Techniques for Reducing Latency in Cloud-Based Networks: A Comprehensive Study
Keywords:
Latency reduction, Cloud-based networks, Content Delivery Networks (CDNs), Edge Computing, Quality of Service (QoS), Multipath TCPAbstract
Reducing latency in cloud-based networks is crucial for enhancing user experience and optimizing application performance across distributed environments. This comprehensive study explores various techniques aimed at minimizing latency in cloud networking infrastructures. Key strategies include the use of Content Delivery Networks (CDNs) for caching and delivering content closer to end-users, Edge Computing to process data near the point of generation, and Quality of Service (QoS) mechanisms to prioritize critical traffic. Additionally, advancements in network protocols, such as Multipath TCP and QUIC, are examined for their ability to improve data transfer efficiency and reduce latency. Moreover, optimization techniques in virtualization, containerization, and workload scheduling are discussed to enhance resource utilization and responsiveness. By synthesizing these approaches, this study provides insights into effective latency reduction strategies that enable cloud-based networks to meet the demands of modern applications while improving overall performance and user satisfaction.
References
[1] S. K. Das and S. Bebortta, "Heralding the future of federated learning framework: architecture, tools and future directions," in 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2021: IEEE, pp. 698-703.
[2] B. Desai and K. Patil, "Demystifying the complexity of multi-cloud networking," Asian American Research Letters Journal, vol. 1, no. 4, 2024.
[3] F. Firouzi et al., "Fusion of IoT, AI, edge–fog–cloud, and blockchain: Challenges, solutions, and a case study in healthcare and medicine," IEEE Internet of Things Journal, vol. 10, no. 5, pp. 3686-3705, 2022.
[4] N. Mazher and I. Ashraf, "A Survey on data security models in cloud computing," International Journal of Engineering Research and Applications (IJERA), vol. 3, no. 6, pp. 413-417, 2013.
[5] V. N. Kollu, V. Janarthanan, M. Karupusamy, and M. Ramachandran, "Cloud-based smart contract analysis in fintech using IoT-integrated federated learning in intrusion detection," Data, vol. 8, no. 5, p. 83, 2023.
[6] J. Balen, D. Damjanovic, P. Maric, and K. Vdovjak, "Optimized Edge, Fog and Cloud Computing Method for Mobile Ad-hoc Networks," in 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021: IEEE, pp. 1303-1309.
[7] B. Desai and K. Patel, "Reinforcement Learning-Based Load Balancing with Large Language Models and Edge Intelligence for Dynamic Cloud Environments," Journal of Innovative Technologies, vol. 6, no. 1, pp. 1− 13-1− 13, 2023.
[8] C. Martín, D. Garrido, L. Llopis, B. Rubio, and M. Díaz, "Facilitating the monitoring and management of structural health in civil infrastructures with an Edge/Fog/Cloud architecture," Computer Standards & Interfaces, vol. 81, p. 103600, 2022.
[9] K. Thakur, M. Qiu, K. Gai, and M. L. Ali, "An investigation on cyber security threats and security models," in 2015 IEEE 2nd international conference on cyber security and cloud computing, 2015: IEEE, pp. 307-311.
[10] K. Patil and B. Desai, "From Remote Outback to Urban Jungle: Achieving Universal 6G Connectivity through Hybrid Terrestrial-Aerial-Satellite Networks," Advances in Computer Sciences, vol. 6, no. 1, pp. 1− 13-1− 13, 2023.
[11] D. Rahbari and M. Nickray, "Computation offloading and scheduling in edge-fog cloud computing," Journal of Electronic & Information Systems, vol. 1, no. 1, pp. 26-36, 2019.
[12] D. Narayanan, K. Santhanam, F. Kazhamiaka, A. Phanishayee, and M. Zaharia, "Analysis and exploitation of dynamic pricing in the public cloud for ml training," in VLDB DISPA Workshop 2020, 2020.
