasible application-level speed scaling method.
 The other chromosomes are generated randomly.
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60. Heuristic Method Based on GA
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61. Results
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62. Experiments
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63. Processors
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64. Task Sets
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65. Application level results on Tranmeta Crusoe
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66. Task level results on Tranmeta Crusoe
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67. Application level results on Intel XScale
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68. Task level results on Intel XScale
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69. Real life implementation
 The energy consumptions of the system board ,excludes the processor time.
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70. Suggestion
 The scheduler can tolerate at least k faults and then tries to DVS by using
slacks.
 Tolerating more faults than k by increasing processor speed when more
faults than k occur.
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