AI-Assisted Virtual Machine Right-Sizing with Human Oversight: A Comprehensive Technical Analysis

Abstract

The challenge of virtual machine right-sizing remains significant because cloud computing environments are characterized by unpredictable workloads, sensitivity to performance, and massive deployments that impact resource optimization strategies. Manual right-sizing relies on periodic reviews, fixed utilization limits, and human intuition and is therefore slow, inconsistent, and difficult to apply to large enterprise infrastructures. On the other hand, completely automated right-sizing systems are operationally unsafe as well because hyper-aggressive resizing behavior may inadvertently lead to performance impairment, service level agreement breach, or even production workload instability. Right-sizing systems supported by AI deal with these limitations by operating on workload telemetry, historical use trends, and performance indicators in real time to produce evidence-based suggestions for optimal VM configurations. These systems do not implement change independently. Instead, they provide recommendations along with confidence scores and impact ratings, and are able to process high-volume data using artificial intelligence while avoiding the risks associated with fully automated operations in sensitive infrastructure environments. Human-in-the-loop governance models position AI as an augmentation of human decision-making rather than a replacement. Cloud platform operators retain veto authority, exercise discretion, and introduce customer-specific factors when evaluating recommendations. This cooperative approach enables improved cost efficiency and more effective resource utilization without sacrificing reliability or operational confidence in cloud platform management.