Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System †
Abstract
:1. Introduction
2. Related Work
2.1. Scheduling Algorithm
2.2. Multi-Task Learning
2.3. Previous Research
3. Modeling the Task Scheduling Switcher
3.1. Task Scheduling Switcher Problem
3.2. Multi-Task Learning Model
3.3. Scheduling Framework Implementation
4. Evaluation
4.1. Hardware Specification
4.2. Classification Accuracy
4.2.1. Dataset Creation
Algorithm 1 Creating a dataset for training the MTL model. |
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4.2.2. Post-Training Log
4.2.3. Cross-Validation Results
4.3. Benchmarking the Scheduling Framework
4.3.1. Benchmark Configuration
4.3.2. Benchmark Result
5. Validation
5.1. Simulating the Resource-Constrained Smart Office
5.2. Simulation Configuration
5.3. Simulation Result
5.3.1. Sampled Scheduling Algorithm Switching
5.3.2. Deadline Accuracy and Optimization
5.4. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
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Scheduling Algorithm | Computation Time When Compared to Octasort and Fit | Task Execution Optimization When Compared to First Come First Serve |
---|---|---|
First Come First Serve | 91.76% | 0.00% |
Shortest Job First | 95.67% | 8.29% |
Sort and Fit | 50.87% | 40.76% |
Octasort and Fit | 0.00% | 42.56% |
Scheduling Algorithm | Computation Time | Task Execution Optimization | Flexible Optimization |
---|---|---|---|
Sort and Fit | ✓ | ✓ | |
Online Scheduling | ✓ | ||
Federated Scheduling | ✓ | ||
Deep Reinforcement Learning | ✓ | ✓ | |
Task Scheduling Switcher | ✓ | ✓ | ✓ |
Training Parameters | Value |
---|---|
Dataset size | 100,000 |
Epochs | 3000 |
Batch size | 1000 |
Training/Test | 7:3 |
Type | OS | CPU | GPU | RAM |
---|---|---|---|---|
Specification | Windows 10 Pro 10.0.19042 Build 19042 | Intel i7-7700HQ 4 Cores (8 threads) 2.807GHz | GeForce GTX 1050 Ti Mobile variant | 12 GB |
Randomization Ranges | Values |
---|---|
Number of tasks | 3∼100 |
Number of resources | 1∼10 |
Resource constraint | 10∼100 |
Type of Device | Number of Unit | Resource Taken by One Unit | ||
---|---|---|---|---|
Electric [W] | Local Area Network [MB/s] | Wide Area Network [MB/s] | ||
Air conditioner | 10 | 400 | 12 | 12 |
Temperature sensor | 10 | 15 | 4 | 6 |
Motion sensor | 8 | 20 | 6 | 8 |
Smoke sensor | 13 | 16 | 10 | 15 |
Security door | 8 | 100 | 3 | 5 |
Security camera | 6 | 100 | 10 | 12 |
Lighting_0 | 40 | 20 | 1 | 1 |
Lighting_1 | 5 | 10 | 1 | 1 |
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Bin Kamilin, M.H.; Bin Ahmadon, M.A.; Yamaguchi, S. Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System. Information 2021, 12, 150. https://doi.org/10.3390/info12040150
Bin Kamilin MH, Bin Ahmadon MA, Yamaguchi S. Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System. Information. 2021; 12(4):150. https://doi.org/10.3390/info12040150
Chicago/Turabian StyleBin Kamilin, Mohd Hafizuddin, Mohd Anuaruddin Bin Ahmadon, and Shingo Yamaguchi. 2021. "Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System" Information 12, no. 4: 150. https://doi.org/10.3390/info12040150