Developing and validating a Distributed Computing Framework for Big Data Analytics - Ahmad Ali Khalifah Al-Zoubi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The unprecedented growth in data volume and complexity has necessitated the evolution of advanced computing frameworks capable of handling Big Data analytics efficiently. This research focuses on the development and validation of a distributed computing framework tailored to the challenges posed by large-scale data analytics. The proposed framework aims to enhance scalability, fault tolerance, and performance, addressing the unique requirements of processing massive datasets. The research begins with an in-depth review of existing distributed computing frameworks and identifies their strengths and limitations in the context of Big Data analytics. Drawing on insights from this analysis, a novel framework is designed, incorporating innovative strategies to optimize data distribution, parallel processing, and fault recovery mechanisms. The architecture integrates both batch and real-time processing capabilities, ensuring versatility in handling diverse analytical workloads. To validate the efficacy of the proposed framework, a series of experiments are conducted using representative Big Data sets from various domains. Performance metrics such as processing speed, resource utilization, and scalability are measured and compared against established benchmarks. Additionally, the framework is subjected to stress testing scenarios to evaluate its robustness under adverse conditions. The research explores the integration of machine learning algorithms within the distributed framework to enable predictive analytics and enhance decision-making capabilities. The adaptability of the framework to different machine learning models is assessed, and its impact on overall system performance is analyzed. The validation results demonstrate that the proposed distributed computing framework exhibits significant improvements in terms of processing speed, scalability, and fault tolerance compared to existing solutions. The findings highlight its potential to address the challenges posed by Big Data analytics and its suitability for deployment in real-world applications. This research contributes to the field of distributed computing by presenting a novel framework specifically tailored for Big Data analytics. The comprehensive validation process establishes its effectiveness and reliability, opening avenues for further research and practical implementation in industries and research domains dealing with massive datasets.