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@article{192405,
        author = {Dr. Neha Sharma},
        title = {Deep Learning Model for Smart Load Balancing of Data Packets in Cloud Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1081-1085},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192405},
        abstract = {Cloud computing has become the backbone of modern digital services, requiring efficient management of network traffic to ensure high performance and reliability. Traditional load balancing techniques such as Round Robin and Least Connection fail to adapt to dynamic traffic patterns and unpredictable workloads. This paper proposes a deep learning-based smart load balancing model for data packet distribution in cloud networks. The proposed system uses a neural network to analyze network traffic features such as packet arrival rate, queue length, and server utilization, and dynamically assigns packets to optimal servers. Simulation results show that the proposed model significantly reduces packet delay, packet loss, and improves throughput compared to conventional load balancing algorithms. This research demonstrates the effectiveness of deep learning in achieving intelligent and adaptive packet traffic management in cloud environments.},
        keywords = {Cloud Computing, Load Balancing, Deep Learning, Neural Networks, Network Traffic Control, Data Packets.},
        month = {February},
        }