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Tag lists for SBMeS

Adaptive,adaptive TT,Adaptivity,algorithm,Algorithm,AM,Application model,automotive,automotive functions,CM,Collision,
complex integrated systems,
computer science,
Context model,
critical events,
Decision Variables,  dependability,
Embedded Real-Time Systems,
embedded systems,
energy consumption,
energy efficiency,  energy saving,
energy-aware,  energy-efficiency,  Energy-efficiency,
execution time,
fault events,  flexibility,
frequency scaling,
Gannt mapping,
global optimum,
Graph mapping,
mathematical programming,
Message Deadlines,
Message Duration,
Mixed-Integer Quadratic Programming,
mobile phones,
Motivation, MPSoC,
multi-core architectures,
neighborhood search,
Neighborhood search,
objective function,
Physical model,
quasi-static scheduling,
Real-time Systems,
safety-critical systems,
Slow Down Factors,
Task Procrastination,
Time-Triggered Embedded Systems,  tree-based graph,
TT systems,



Event tree

List of Figures

Figure 1. Conceptual difference [23] 
Figure 2. Current and future trend in an embedded system [23] ….
Figure 3. Kalray’s MPPA network-on-chip (The MPPA2®−256 Bostan2 processor [52]) 
Figure 4. Simplified design flow and meta-scheduler (MeS) integration in SAFEPOWER [3]Figure 5. A search tree for time slot [17] 
Figure 6. Conceptual physical model (PM) 
Figure 7. General physical model (PM) schema 
Figure 8. General application model (AM) schema 
Figure 9. General context model (CM) schema 
Figure 10. Meta-scheduler (MeS) data structure schema model 
Figure 11. The basic model of meta-scheduling (MeS) 
Figure 12. Conceptual AM
Figure 13. Quadratization technique via hops .
Figure 14.The effect of TSDF on tasks et(t) and core fault 
Figure 15. Three-step scheduling for finding optimum solutions 
Figure 16. a. Stack slack (SS) and b. Dynamic slack (DS) sample 1
Figure 17. Usage of dynamic slack (DS) to reduce makespan

