Authors:
Padmaja Pulivarthy
Addresses:
Department of IT Infrastructure, Samsung Semiconductor, Austin, Texas, United States of America.
The paper is required to manage massive distributed data systems so that performance and reliability can be optimized in modern computational ecosystems. The rate at which applications have been more data-intensive necessitates new solutions for balancing loads that may not create bottlenecks in the system but increase an application’s scalability. This also foresees an environment where the provisioning of resources through machine learning and optimization algorithms will see efficient use of resources in case of low latency. The method advocates for predictive analytics in tandem with runtime decision-making, which means dynamic workload distribution across the system. From these empirical evaluation findings, intelligent load balancing performs considerably better than traditional techniques regarding various workload patterns. Such results reflect intelligent algorithms’ capacity to alter the nature of distributed data management, thereby bringing forth efficiency, robustness, and adaptability toward increased data demands.
Keywords: Distributed Systems; Load Balancing; Machine Learning; Optimization Algorithms; Resource Utilization; Computational Resources; Cloud Computing; Infrastructural Support Structure.
Received on: 25/05/2024, Revised on: 12/08/2024, Accepted on: 19/10/2024, Published on: 14/12/2024
AVE Trends in Intelligent Computing Systems, 2024 Vol. 1 No. 4, Pages: 219-230