A robust fault-tolerant framework for VM failure predication and efficient task scheduling in dynamic cloud environments

Abstract

Due to the dynamic nature of cloud computing, maintaining fault-tolerance is essential to ensure the reliability and performance of virtualized environments. Failures in Virtual Machines (VMs) disrupt the seamless operation of cloud-based services, making it vital to implement a strong failure prediction system. As a solution, this work proposes a Segmented Regressive Learning-based Multivariate Raindrop Optimized Lottery Scheduling (SRL-MROLS) for dynamic cloud environments. Initially, the VM failure prediction is carried out using a Segmented Regressive Q-learning algorithm, where a set of VMs is provided as input. Segmented regression analyzes the average failure rate of VMs, while a reward-based framework guides the decision-making process for accurate failure prediction. Once a failure is predicted, a relocation process is triggered, involving the migration of workloads or tasks from the failing VM to an alternate VM. Next, a Multivariate Elitism Raindrop Optimization approach is employed to identify the optimal VM for task migration. Finally, a Deadline-Aware Stochastic Prioritized Lottery Scheduling is employed for efficient allocation of tasks to the selected VMs, maintaining seamless operations even in the event of VM failures. This process significantly improves task scheduling by maximizing throughput and minimizing response time in cloud environments. Experimental results demonstrate the superior performance of SRL-MROLS across different metrics. Specifically, it achieves an average improvement of 6.4% in failure prediction accuracy, 27.4% in throughput, and a 13% reduction in response time. Additionally, it reduces failure prediction time by 15%, migration cost by 14.3%, and makespan by 15%, significantly outperforming conventional techniques.