Figure 18. Usage of SDF regarding dynamic slack (DS) 
Figure 19. Usage of scenario-based meta-scheduling (SBMeS) on system-level design for SAFEPOWER multi-processor system-on-a-chip (MPSoC) [3] 
Figure 20. Depth-first algorithm establishing schedule backwards with tabu-set for re-convergence (FAESB-TSR) 
Figure 21. Conceptual of the meta-scheduler (MeS) tool 
Figure 22. Scenario-based meta-scheduling (SBMeS) general model 
Figure 23. States of 3 events and their effect of each SM 
Figure 24. Events’ effects in task scheduling 
Figure 25. Schedule Gantt map 
Figure 26. Schedule tree include all data 
Figure 27. Meta-visualization of an event 
Figure 28. Events state in s schedule tree 
Figure 29. Schedules share points regarding task changes and Figure 28 
Figure 30. Decoding schedules from delta tree (DT) 
Figure 31. Delta tree (DT) data model 
Figure 32. Schema technique for standard data structure modelling 
Figure 33. Overview of scheduling models 
Figure 34. The simple architecture of meta-scheduling (MeS) 
Figure 35. Standardized input XML sample 
Figure 36. Example a textual data with three nodes 
Figure 37. Static slack (SS) schedule model (SM) generated by meta-scheduling visualization tool (MeSViz) 
Figure 38. Schedule tree with 94 schedules (created via meta-scheduling visualization tool (MeSViz) and GVEdit) 
Figure 39. Gantt map of schedule ID 44 
Figure 40. Incorrect results and information found in the complex schedule 
Figure 41. Meta-schedule Gantt map generated from meta-scheduling (MeS) class
Figure 42. Schedule SM0 with static slack (SS) 
Figure 43. Schedule SM1 with dynamic slack (DS) 
Figure 44. Comparing two schedules SM0 (Figure 42) and SM1 (Figure 43) after slack 
Figure 45. Graph output of node dependency 
Figure 46. Few changes from ID2 to ID3
Figure 47. Minimum changes from SM37 to SM38 regarding T3 slack 
Figure 48. Meta-visualization for Example 3 (schedules SM3 & SM4) 
Figure 49. Comparison of three different scenarios for memory saving with delta scheduling technique (DTS) 
Figure 50. The physical model (PM) of case study 
Figure 51. The application model (AM) of case study 
Figure 52. The application model (AM) of the case study [6] 
Figure 53. The physical model (PM) of the case study 7.3.2 
Figure 54. Energy consumption results for cores and routers FECsm ,FERsm , FEC,avg,dyn ,FER,avg,dyn
Figure 55. FEsm schedule models( SMs) results and average FEavg,dyn 
Figure 56. Total energy reduction results for coresReFEC(SM) and routers ReFER(SM) and averageFEC,avg,dyn, FER,avg,dyn 
Figure 57.Total FE reduction results for schedules ReEsm and average ReFE 
Figure 58. The application model (AM) of the case study 
Figure 59. The physical model (PM) of the case study 
Figure 60. FECsm,FERsm results for cores and routers and average FEC,avg,dyn, FER,avg,dyn 
Figure 61. FE(sm) results and average FEavg,dyn 
Figure 62. FE results for cores ReFEC(SM) and routers ReFER(SM) compare to SM0 and average FEC,avg,dyn, FER,avg,dyn 
Figure 63. Total ReFE(sm) in each schedule compare to SM0 and average FEavg,dyn 
Figure 64. The physical model (PM) of the case study 
Figure 65. The application model (AM) of case study 
Figure 66. All nodes in a sample schedules tree 
Figure 67. Real nodes in a sample schedules tree 
Figure 68. Combination dynamic slack (DS) and core fault results in SM101 which generated by meta-scheduling visualization tool (MeSViz) 
Figure 69. The total number of generated schedules Nsm for each scenario 
Figure 70.Time of computation for scenarios 
Figure 71. FE results for each scenario SSMx 
Figure 72. Total ReFE(SMM) (percentage) results for each scenario of comparing dynamic schedules with a static schedule SMx 

Meta-schedule Gannt map

List of Tables

Table 1. An overview of related works compared to scenario-based meta-scheduling (SBMeS) 
Table 2. Overview of existing real-time scheduling tools [7] 
Table 3. Overview of input table [90] 
Table 4. Sample data calculated for Figure 14 
Table 5. Sample raw input data before forming in XML format 
Table 6. Difference between the designed scenarios 
Table 7. Results of dynamic slack (DS) schedules 
Table 8. Memory consumption and saving via delta scheduling technique (DST)
Table 9. Sample results of Example 2
Table 10. Results of delta scheduling technique (DST) memory saving for Example 2 
Table 11. Sample results for Example 3 
Table 12. Results of delta scheduling technique (DST) memory saving for Example 3 
Table 13. Meta-scheduling (MeS) input constant 
Table 14. Results of delta scheduling technique (DST) memory saving 
Table 15. Some collected results for model example 
Table 16. Energy results for example 
Table 17. Meta-scheduling (MeS) input constant devices [13] 
Table 18. Energy results for Example 7.3.2 
Table 19. General results for a model example 
Table 20. Results of delta scheduling technique (DST) memory saving 
Table 21. Meta-scheduling (MeS) input constant 
Table 22. General results of for the example model 
Table 23. Results of delta scheduling technique (DST) memory saving 
Table 24. The context model (CM) for application model (AM) and physical model (PM) scenarios 
Table 25. Meta-scheduling (MeS) input constant 
Table 26. Output results
Table 27. Redundancy path routing effect 

List of Algorithms

Algorithm 1. Main function algorithm
Algorithm 2. Fault recovery and scheduling technique
Algorithm 3. DTS discover and calculating changes for messages (a) and tasks (b) ….. 83
Algorithm 4. XML parser
Algorithm 5. Slope Evaluation 1
Algorithm 6. Slope Evaluation 2


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