AI-Enabled VM Migration Strategies in Cloud Infrastructure

Abstract

Live migration of virtual machines (VMs) is a foundational mechanism for elasticity, high availability, and energy-aware consolidation in cloud datacenters. Yet, traditional threshold-based or rule-driven policies struggle with nonstationary workloads, heterogeneous hosts, and multi-objective trade-offs among service-level objectives (SLOs), energy, and migration overhead. This manuscript proposes and evaluates AI-enabled migration strategies that combine (i) predictive analytics for host-overload/underload detection and migration cost estimation, and (ii) decision-making via reinforcement learning (RL) to schedule and route migrations under uncertainty. We present a modular pipeline: feature engineering from per-host/per-VM telemetry; supervised forecasting of near-future resource pressure; a learned migration-cost model; and an RL policy that selects migration actions using a reward shaping that balances SLO violations, energy consumption, and migration time.

A simulation study with realistic bursty traces and heterogenous hosts compares a baseline heuristic, a supervised-learning policy, and a Proximal Policy Optimization (PPO) RL agent. Results indicate that the AI-enabled strategies reduce SLO violation rate by 31–54% and energy consumption by 12–23% relative to the baseline, while maintaining low downtime and bounded network overhead. A statistical analysis (one-way ANOVA with post-hoc tests) confirms improvements are significant at α=0.05 for primary outcomes. We discuss design choices (e.g., reward coefficients, safe-action filters), operational safeguards (e.g., blacklisting hot pages, rate-limiting), and limitations (trace bias, simulator fidelity). The study demonstrates that AI-driven migration unifies prediction and control to adapt to workload dynamics, providing a principled path to greener, more reliable cloud infrastructure.