Benchmarking Analytical Query Processing in Intel SGXv2

TL;DR

This paper evaluates the performance of analytical query processing algorithms inside Intel SGXv2, revealing potential and challenges, and proposing optimizations to improve enclave execution efficiency.

Contribution

It provides the first comprehensive evaluation of query algorithms in SGXv2 and introduces optimizations to enhance performance within enclaves.

Findings

Radix joins and SIMD scans perform well inside SGXv2

Hardware and software differences impact enclave performance

Proposed optimizations significantly reduce overheads

Abstract

Trusted Execution Environments (TEEs), such as Intel's Software Guard Extensions (SGX), are increasingly being adopted to address trust and compliance issues in the public cloud. Intel SGX's second generation (SGXv2) addresses many limitations of its predecessor (SGXv1), offering the potential for secure and efficient analytical cloud DBMSs. We assess this potential and conduct the first in-depth evaluation study of analytical query processing algorithms inside SGXv2. Our study reveals that, unlike SGXv1, state-of-the-art algorithms like radix joins and SIMD-based scans are a good starting point for achieving high-performance query processing inside SGXv2. However, subtle hardware and software differences still influence code execution inside SGX enclaves and cause substantial overheads. We investigate these differences and propose new optimizations to bring the performance inside enclaves on par with native code execution outside enclaves.