Query Cost Model Calibration in Confidential Virtual Machines (opens in new tab)

With the growing adoption of Confidential Computing, running databases in confidential virtual machines (CVMs) such as AMD SEV-SNP has become an attractive way to protect sensitive cloud data with minimal changes to legacy DBMSs. However, analytical queries in such CVMs often suffer substantial overhead, and prior database work has largely stopped at benchmarking these slowdowns rather than optimizing them. We show that this problem stems from a...