Load Balancing Optimization through Multi-Label Classification of Network Traffic Using Deep Learning in Distributed Computing Systems
Received: 6 September 2025 | Revised: 19 October 2025, 1 November 2025, and 5 November 2025 | Accepted: 6 November 2025 | Online: 9 February 2026
Corresponding author: Ferry Fachrizal
Abstract
This study discusses the application of a deep learning-based multi-label classification method integrated with an adaptive load balancing mechanism in a distributed computing system. The main objective of this study is to improve the efficiency, stability, and responsiveness of the system in handling complex and dynamic network traffic. The simulation process begins with traffic data processing, feature extraction using a Recurrent Neural Network (RNN) with a Long Short-Term Memory (LSTM) architecture, and the implementation of a multi-label classification framework. The classification results are then used as input for the adaptive load balancing mechanism, which is further optimized through integration with Deep Reinforcement Learning (DRL). Performance evaluation shows that the multi-label RNN model is able to achieve an Area Under the Curve (AUC) value close to 0.95 on training and validation data, indicating good generalization ability. However, the confusion matrix reveals that there are still quite high classification errors, especially in Video Streaming and Web Traffic, whereas performance on VoIP is relatively more stable. The implementation of DRL is proven effective with a consistently increasing reward trend, indicating the agent's ability to adapt to system conditions. Furthermore, the results of throughput and latency measurements show significant improvements after the implementation of adaptive load balancing. Average throughput increased from 85–115 requests/s to 120–150 requests/s, whereas latency decreased from 95–120 ms to 65–95 ms.
Keywords:
multi-label classification, deep learning, adaptive load balancing, Deep Reinforcement Learning (DRL), distributed computingDownloads
References
I. C. Obasi and C. Benson, "The Impact of Digitalization and Information and Communication Technology on the Nature and Organization of Work and the Emerging Challenges for Occupational Safety and Health," International Journal of Environmental Research and Public Health, vol. 22, no. 3, Mar. 2025, Art. no. 362. DOI: https://doi.org/10.3390/ijerph22030362
A. A. Vărzaru and C. G. Bocean, "Digital Transformation and Innovation: The Influence of Digital Technologies on Turnover from Innovation Activities and Types of Innovation," Systems, vol. 12, no. 9, Sept. 2024, Art. no. 359. DOI: https://doi.org/10.3390/systems12090359
A. T. Rosário and R. Raimundo, "Internet of Things and Distributed Computing Systems in Business Models," Future Internet, vol. 16, no. 10, Oct. 2024, Art. no. 384. DOI: https://doi.org/10.3390/fi16100384
P. J. Escamilla-Ambrosio, A. Rodríguez-Mota, E. Aguirre-Anaya, R. Acosta-Bermejo, and M. Salinas-Rosales, "Distributing Computing in the Internet of Things: Cloud, Fog and Edge Computing Overview," in NEO 2016: Results of the Numerical and Evolutionary Optimization Workshop NEO 2016 and the NEO Cities 2016 Workshop held on September 20-24, 2016 in Tlalnepantla, Mexico, Y. Maldonado, L. Trujillo, O. Schütze, A. Riccardi, and M. Vasile, Eds. Cham, Switzerland: Springer International Publishing, 2018, pp. 87–115. DOI: https://doi.org/10.1007/978-3-319-64063-1_4
A. Barrak, F. Petrillo, and F. Jaafar, "Architecting Peer-to-Peer Serverless Distributed Machine Learning Training for Improved Fault Tolerance." arXiv, Feb. 27, 2023.
