Volume no :
|
Issue no :
Article Type :
Author :
Yoheswari S
Published Date :
Publisher :
Page No: 377 - 382
Abstract : In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated by modern applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. Simulations conducted on various network scenarios demonstrate the effectiveness of this approach, showcasing improvements in data throughput, reduced packet loss, and enhanced quality of service (QoS). The results validate the potential of the proposed technique in optimizing network resource allocation, making it a promising solution for the challenges faced by contemporary data-intensive environments.
Keyword: Load Balancing, Multipath Routing, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Network Optimization
Reference:

1. Ramesh, G., Gorantla, V. A. K., & Gude, V. (2023). A hybrid methodology with learning based approach for protecting systems from DDoS attacks. Journal of Discrete Mathematical Sciences and Cryptography, 26(5), 1317-1325.
2. Logeshwaran, J., Gorantla, V. A. K., Gude, V., & Gorantla, B. (2023, September). The Smart Performance Analysis of Cyber Security Issues in Crypto Currency Using Blockchain. In 2023 6th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 6, pp. 2235-2241). IEEE.
3. Komatireddy, S. R., Meghana, K., Gude, V., & Ramesh, G. (2023, December). Facial Shape Analysis and Accessory Recommendation: A Human-Centric AI Approach. In 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 182-191). IEEE.
4. Sriramulugari, S. K., Gorantla, V. A. K., Gude, V., Gupta, K., & Yuvaraj, N. (2024, March). Exploring mobility and scalability of cloud computing servers using logical regression framework. In 2024 2nd International Conference on Disruptive Technologies (ICDT) (pp. 488-493). IEEE.
5. Gorantla, V. A. K., Gude, V., Sriramulugari, S. K., Yuvaraj, N., & Yadav, P. (2024, March). Utilizing hybrid cloud strategies to enhance data storage and security in e-commerce applications. In 2024 2nd International Conference on Disruptive Technologies (ICDT) (pp. 494-499). IEEE.
6. Bharathi, G. P., Chandra, I., Sanagana, D. P. R., Tummalachervu, C. K., Rao, V. S., & Neelima, S. (2024). AI-driven adaptive learning for enhancing business intelligence simulation games. Entertainment Computing, 50, 100699.
7. Sanagana, D. P. R., & Tummalachervu, C. K. (2024, May). Securing Cloud Computing Environment via Optimal Deep Learning-based Intrusion Detection Systems. In 2024 Second International Conference on Data Science and Information System (ICDSIS) (pp. 1-6). IEEE.
8. Thangapalani, L., Dharini, R., & Keerthana, R. (2023, May). Securing Medical Image Transmission using Memetic Algorithm. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-8). IEEE.
9. Vennila, D., Vinotha, C., Shanthakumari, A., & Thangapalani, L. Convex Optimization Algorithm for Product Recommendation Using Microblogging Information. Journal of Data Mining and Management, 2(1).
10. Kumar, V. S., Thansekhar, M. R., Saravanan, R., & Amali, S. M. J. (2014). Solving multi-objective vehicle routing problem with time windows by FAGA. Procedia Engineering, 97, 2176-2185.
11. Selvan, M. A., & Amali, S. M. J. (2024). RAINFALL DETECTION USING DEEP LEARNING TECHNIQUE.
12. Sivaramkumar, V., Thansekhar, M. R., Saravanan, R., & Miruna Joe Amali, S. (2018). Demonstrating the importance of using total time balance instead of route balance on a multi-objective vehicle routing problem with time windows. The International Journal of Advanced Manufacturing Technology, 98, 1287-1306.
13. Sashi Rekha, K., & Miruna Joe Amali, S. A. (2022). Efficient feature subset selection and classification using levy flight‐based cuckoo search optimization with parallel support vector machine for the breast cancer data. International Journal of Imaging Systems and Technology, 32(3), 869-881.
14. Jemimah, J. P. P., & Miruna Joe Amali, S. (2024). An efficient PCCM masking scheme for PAPR reduction and encryption in OFDM-VLC system. Telecommunication Systems, 1-31.
15. Kiran, A., Kalpana, V., Madanan, M., Ramesh, J. V. N., Alfurhood, B. S., & Mubeen, S. (2023). Anticipating network failures and congestion in optical networks a data analytics approach using genetic algorithm optimization. Optical and Quantum Electronics, 55(13), 1193.
16. Lalithambigai, M., Kalpana, V., Kumar, A. S., Uthayakumar, J., Santhosh, J., & Mahaveerakannan, R. (2023, February). Dimensionality reduction with DLMNN technique for handling secure medical data in healthcare-IoT model. In 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 111-117). IEEE.
17. Kalpana, V., Mishra, D. K., Chanthirasekaran, K., Haldorai, A., Nath, S. S., & Saraswat, B. K. (2022). On reducing energy cost consumption in heterogeneous cellular networks using optimal time constraint algorithm. Optik, 270, 170008.
18. Kalpana, V., & Karthik, S. (2020). Route availability with QoE and QoS metrics for data analysis of video stream over a mobile ad hoc networks. Wireless Personal Communications, 114(3), 2591-2612.
19. Kalpana, V., & Karthik, S. (2018, February). Bandwidth Constrained Priority Based Routing Algorithm for Improving the Quality of Service in Mobile Ad hoc Networks. In 2018 International Conference on Soft-computing and Network Security (ICSNS) (pp. 1-8). IEEE.