面向动态工作负载的认证数据结构
Authenticated Data Structures for Dynamic Workloads
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中文总结 AI 辅助
针对动态工作负载的访问频率变化问题，提出哈夫曼-默克尔树（HMT）这一认证数据结构，经实验对比，其在哈希操作量和证明长度上均优于以太坊的MPT与UBT。
中文摘要 AI 辅助
我们提出了哈夫曼-默克尔树（Huffman-Merkle Tree，简称HMT），这是一种面向动态工作负载的认证数据结构（Authenticated Data Structure，简称ADS），其中项目的访问频率可能存在差异，且访问频率会随时间变化。ADS支持针对大型可变状态的短承诺证明项目成员关系，其应用场景包括可验证存储、互联网透明度服务以及区块链。此前，无论是在理论还是实践层面，均未充分解决在访问频率持续变化的情况下优化ADS性能的问题。HMT通过两种互补机制应对动态变化的访问偏斜：第一种是基于哈夫曼编码的默克尔树布局，具有支持访问频率演化的新型扩展；第二种是弹性分层机制，将项目划分为不同树层，如热层和冷层，并在它们之间进行自适应迁移。该方法的核心思路是将频繁访问的项目放置在靠近根节点的位置，而将访问频率较低的项目分配到逐渐更大、更深的树中，从而降低整体频率加权访问成本。我们的方案设计可扩展至千兆字节级数据，覆盖数百万个项目。为高效处理动态性，布局更新以批量方式应用，访问频率使用计数-最小概型（count-min sketch）进行跟踪，系统采用分层晋升缓存并探索多种分层迁移策略。我们实现了HMT，并在真实数据上将其与以太坊的默克尔帕特里夏树（Merkle Patricia Trie，简称MPT）ADS及其拟议替代方案统一二叉树（Unified Binary Tree，简称UBT）进行比较。我们的评估考虑了两个指标：每次更新的哈希操作量和访问加权成员关系证明大小，后者同时反映了项目访问成本和频率。我们发现，最优HMT策略的平均哈希操作量分别比MPT和UBT少约2.4倍和0.34倍，证明长度分别短0.18倍和0.55倍。
英文摘要
We introduce the Huffman-Merkle Tree (HMT), an authenticated data structure (ADS) for dynamic workloads where items may differ in access frequencies, and access frequencies can change over time. An ADS allows proving item membership against a short commitment to a large mutable state, with applications including verifiable storage, Internet transparency services, and blockchains. Optimizing ADS performance under continuously changing access frequencies has not been fully addressed before, neither in theory nor in practice. HMT addresses dynamically changing access skew through two complementary mechanisms. The first is a Huffman-coding-based Merkle-tree layout, with a novel extension to support evolving access frequencies. The second is an elastic tiering regime that partitions items across separate trees, such as hot and cold tiers, with adaptive migration between them. The key insight in this approach is to place frequently accessed items closer to the root, while assigning less frequently accessed items to progressively larger and deeper trees. This reduces the overall frequency-weighted access cost. Our scheme is designed to scale to gigabytes of data spanning millions of items. To handle dynamism efficiently, layout updates are applied in batches, access frequencies are tracked using a count-min sketch, and the system employs a tier-promotion cache while exploring multiple tier-migration policies. We implement HMT and compare it on real-world data with Ethereum's Merkle Patricia Trie (MPT) ADS and its proposed replacement, the Unified Binary Tree (UBT). Our evaluation considers two metrics: the amount of hashing per update and access-weighted membership-proof size. The latter captures both item access cost and frequency. We find that the best HMT policy uses about 2.4x and 0.34x less average hash operations than MPT and UBT respectively, and has 0.18x and 0.55x shorter proofs.
