A Proactive Approach to Fault Tolerance Using Predictive Machine Learning Models in Distributed Systems
Journal Title: International Journal of Experimental Research and Review - Year 2024, Vol 44, Issue 8
Abstract
In the era of cloud computing and large-scale distributed systems, ensuring uninterrupted service and operational reliability is crucial. Conventional fault tolerance techniques usually take a reactive approach, addressing problems only after they arise. This can result in performance deterioration and downtime. With predictive machine learning models, this research offers a proactive approach to fault tolerance for distributed systems, preventing significant failures before they arise. Our research focuses on combining cutting-edge machine learning algorithms with real-time analysis of massive streams of operational data to predict abnormalities in the system and possible breakdowns. We employ supervised learning algorithms such as Random Forests and Gradient Boosting to predict faults with high accuracy. The predictive models are trained on historical data, capturing intricate patterns and correlations that precede system faults. Early defect detection made possible by this proactive approach enables preventative remedial measures to be taken, reducing downtime and preserving system integrity. To validate our approach, we designed and implemented a fault prediction framework within a simulated distributed system environment that mirrors contemporary cloud architectures. Our experiments demonstrate that the predictive models can successfully forecast a wide range of faults, from hardware failures to network disruptions, with significant lead time, providing a critical window for implementing preventive measures. Additionally, we assessed the impact of these pre-emptive actions on overall system performance, highlighting improved reliability and a reduction in mean time to recovery (MTTR). We also analyse the scalability and adaptability of our proposed solution within diverse and dynamic distributed environments. Through seamless integration with existing monitoring and management tools, our framework significantly enhances fault tolerance capabilities without requiring extensive restructuring of current systems. This work introduces a proactive approach to fault tolerance in distributed systems using predictive machine learning models. Unlike traditional reactive methods that respond to failures after they occur, this work focuses on anticipating faults before they happen.
Authors and Affiliations
Mohd Haroon, Zeeshan Ali Siddiqui, Mohammad Husain, Arshad Ali, Tameem Ahmad
Keywords
Related Articles
- EP ID EP750737
- DOI 10.52756/ijerr.2024.v44spl.018
- Views 60
- Downloads 0
Pathophysiological Role of Cytokines in Bovine
Cytokines are important agents in regulating immune response in many diseases, and knowledge of relevant cytokine immune networks is necessary for understanding processes encompassed within their pathophysiology. One suc...
Impact of Social Entrepreneurship on Digital Technology and Students’ Skill Set in Higher Education Institutions: A Structured Equation Model
This research paper investigates the impact of social entrepreneurial initiatives implemented by Colleges and Universities on the utilization of digital technology, examining how this utilization, in turn, affects the le...
A Hybrid Framework for Plant Leaf Region Segmentation: Comparative Analysis of Swarm Intelligence with Convolutional Neural Networks
Agriculture is important for the survival of humanity since about 70% of the world's population is engaged in agricultural pursuits to varying degrees. The previous and present methodology lacks ways to identify diseases...
Copper Mining Solid Waste as Replacement Aggregate of River Sand in Cement Mortar to Investigate the Effect on Fresh, Durability and Microstructural Properties: A Walkthrough to Sustainability
The present study investigates the feasibility and efficiency of river sand replacement with copper mine tailing waste as fine aggregate in cement mortar. The scope of the present study includes durability, microstructur...
Computational Thinking Processes in Solving the Corona Epidemic Model: Pre-service Maths Teachers
In the 21st century, pre-service mathematics teachers are expected to have problem-solving skills that are effective, efficient, and solutive and are in line with the mindset of computer experts. In learning mathematics,...