Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems
Abstract
:1. Introduction
- Presenting a novel adaptive technique with high QoS to schedule MC tasks at run-time.
- Proposing a learning-based drop-aware MC task scheduling mechanism, called SOLID, to improve the QoS by exploiting the generated dynamic slacks rigorously, during run-time with no HC tasks’ deadline misses.
- Extending the proposed mechanism (SOLID) to a mechanism that uses accumulated dynamic slack moderately, called LIQUID.
2. Related Works
3. System Model
4. Motivational Example and Problem Statement
5. Proposed Method in Detail
5.1. An Overview of the Design-Time Approach
5.2. Run-Time Approach: Employment of SOLID
5.2.1. Learning-Based System Properties Optimization
5.2.2. SOLID Optimization
5.3. LIQUID Approach
Algorithm 1 Proposed Learning-Based Scheme at Run-Time |
Input:Task Set, Cores, Q-table |
Output:QoS, Scheduled Tasks |
1: procedure LEARNING-BASED QoS OPTIMIZATION() |
2: for each cr in Cores do = 0; |
3: for t = 1 to Time do |
4: [,] = TaskStatusCheck(Tasks) |
5: [] = EDF-VD (, Cores) |
6: if = = 1 then |
7: for each released LC do |
8: + = 1; |
9: if mod(,) = = 0 then + = 1; |
10: end if |
11: end for |
12: else |
13: for each do = 0; |
14: end for |
15: end if |
16: = TaskOutputCheck(Tasks) |
17: if = = 1 then = |
18: end if |
19: if mod(t,) = = 0 then |
20: State = Deter-State (,) |
21: k = rand (1); //(0<k<1) //ϵ-Greedy Policy |
22: if then = argrand |
23: else = argmax |
24: end if |
25: Set the new task’s drop-rate based on the action |
26: R = CompReward () // Equation (5) |
27: //Equation (4) |
28: = 0; = 0; |
29: end if |
30: end for |
31: end for |
32: end procedure |
6. System Setup and Evaluation
6.1. Evaluation with Real-Life Benchmarks
6.2. Evaluation with Synthetic Task Sets
6.2.1. Effects of System Utilization
6.2.2. Effects of HC Tasks’ Run-Time Behaviour
6.2.3. Impacts of Task Mixtures
6.2.4. Investigating the LC Tasks’ Drop-Rate Parameter
6.3. Investigating the Timing and Memory Overheads of ML Technique
7. Conclusions and Future Studies
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
BF | Best-Fit |
DC | Decreasing Criticality |
DU | Decreasing Utilization |
EDF | Earliest-Deadline-First |
EDF-VD | EDF with Virtual Deadline |
FF | First-Fit |
HC | High-Criticality |
HI mode | HIgh-criticality mode |
LC | Low-Criticality |
LCM | Least Common Multiple |
LO mode | LOw-criticality mode |
MC | Mixed-Criticality |
ML | Machine Learning |
NDM | Number of Deadline Misses |
QoS | Quality-of-Service |
RL | Reinforcement Learning |
UAV | Unmanned Aerial Vehicles |
WCET | Worst Case Execution Time |
WF | Worst-Fit |
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# | S/M-Core | MC Tasks | Run-/Design-Time | QoS Opt. | Use of ML | |
---|---|---|---|---|---|---|
1 | Gettings’15 [11], Ranjbar’20a [7], Liu’18 [9] Guo’18 [5], Ranjbar’21 [13] | S-Core | ✓ | Design-Time | offline | None |
2 | Ramanathan’18 [14], Pathan’17 [15], Pathan’18 [16] | M-Core | ✓ | Design-Time | offline | None |
3 | Sigrist’15 [17] | M-Core | ✓ | Run-Time | None | None |
4 | Huang’19 [8], Lee’17 [4], Li’14 [12], Hu’16 [18], Bate’15 [19] | S/M-Core | ✓ | Run-Time | online | None |
5 | Li’09 [20], Eom’13 [21], Horstmann’19 [22] | M-Core | × | Run-Time | None | ✓ |
6 | Proposed Work | M-Core | ✓ | Run-Time | offline & online | ✓ |
Symbol | Description | Symbol | Description |
---|---|---|---|
Criticality level of task | Skip parameter of task | ||
Optimistic WCET of task | Pessimistic WCET of task | ||
Actual deadline of task | Virtual deadline of task | ||
Period of task | Hyper-period of task set | ||
A released job (j) of task | Finish Time of task | ||
Number of all LC tasks | Number of executed LC tasks |
Task Function | |||||||
---|---|---|---|---|---|---|---|
engine control | HI | 2 | 7 | 24 | 11 | ∞ | |
collision avoidance | HI | 2 | 4 | 48 | 22 | ∞ | |
video capturing and transferring | LO | 2 | 2 | 8 | - | 3 | |
sensor data recording | LO | 2 | 2 | 6 | - | 4 | |
navigation | HI | 0.8 | 1 | 12 | 6 | ∞ |
Metrics | LIQUID | Gettings’15 [11], Ranjbar’20 [7] | Liu’18 [9] | Li’14 [12] | Huang’19 [8] |
---|---|---|---|---|---|
NDM | 0.009 | 0.010 | 1 | 0.341 | 0.999 |
QoS | 99.67% | 96.92% | 64.33% | 87.83% | 64.36% |
LIQUID | SOLID | Gettings’15 [11], Ranjbar’20 [7] | Liu’18 [9] | Li’14 [12] | Huang’19 [8] | |
---|---|---|---|---|---|---|
= [0.2,0.4] | 0.38, 0.82 | 0.42, 0.83 | 0.46, 0.84 | 1.00, 1.00 | 0.70, 0.99 | 1.00, 0.95 |
= [0.4,0.6] | 0.36, 0.80 | 0.41, 0.81 | 0.47, 0.83 | 0.96, 0.99 | 0.71, 0.97 | 0.96, 0.94 |
= [0.6,0.8] | 0.29, 0.78 | 0.34, 0.79 | 0.38, 0.80 | 0.80, 0.94 | 0.58, 0.93 | 0.80, 0.90 |
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Ranjbar, B.; Alikhani, H.; Safaei, B.; Ejlali, A.; Kumar, A. Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems. Computers 2022, 11, 101. https://doi.org/10.3390/computers11070101
Ranjbar B, Alikhani H, Safaei B, Ejlali A, Kumar A. Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems. Computers. 2022; 11(7):101. https://doi.org/10.3390/computers11070101
Chicago/Turabian StyleRanjbar, Behnaz, Hamidreza Alikhani, Bardia Safaei, Alireza Ejlali, and Akash Kumar. 2022. "Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems" Computers 11, no. 7: 101. https://doi.org/10.3390/computers11070101