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Global Multidisciplinary Journal

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Integrated Resource Management And Load Optimization Strategies In Cloud-Based Distributed Systems: A Unified Framework

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Rahul Mehta 1 ,
4 Global Institute of Technology, Jaipur, India

Abstract

This paper explores a unified framework for resource management, load optimization, and secure deployment in cloud-based distributed systems. Drawing upon contemporary and seminal works in cloud computing, distributed frameworks, virtualization technologies, and practical application contexts such as e‑learning, small- and medium‑sized enterprises (SMEs), and real-time video streaming, we articulate a comprehensive conceptual model that integrates resource allocation, workload balancing, data security, and energy efficiency. The proposed model leverages theoretical underpinnings from distributed systems and cloud virtualization, as well as empirical insights from recent studies, to address critical challenges including dynamic resource provisioning, load surges, data security over encrypted data, and overhead in framework initialization. Through a detailed methodological exposition and descriptive analysis of synthesized findings, we identify best practices and latent gaps, discuss limitations, and chart a roadmap for future experimental validation and extensions. The unified framework advances state-of-the-art understanding by bridging fragmented research streams into a cohesive architecture for resilient, efficient, and scalable cloud deployments.

Keywords

Cloud computing, distributed systems, resource management

References

📄 Swain, S. R., Singh, A. K., & Lee, C. N. (2022). Efficient resource management in cloud environment. arXiv preprint arXiv:2207.12085.
📄 Sunyaev, A., & Sunyaev, A. (2020). Cloud computing. In Internet Computing: Principles of Distributed Systems and Emerging Internet-Based Technologies (pp. 195–236).
📄 Zebari, I. M. I., Zeebaree, S. R. M., & Yasin, H. M. (2019). Real time video streaming from multi-source using client-server for video distribution. In 2019 4th Scientific International Conference Najaf (SICN) (pp. 109–114). IEEE.
📄 Mahajan, R., Patil, P. R., Shahakar, M., & Potgantwar, A. (2024). An Analytical Evaluation of Various Approaches for Load Optimization in Distributed System. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 526–548.
📄 Siddiqui, S. T., Alam, S., Khan, Z. A., & Gupta, A. (2019). Cloud-based e-learning: using cloud computing platform for an effective e-learning. In Smart Innovations in Communication and Computational Sciences: Proceedings of ICSICCS-2018 (pp. 335–346). Springer.
📄 Abdullah, P. Y., Zeebaree, S. R., Shukur, H. M., & Jacksi, K. (2020). HRM system using cloud computing for Small and Medium Enterprises (SMEs). Technology Reports of Kansai University, 62(04), 04.
📄 Giri, S., & Shakya, S. (2019). Cloud computing and data security challenges: A Nepal case. International Journal of Engineering Trends and Technology, 67(3), 146.
📄 Attaran, M., & Woods, J. (2019). Cloud computing technology: improving small business performance using the Internet. Journal of Small Business & Entrepreneurship, 31(6), 495–519.
📄 Marinescu, D. C. (2022). Cloud computing: theory and practice. Morgan Kaufmann.
📄 Premnath, V., & Vetrivel, M. (2019). Energy Efficient Search Scheme Over Encrypted Data On Mobile Users On Cloud. South Asian Journal of Engineering and Technology, 8(2), 217–222.
📄 Kesarpu, S. (2025). Contract Testing with PACT: Ensuring Reliable API Interactions in Distributed Systems. The American Journal of Engineering and Technology, 7(06), 14–23. https://doi.org/10.37547/tajet/Volume07Issue06-03
📄 van Steen, M., & Tanenbaum, A. S. (2016). A brief introduction to distributed systems. Computing, 98, 967–1009. https://doi.org/10.1007/s00607-016-0508-7
📄 Janardhanan, P. S., & Samuel, P. (2020). Launch overheads of Spark applications on standalone and Hadoop YARN clusters. In Advances in Electrical and Computer Technologies (pp. 47–54). Springer.
📄 Sun, X., He, Y., Wu, D., & Huang, J. Z. (2023). Survey of Distributed Computing Frameworks for Supporting Big Data Analysis. Big Data Mining and Analytics, 6(2), 154–169.
📄 Gu, R., Yang, X., Yan, J., Sun, Y., Wang, B., & Yuan, C., et al. (2014). SHadoop: Improving MapReduce performance by optimizing job execution mechanism in Hadoop clusters. Journal of Parallel and Distributed Computing, 74(3), 2166–2179.
📄 Polato, I., Ré, R., Goldman, A., & Kon, F. (2014). A comprehensive view of Hadoop research — A systematic literature review. Journal of Network and Computer Applications, 46, 1–25.
📄 Wang, Y., Jiang, W., & Agrawal, G. (2012). SciMATE: A novel MapReduce‑like framework for multiple scientific data formats. In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID 2012) (pp. 443–450).
📄 Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113.

How to Cite

Rahul Mehta. (2025). Integrated Resource Management And Load Optimization Strategies In Cloud-Based Distributed Systems: A Unified Framework. Global Multidisciplinary Journal, 4(08), 9-14. https://www.grpublishing.org/journals/index.php/gmj/article/view/211

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