Efficiency Analysis of AI Self-Control System and Data Processing Unit Based on Edge Computing Technology
Abstract
This study proposes a novel Dynamic Task Allocation and Resource Adaptive Adjustment (DTARA) algorithm to enhance the operational efficiency of AI self-control systems and data processing units within edge computing frameworks. Through an experimental environment simulating real-world scenarios, the DTARA algorithm is compared with fixed task allocation algorithms and simple priority-based task allocation algorithms. The experimental setup includes large-scale task scenarios (500-1000 concurrent tasks) and long-term operation scenarios (lasting several hours to several days). The results demonstrate that under complex task loads, the DTARA algorithm reduces system task completion time by an average of 30.5% compared to traditional algorithms and improves resource utilization by 28.8%. When the data processing volume reaches 10GB, the data processing delay is reduced by 45.6% compared to the benchmark algorithm. In large-scale task scenarios, as the number of tasks increases from 500 to 1000, the DTARA algorithm maintains low task completion times and data processing delays. In long-term operation experiments, the task execution success rate exceeds 95%, the CPU utilization fluctuation range is within 10%, and no system crashes occur. This study offers a practical and effective solution to improve the performance of related systems, supported by theoretical analyses of computational complexity, optimality guarantees, and convergence properties of the DTARA algorithm.
Full Text:
PDFReferences
Gao, C. (2023). Efficiency of artificial intelligence automatic control system and data processing unit based on edge computing technology. International Journal of Emerging Electric Power Systems, 24(4), 519–528. https://doi.org/10.1515/ijeeps-2023-0115
Chang, Z., Liu, S., Xiong, X., Cai, Z., & Tu, G. (2021). A survey of recent advances in edge-computing-powered artificial intelligence of things. IEEE Internet of Things Journal, 8(18), 13849–13875. https://doi.org/10.1109/JIOT.2021.3088875
Hua, H., Li, Y., Wang, T., Dong, N., Li, W., & Cao, J. (2023). Edge computing with artificial intelligence: A machine learning perspective. ACM Computing Surveys, 55(9), 1–35. https://doi.org/10.1145/3555802
Nain, G., Pattanaik, K. K., & Sharma, G. K. (2022). Towards edge computing in intelligent manufacturing: Past, present and future. Journal of Manufacturing Systems, 62, 588–611. https://doi.org/10.1016/j.jmsy.2022.01.010
Chavhan, S., Gupta, D., Gochhayat, S. P., B, C. B., Khanna, A., Shankar, K., & Rodrigues, J. J. (2022). Edge computing AI-IoT integrated energy-efficient intelligent transportation system for smart cities. ACM Transactions on Internet Technology, 22(4), 1–18. https://doi.org/10.1145/3507906
Zhu, S., Ota, K., & Dong, M. (2021). Green AI for IIoT: Energy efficient, intelligent edge computing for the industrial internet of things. IEEE Transactions on Green Communications and Networking, 6(1), 79–88. https://doi.org/10.1109/TGCN.2021.3100622
Lv, Z., Qiao, L., Verma, S., & Kavita. (2021). AI-enabled IoT-edge data analytics for connected living. ACM Transactions on Internet Technology, 21(4), 1–20. https://doi.org/10.1145/3421510
Thota, R. C. (2024). Optimizing edge computing and AI for low-latency cloud workloads. International Journal of Science and Research Archive, 13(1), 3484–3500. https://doi.org/10.30574/ijsra.2024.13.1.1761
Singh, A., Satapathy, S. C., Roy, A., & Gutub, A. (2022). AI-based mobile edge computing for IoT: Applications, challenges, and future scope. Arabian Journal for Science and Engineering, 47(8), 9801–9831.
Hayyolalam, V., Aloqaily, M., Özkasap, Ö., & Guizani, M. (2021). Edge intelligence for empowering IoT-based healthcare systems. IEEE Wireless Communications, 28(3), 6–14. https://doi.org/10.48550/arXiv.2103.12144
Zhu, S., Ota, K., & Dong, M. (2022). Energy-efficient artificial intelligence of things with intelligent edge. IEEE Internet of Things Journal, 9(10), 7525–7532. https://doi.org/10.1109/JIOT.2022.3143722
Bajaj, K., Sharma, B., & Singh, R. (2022). Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor-generated big data. Complex & Intelligent Systems, 8(5), 3641–3658. https://doi.org/10.1007/s40747-021-00434-6
Yu, W., Liu, Y., Dillon, T., & Rahayu, W. (2022). Edge computing-assisted IoT framework with an autoencoder for fault detection in manufacturing predictive maintenance. IEEE Transactions on Industrial Informatics, 19(4), 5701–5710. https://doi.org/10.1109/TII.2022.3178732
Lu, S., Lu, J., An, K., Wang, X., & He, Q. (2023). Edge computing on IoT for machine signal processing and fault diagnosis: A review. IEEE Internet of Things Journal, 10(13), 11093–11116. https://doi.org/10.1109/JIOT.2023.3239944
Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022). Distributed artificial intelligence empowered by end-edge-cloud computing: A survey. IEEE Communications Surveys & Tutorials, 25(1), 591–624. https://doi.org/10.1109/COMST.2022.3218527
McEnroe, P., Wang, S., & Liyanage, M. (2022). A survey on the convergence of edge computing and AI for UAVs: Opportunities and challenges. IEEE Internet of Things Journal, 9(17), 15435–15459. https://doi.org/10.1109/JIOT.2022.3176400
Liu, X., Yang, J., Zou, C., Chen, Q., Yan, X., Chen, Y., & Cai, C. (2021). Collaborative edge computing with FPGA-based CNN accelerators for energy-efficient and time-aware face tracking system. IEEE Transactions on Computational Social Systems, 9(1), 252–266. https://doi.org/10.1109/TCSS.2021.3059318
Kasparaitis, P. (2025). Evaluation of Lithuanian Speech-to-Text Transcribers. Informatica, 1-16. https://doi.org/10.15388/25-INFOR591
Munir, A., Blasch, E., Kwon, J., Kong, J., & Aved, A. (2021). Artificial intelligence and data fusion at the edge. IEEE Aerospace and Electronic Systems Magazine, 36(7), 62–78. https://doi.org/10.1109/MAES.2020.3043072
Sanfilippo, S., Hernández-Gálvez, J. J., Hernández-Cabrera, J. J., Évora-Gómez, J., Roncal-Andrés, O., & Caballero-Ramirez, M. (2025). Evolving Electricity Demand Modelling in Microgrids Using a Kolmogorov-Arnold Network. Informatica, 1-22. https://doi.org/10.15388/25-INFOR590
Rajavel, R., Ravichandran, S. K., Harimoorthy, K., Nagappan, P., & Gobichettipalayam, K. R. (2022). IoT-based smart healthcare video surveillance system using edge computing. Journal of Ambient Intelligence and Humanized Computing, 13(6), 3195–3207. https://doi.org/10.1007/s12652-021-03157-1
DOI: https://doi.org/10.31449/inf.v49i34.9291
This work is licensed under a Creative Commons Attribution 3.0 License.