Predictive Analytics For Fault Tolerance In Distributed Systems Using Logistic Regression And Support Vector Machine

Abstract

Fault tolerance is crucial to distributed systems to remain reliable and to mitigate unanticipated loss of services. Through predictive analytics we are able to identify and anticipate underlying faults enabling us to intervene and take appropriate steps to ensure continuity of services. The goal of this initial research was to introduce a new predictive fault tolerance method that exploits Logistic Regression (LR) and Support Vector Machine (SVM) models. Our approach developed predictive models to analyze logs, performance data, and environmental factors which include resource utilization and network latency to identify advance warning signs of instability. The LR model gives you the probability of failure which represents a form of risk assessment and the SVM provides a limiting factor in operational stability and potential failure. To illustrate our approach, we built two distinct systems that operated within a simulated distributed environment and both models offered realistic predictive accuracy. The combinations of the two techniques are significant: LR provides a transparent model of potential failure and SVM guarantees an accurate definition of faulty. With predictive fault tolerance, we give back some fault mitigation planning to distributed systems, enabling that systems can remediate faults proactively and build resilience to ensure uninterrupted operations. These techniques will be especially relevant when scaling up to large data centers, traditional cloud environments, or to critical infrastructure where very short amounts of downtime can have severe consequences..