A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms
Abstract
:1. Introduction
- We present a new architecture for driver-profiling with deep learning techniques in cars with embedded system.
- We achieve a greater number of responses from a driver profiling service.
- We successfully re-implement all the algorithms in an edge server environment.
- We conduct extensive experiments to confirm the advantages of our approach.
2. Related Work
2.1. Existing Driver-Profiling Deep Learning Models
2.2. Applied Embedded Deep Learning Platforms
Algorithm 1: The traditional EDPA [6,8,11] |
|
3. Edge-Based Data Scheduling for FCN-LSTM Driver Profiling
Algorithm 2: The proposed EDPA |
|
In-Memory Data Scheduler
Algorithm 3: Data scheduling in Insert_cache function |
|
4. Experimental Results
4.1. Data Sources
4.2. Hardware Settings
4.3. Evaluation of Driver Profiling
- In the traditional EDPA, EDPA-Client( ) and EDPA-Server( ) are located in different containers inside each Embedded Nano board, which clearly shows they have a 1:1 relationship. Therefore, in the traditional EDPA, Algorithm 1 does not have a loop structure in client/server, but Algorithm 2 does. In the proposed EDPA, Algorithm 2, EDPA-Server( ) uses Node.js applications which employ multiple workers in parallel which clearly show they are having 1:4 relationship. Besides, we can scale the number of Node.js applications using the load balancer. In the proposed EDPA-server function, initialization latency , memory allocation latency , and trained model loading latency are excluded from end-to-end latency , which results in improving and reducing the average of the EDPA system.
- The function driver_profiling employs the light-weight FCN-LSTM, which execute five requests per second.
- The proposed EDPA-server function connects four embedded cars via the in-memory scheduler, and at the same time uses four Node.js workers.
- The proposed EDPA-server function operates four Node.js workers in parallel.
- Each proposed EDPA-client activates a Nodejs worker using the check_server REST API.
- Each in-memory scheduler thread allocates key, value, and meta information related to each data sensor file using a linked-list structure in parallel.
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Symbol | Representation | Applied Function |
---|---|---|
Initialization latency | EDPA-client( ) | |
Read latency from sensors | EDPA-client( ) | |
Visualization latency | EDPA-client( ) | |
Insert data to cache | EDPA-client( ) | |
Delay | EDPA-client( ) | |
Initialization latency | EDPA-server( ) | |
Memory allocation latency | EDPA-server( ) | |
Trained model loading latency | EDPA-server( ) | |
Driver profiling latency | EDPA-server( ) | |
Latency when requesting a job | EDPA-server( ) | |
Latency when updating prediction | EDPA-server( ) | |
Total classification latency | EDPA-client( ) | |
End-to-end latency | Embedded system |
EDPA Reference | DA | DST | SAT | RHL | SL | EEL |
---|---|---|---|---|---|---|
Algorithm 1 [6] | DeepConvRNN-Attention | - | Sequential processing | Low | Low | Low |
Algorithm 2 [8] | FCN-LSTM | - | Sequential processing | Low | Low | Low |
Algorithm 3 [11] | light-weight FCN-LSTM | - | Sequential processing | Low | Low | Medium |
This work | FCN-LSTM, DeepConvRNN-Attention, light-weight FCN-LSTM | Yes | Distributed-Parallel processing | High | High | High |
Domain | Platform | Hardware Specifications | Implementation Details |
---|---|---|---|
Edge Server | Desktop | CPU: Intel Core.i7, RAM: 4 GB, NVME: 128 GB | An edge-Server for four clients |
Embedded system | Jetson Nano | CPU: ARM Cortex-A57, RAM: 4 GB, HDD: 128 GB | A local server for a client * |
Input | Algorithm | Accuracy (%) | FLOPs * | Memory | Feature Engineering | Windowing |
---|---|---|---|---|---|---|
60 × 45 | Algo1 in the proposed EDPA | 97.72 | 1.524 M | 7.53 MB | Yes | Wx = 60, dx = 6 |
60 × 45 | Algo2 in the proposed EDPA | 95.19 | 1.521 M | 7.53 MB | Yes | Wx = 60, dx = 6 |
60 × 15 | Algo3 in the proposed EDPA | 95.1 | 0.46 M | 3.09 MB | No | Wx = 60, dx = 10 |
60 × 45 | Algo1 in the traditional EDPA [6] | 97.83 | 1.624 M | 7.88 MB | Yes | Wx = 60, dx = 6 |
60 × 45 | Algo2 in the traditional EDPA [8] | 95.29 | 1.623 M | 7.88 MB | Yes | Wx = 60, dx = 6 |
60 × 15 | Algo3 in the traditional EDPA [11] | 94.9 | 0.56 M | 3.28 MB | No | Wx = 60, dx = 10 |
Algorithm | Req/s | Total | Average |
---|---|---|---|
Traditional EDPA using Algo1 DeepConvRNN_Attention [6] | 1 | 0.7910 s | 0.7910 s |
Traditional EDPA using Algo2 FCN-LSTM [8] | 1 | 0.7510 s | 0.7510 s |
Traditional EDPA using Algo3 light-weight FCN-LSTM [11] | 2 | 1.096 s | 0.498 s |
Proposed EDPA using Algo3 | 7 | 0.651 s | 0.142 s |
Proposed EDPA using Algo2 | 5 | 0.851 s | 0.182 s |
Proposed EDPA using Algo1 | 4 | 0.951 s | 0.242 s |
Algorithm | Total Prediction and Input Frames Data Size | Average |
---|---|---|
Traditional EDPA using Algo1 DeepConvRNN_Attention [6] | 10 MB | 10.09 MB/s |
Traditional EDPA using Algo2 FCN-LSTM [8] | 10 MB | 10.51 MB/s |
Traditional EDPA using Algo3 light-weight FCN-LSTM [11] | 5 MB | 9.12 MB/s |
Proposed EDPA using Algo3 | 5 MB | 53.76 MB/s |
Proposed EDPA using Algo2 | 10 MB | 58.75 MB/s |
Proposed EDPA using Algo1 | 10 MB | 42.06 MB/s |
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Pirahandeh, M.; Ullah, S.; Kim, D.-H. A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms. Electronics 2021, 10, 972. https://doi.org/10.3390/electronics10080972
Pirahandeh M, Ullah S, Kim D-H. A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms. Electronics. 2021; 10(8):972. https://doi.org/10.3390/electronics10080972
Chicago/Turabian StylePirahandeh, Mehdi, Shan Ullah, and Deok-Hwan Kim. 2021. "A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms" Electronics 10, no. 8: 972. https://doi.org/10.3390/electronics10080972
APA StylePirahandeh, M., Ullah, S., & Kim, D.-H. (2021). A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms. Electronics, 10(8), 972. https://doi.org/10.3390/electronics10080972