Multi-Objective Optimization Models for Performance, Energy, and Security in Hybrid Cloud Infrastructures

Abstract

Hybrid clouds spanning on-premises; edge; and multiple public providers demand orchestration that balances conflicting goals: low latency and high throughput; energy and carbon reduction; and rigorous security. This work frames resource provisioning; workload placement; and autoscaling as a multi-objective optimization problem under uncertainty. We model application components (VMs/containers/functions) with performance SLOs; security postures; and energy carbon profiles tied to time-varying grid intensity and datacenter PUE. Objectives minimize end-to-end latency and SLO violations; total energy and carbon cost; and security risk (e.g.; exposure time; vulnerability risk scores; data-in-motion footprint); while respecting budget; data-sovereignty; and trust-zone constraints. Solution strategies combine exact formulations (MILP for small horizons) with scalable heuristics and metaheuristics (e.g.; NSGA-II/MOEA-D) to approximate the Pareto frontier; and a model-predictive layer that adapts to demand; spot price volatility; and carbon signals. Robust and risk-aware variants incorporate chance constraints and CVaR to hedge against workload and failure uncertainty. We integrate zero-trust placement rules; encryption overhead; and privacy controls (federated learning/differential privacy) as first-class constraints; and co-optimize network paths to limit cross-cloud data exfiltration risk. A learning-augmented scheduler uses surrogate models for queueing delays and power draw to accelerate search at runtime. The resulting policies enable operators to trade milliseconds of tail latency for double-digit energy/carbon savings or reduced attack surface; making decisions transparent via Pareto sets and what-if analysis. The framework generalizes across microservices; data pipelines; and AI inference; and can plug into Kubernetes-centric control planes