On Scalable Integrity Checking for Secure Cloud Disks

TL;DR

This paper analyzes the performance costs of Merkle hash trees in cloud storage, introduces Dynamic Merkle Trees (DMTs) to reduce overhead, and demonstrates up to 2.2x improvements in throughput and latency.

Contribution

It presents DMTs, an optimized Merkle tree structure that significantly reduces performance overheads in secure cloud storage systems.

Findings

DMTs achieve up to 2.2x throughput and latency improvements.

Performance overheads of hash trees are characterized in realistic settings.

DMTs exploit workload patterns for efficiency.

Abstract

Merkle hash trees are the standard method to protect the integrity and freshness of stored data. However, hash trees introduce additional compute and I/O costs on the I/O critical path, and prior efforts have not fully characterized these costs. In this paper, we quantify performance overheads of storage-level hash trees in realistic settings. We then design an optimized tree structure called Dynamic Merkle Trees (DMTs) based on an analysis of root causes of overheads. DMTs exploit patterns in workloads to deliver up to a 2.2x throughput and latency improvement over the state of the art. Our novel approach provides a promising new direction to achieve integrity guarantees in storage efficiently and at scale.