Confidential computing is gaining traction in the cloud, driven by the increasing security and privacy concerns across various industries. Recent trusted hardware advancements introduce Confidential Virtual Machines (CVMs) to alleviate the programmability and usability challenges of the previously proposed enclave-based trusted computing technologies. CVM hardware extensions facilitate secure, hardware-isolated encrypted VMs, promoting programmability and easier deployment in cloud infrastructures. However, differing microarchitectural features, interfaces, and security properties among hardware vendors complicate the evaluation of CVMs for different use cases. Understanding the performance implications, functional limitations, and security guarantees of CVMs is a crucial step toward their adoption. This paper presents a detailed empirical analysis of two leading CVM technologies: AMD Secure Encrypted Virtualization-Secure Nested Paging (SEV-SNP) and Intel Trust Domain Extensions (TDX). We review their microarchitectural components and conduct a thorough performance evaluation across various aspects, including memory management, computational performance, storage and network stacks, and attestation primitives. We further present a security analysis through a trusted computing base (TCB) evaluation and Common Vulnerabilities and Exposures (CVE) analysis. Our key findings demonstrate, among others, the effect of CVMs on boot time, memory management and I/O, and identify inefficiencies in their context switch mechanisms. We further provide insights into the performance implications of CVMs and highlight potential room for improvement.

Masanori Misono

Dimitrios Stavrakakis

Confidential VMs Explained: An Empirical Analysis of AMD SEV-SNP and Intel TDX

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	3