P. K. Donta, I. Murturi, V. Casamayor Pujol, B. Sedlak, and S. Dustdar, "Exploring the Potential of Distributed Computing Continuum Systems," Computers, vol. 12, no. 10, Oct. 2023, Art. no. 198. DOI: https://doi.org/10.3390/computers12100198
B. Pourghebleh and V. Hayyolalam, "A comprehensive and systematic review of the load balancing mechanisms in the Internet of Things," Cluster Computing, vol. 23, no. 2, pp. 641–661, June 2020. DOI: https://doi.org/10.1007/s10586-019-02950-0
P. Kumar and R. Kumar, "Issues and Challenges of Load Balancing Techniques in Cloud Computing: A Survey," ACM Computing Surveys, vol. 51, no. 6, Feb. 2019, Art. no. 120. DOI: https://doi.org/10.1145/3281010
X. Liu, Y. Han, and Y. Du, "IoT Device Identification Using Directional Packet Length Sequences and 1D-CNN," Sensors, vol. 22, no. 21, Nov. 2022, Art. no. 8337. DOI: https://doi.org/10.3390/s22218337
C. Zhao, L. X. Liao, G. Chen, and H.-C. Chao, "Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open Issues," Sensors, vol. 25, no. 8, Apr. 2025, Art. no. 2368. DOI: https://doi.org/10.3390/s25082368
Y. Guo et al., "Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration," Sensors, vol. 23, no. 16, Sept. 2023, Art. no. 7091. DOI: https://doi.org/10.3390/s23167091
M. Amjad Dipa, S. Yakoob, F. Rasid, F. Ahmad, and A. Mahmud, "Deep Reinforcement Learning Based Load Balancing Scheme in Dense Cellular Network Using RoF Technology," Journal of Communications Software and Systems, vol. 21, no. 3, pp. 317–326, July 2025. DOI: https://doi.org/10.24138/jcomss-2025-0056
F. Garcia-Carballeira, A. Calderon, and J. Carretero, "Enhancing the power of two choices load balancing algorithm using round robin policy," Cluster Computing, vol. 24, no. 2, pp. 611–624, June 2021. DOI: https://doi.org/10.1007/s10586-020-03139-6
D. Choi, K. S. Chung, and J. Shon, "An Improvement on the Weighted Least-Connection Scheduling Algorithm for Load Balancing in Web Cluster Systems," in 2010 International Conferences on Grid and D- tributed Computing (GDC 2010), and Control and Automation (CA 2010), Jeju Island, Korea, 2010, pp. 127–134. DOI: https://doi.org/10.1007/978-3-642-17625-8_13
T. Akhtar, N. G. Haider, and S. M. Khan, "A Comparative Study of the Application of Glowworm Swarm Optimization Algorithm with other Nature-Inspired Algorithms in the Network Load Balancing Problem," Engineering, Technology & Applied Science Research, vol. 12, no. 4, pp. 8777–8784, Aug. 2022. DOI: https://doi.org/10.48084/etasr.4999
H. M. Abdelghany, "SVM-Based Load Balancing for Efficient Edge Computing," International Journal of Telecommunications, vol. 5, no. 1, pp. 1–20, Feb. 2025. DOI: https://doi.org/10.21608/ijt.2025.339202.1068
P. Moutis, S. Skarvelis-Kazakos, and M. Brucoli, "Decision tree aided planning and energy balancing of planned community microgrids," Applied Energy, vol. 161, pp. 197–205, Jan. 2016. DOI: https://doi.org/10.1016/j.apenergy.2015.10.002
A. Banerjee, G. Chatterjee, D. Chakraborty, and S. Majumder, "Cluster Based Intelligent Load Balancing Algorithm Applied in Cloud Computing Using KNN," in 2nd International Conference on Non-Conventional Energy: Nanotechnology & Nanomaterials for Energy & Environment, Rochester, NY, 2019. DOI: https://doi.org/10.2139/ssrn.3503518
S. Izadi, M. Ahmadi, and A. Rajabzadeh, "Network Traffic Classification Using Deep Learning Networks and Bayesian Data Fusion," Journal of Network and Systems Management, vol. 30, no. 2, Jan. 2022, Art. no. 25. DOI: https://doi.org/10.1007/s10922-021-09639-z
S. Guo, Y. Lin, S. Li, Z. Chen, and H. Wan, "Deep Spatial–Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting," IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 10, pp. 3913–3926, Oct. 2019. DOI: https://doi.org/10.1109/TITS.2019.2906365
N. Ramakrishnan and T. Soni, "Network Traffic Prediction Using Recurrent Neural Networks," in 2018 17th IEEE International Conference on Machine Learning and Applications, Orlando, FL, USA, 2018, pp. 187–193. DOI: https://doi.org/10.1109/ICMLA.2018.00035
T.-Y. Kim and S.-B. Cho, "Web traffic anomaly detection using C-LSTM neural networks," Expert Systems with Applications, vol. 106, pp. 66–76, Sept. 2018. DOI: https://doi.org/10.1016/j.eswa.2018.04.004
S. Soleymanpour, H. Sadr, and M. Nazari Soleimandarabi, "CSCNN: Cost-Sensitive Convolutional Neural Network for Encrypted Traffic Classification," Neural Processing Letters, vol. 53, no. 5, pp. 3497–3523, Oct. 2021. DOI: https://doi.org/10.1007/s11063-021-10534-6
M. Li and Z. Wang, "Deep learning for high-dimensional reliability analysis," Mechanical Systems and Signal Processing, vol. 139, May 2020, Art. no. 106399. DOI: https://doi.org/10.1016/j.ymssp.2019.106399
T. Georgiou, Y. Liu, W. Chen, and M. Lew, "A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision," International Journal of Multimedia Information Retrieval, vol. 9, no. 3, pp. 135–170, Sept. 2020. DOI: https://doi.org/10.1007/s13735-019-00183-w
A. Kumar, D. Anand, S. Jha, G. Joshi, and W. Cho, "Optimized Load Balancing Technique for Software Defined Network," Computers, Materials & Continua, vol. 72, no. 1, pp. 1409–1426, Feb. 2022. DOI: https://doi.org/10.32604/cmc.2022.024970
J. S. Rojas, "IP Network Traffic Flows Labeled with 75 Apps." Kaggle. [Online]. Available: https://www.kaggle.com/datasets/jsrojas/ip-network-traffic-flows-labeled-with-87-apps.
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