Predictive and fault-tolerant virtual machine migration for energy-efficient cloud data centers

Abstract

Virtual machine (VM) migration is a key mechanism for improving energy efficiency, service continuity and reliability in cloud data centers. However, conventional migration strategies are largely reactive and fail to account for workload fluctuations and potential failures, often resulting in inefficient resource utilization and increased service-level agreement (SLA) violations. This paper proposes the predictive VM migration manager (PVMM), a unified framework that integrates workload forecasting and failure prediction for proactive migration control. PVMM combines a gated recurrent unit (GRU)-based model for short-term resource prediction with a machine-learning-based failure predictor to identify high-risk VMs. An adaptive dynamic threshold based on energy consumption (ADT-EC) detects host overload conditions, while an improved energy-aware best-fit (IEABF) heuristic selects optimal migration targets. Experimental results demonstrate that PVMM reduces unnecessary migration, improves SLA compliance and enhances overall energy efficiency and system reliability. These findings highlight the effectiveness of predictive, multi-objective migration strategies for next-generation cloud data center management.