Conference：51st International Conference on
Very Large Data Bases
CCF level：CCF A
Categories：Database/Data Mining/Content Retrieval
Year：2025
Num：4
Conference time：London, United Kingdom - September 1-5, 2025
1
Title:
Seer: Accelerating Blockchain Transaction Execution by Fine-Grained Branch Prediction
Seer：通过细粒度分支预测加速区块链交易执行
Authors:****
Abstract:****
Increasingly popular decentralized applications (dApps) with com-Increasingly popular decentralized applications (dApps) with complex application logic incur significant overhead for executing smart contract transactions, which greatly limits public blockchain perfor-contract transactions, which greatly limits public blockchain performance. Pre-executing transactions off the critical path can mitigate substantial I/O and computation costs during execution. However, pre-execution does not yield any state transitions, rendering the sys-pre-execution does not yield any state transitions, rendering the system state inconsistent with actual execution. This inconsistency can lead to deviations in pre-execution paths when processing smart contracts with multiple state-related branches, thus diminishing pre-execution effectiveness. In this paper, we develop Seer, a novel public blockchain execution engine that incorporates fine-grained branch prediction to fully exploit pre-execution effectiveness. Seer predicts state-related branches using a two-level prediction ap-predicts state-related branches using a two-level prediction approach, reducing inconsistent execution paths more efficiently than executing all possible branches. To enable effective reuse of pre-executing all possible branches. To enable effective reuse of preexecution results, Seer employs checkpoint-based fast-path execu-execution results, Seer employs checkpoint-based fast-path execution, enhancing transaction execution for both successful and unsuc-tion, enhancing transaction execution for both successful and unsuccessful predictions. Evaluations with realistic blockchain workloads demonstrate that Seer delivers an average of 27.7 × transaction-level speedup and an overall 20.6 × speedup in the execution phase over vanilla Ethereum, outperforming existing blockchain execution acceleration solutions.
日益流行的去中心化应用程序（dApps）具有复杂的应用逻辑，这导致执行智能合约交易时产生显著的开销，从而极大地限制了公共区块链的性能。在关键路径之外预先执行交易可以显著降低执行过程中的I/O和计算成本。然而，预执行不会产生任何状态转换，导致系统状态与实际执行状态不一致。这种不一致性可能导致在处理具有多个状态相关分支的智能合约时，预执行路径出现偏差，从而降低预执行的有效性。在本文中，我们开发了一种名为 Seer 的新型公共区块链执行引擎，该引擎集成了细粒度分支预测功能，以充分发挥预执行的有效性。Seer 使用两级预测方法预测状态相关分支，比执行所有可能的分支更有效地减少不一致的执行路径。为了有效重用预执行结果，Seer 采用基于检查点的快速路径执行，从而提高了成功和不成功预测情况下的交易执行效率。使用真实的区块链工作负载进行的评估表明，Seer 在交易层面平均实现了 27.7 倍的加速，在执行阶段整体实现了 20.6 倍的加速，优于现有的区块链执行加速方案。
Pdf下载链接:
https://www.vldb.org/pvldb/vol18/p822-xiao.pdf
2
Title:
Falcon: Advancing Asynchronous BFT Consensus for Lower Latency and Enhanced Throughput
Falcon：推进异步BFT共识，实现更低延迟和更高吞吐量
Authors:****
Abstract:****
Asynchronous Byzantine Fault Tolerant (BFT) consensus protocols have garnered significant attention with the rise of blockchain technology. A typical asynchronous protocol is designed by executing sequential instances of the Asynchronous Common Sub-seQuence (ACSQ). The ACSQ protocol consists of two primary components: the Asynchronous Common Subset (ACS) protocol and a block sorting mechanism, with the ACS protocol comprising two stages: broadcast and agreement. However, current protocols encounter three critical issues: high latency arising from the execution of the agreement stage, latency instability due to the integral-sorting mechanism, and reduced throughput caused by block discarding. To address these issues, we propose Falcon, an asynchronous BFT protocol that achieves low latency and enhanced throughput. Falcon introduces a novel broadcast protocol, Graded Broadcast (GBC), which enables a block to be included in the ACS set directly, bypassing the agreement stage and thereby reducing latency. To ensure safety, Falcon incorporates a new binary agreement protocol called Asymmetrical Asynchronous Binary Agreement (AABA), designed to complement GBC. Additionally, Falcon employs a partial-sorting mechanism, allowing continuous rather than simultaneous block committing, enhancing latency stability. Finally, we incorporate an agreement trigger that, before its activation, enables nodes to wait for more blocks to be delivered and committed, thereby boosting throughput. We conduct a series of experiments to evaluate Falcon, demonstrating its superior performance.
随着区块链技术的兴起，异步拜占庭容错（BFT）共识协议受到了广泛关注。典型的异步协议通过执行异步公共子序列（ACSQ）的顺序实例来实现。ACSQ 协议包含两个主要组件：异步公共子集（ACS）协议和区块排序机制，其中 ACS 协议又分为两个阶段：广播和协商。然而，目前的协议存在三个关键问题：协商阶段执行导致的延迟过高、整体排序机制导致的延迟不稳定以及区块丢弃导致的吞吐量降低。为了解决这些问题，我们提出了 Falcon，一种低延迟、高吞吐量的异步 BFT 协议。Falcon 引入了一种新颖的广播协议------分级广播（GBC），该协议允许区块直接包含在 ACS 集合中，绕过协商阶段，从而降低延迟。为了确保安全性，Falcon 集成了一种名为非对称异步二元协商（AABA）的新型二元协商协议，该协议旨在与 GBC 协同工作。此外，Falcon 采用部分排序机制，允许连续而非同时提交区块，从而增强延迟稳定性。最后，我们引入了一个协商触发器，在触发器激活之前，节点可以等待更多区块的传输和提交，从而提高吞吐量。我们进行了一系列实验来评估 Falcon，结果表明其性能优越。
Pdf下载链接: