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.References
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
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