A Secure and Scalable Model for Heterogeneous Cloud-Based Computing Infrastructures

Abstract

Heterogeneous cloud-based computing infrastructures spanning public clouds, private data centers, and edge sites must deliver strong security guarantees without sacrificing elasticity or performance. This paper proposes a secure and scalable reference model that unifies control and data planes across diverse substrates. The model integrates: (i) a Zero-Trust posture with continuous verification, workload identity (SPIFFE/SPIRE), and fine-grained micro-segmentation; (ii) confidential computing and hardware-rooted attestation to protect code and data in use; (iii) a policy-as-code layer that compiles high-level security and compliance intents into enforceable controls (eBPF/service-mesh) at runtime; and (iv) an SLO-aware orchestration tier that couples autoscaling and placement decisions with risk scores derived from streaming telemetry. A portable abstraction for multi-cloud networking ensures least-privilege connectivity via identity-based overlay meshes, while a data governance fabric enforces encryption, lineage, and residency constraints. We further introduce a resilience loop observe, assess, adapt that combines anomaly detection with safe rollback and blast-radius containment. A prototype implementation demonstrates linear horizontal scaling of stateless services, sub-second policy propagation, and low enforcement overhead (<3% median). In fault injection and red-team scenarios, the model reduces lateral-movement paths and mean time to remediate by coordinating identity, network, and workload controls. The result is an end-to-end architecture that enables secure-by-default operations, consistent compliance, and predictable performance for mixed workloads across heterogeneous clouds and edges