A Proactive Approach to Fault Tolerance Using Predictive Machine Learning Models in Distributed Systems

Mohd Haroon; Zeeshan Ali Siddiqui; Mohammad Husain; Arshad Ali; Tameem Ahmad

doi:10.52756/ijerr.2024.v44spl.018

Authors

Mohd Haroon Department of Computer Science and Engineering, Integral University, Lucknow, India https://orcid.org/0000-0001-7967-7302
Zeeshan Ali Siddiqui Department of Computer Science and Engineering, University of Lucknow, Lucknow, India https://orcid.org/0000-0002-0049-2362
Mohammad Husain Faculty of Computer and Information System, Islamic University of Madinah, Saudi Arabia
Arshad Ali Faculty of Computer and Information System, Islamic University of Madinah, Saudi Arabia https://orcid.org/0000-0001-5625-0867
Tameem Ahmad Department of Computer Engineering, Aligarh Muslim University, Aligarh, India https://orcid.org/0000-0001-9802-5713

DOI:

https://doi.org/10.52756/ijerr.2024.v44spl.018

Keywords:

Cloud computing, distributed systems, preventive maintenance, proactive fault tolerance, random forest machine learning models

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.

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