A User Threat-Driven Multi-Objective Adaptive VM Allocation Framework for Cloud Security

Summary

Researchers propose an adaptive VM allocation framework that uses user threat assessment to reduce side-channel attack risk in cloud environments. They analyze a large-scale Microsoft Azure dataset to guide security-aware initial placement, then apply a non-dominated sorting snow ablation optimizer to dynamically migrate VMs and disrupt co-residency. Experiments report up to 25% lower co-residency risk, up to 20% lower defensive costs, and up to 30% better resource utilization compared with prior adaptive cyber defense approaches.