Transaction ordering attacks extract billions of dollars annually from decentralized finance users in the form of Maximal Extractable Value (MEV). Byzantine Fault-Tolerant (BFT) consensus protocols guarantee total order but place no constraint on how that order is chosen, leaving the door open for adversarial reordering. Batch-order-fairness (batch-OF) protocols close this gap, but existing designs pay a steep performance price for this guarantee. Leader-based protocols such as Themis concentrate all fairness decisions at a single replica, while recent DAG-based proposals FairDAG and DAG of DAGs (DoD) force their fairness layer into strictly serial execution despite running on multi-proposer DAGs. We present Herring, the first $γ$-batch-OF DAG BFT protocol whose fairness layer parallelizes the dominant graph construction cost across committed subdags. Herring combines post-consensus graph construction with explicit missing edge resolution piggybacked on the DAG's reliable broadcast layer, a pairing that turns fair ordering from a per-round serial bottleneck into a CPU-bound task. We also uncover previously unreported liveness vulnerabilities in both FairDAG-RL and DoD that a malicious client can trigger to halt the fairness layer indefinitely, and propose patches that we integrate into our reimplementations. We implement Herring on top of the Rust implementation of Narwhal \& Tusk and evaluate it against FairDAG-RL, DoD-W, and Themis. Herring tracks the throughput of Narwhal \& Tusk closely up to roughly $10{,}000$\,tx/s, achieves roughly $90\%$ higher saturation throughput than FairDAG-RL and $100\%$ higher than DoD-W, and substantially reduces execution latency at saturation.

翻译：交易排序攻击每年以最大可提取价值（MEV）的形式从去中心化金融用户手中攫取数十亿美元。拜占庭容错（BFT）共识协议保证了全序，但未对排序的选择方式施加任何约束，从而为敌意重排序留下了可乘之机。批次顺序公平性（batch-OF）协议弥补了这一缺陷，但现有设计为此保证付出了高昂的性能代价。基于领导者的协议（如Themis）将所有公平性决策集中在单一副本上，而近期基于DAG的方案FairDAG与DAG of DAGs（DoD）则强制其公平性层在拥有多提议者DAG的情况下仍严格串行执行。我们提出Herring，这是首个γ-batch-OF DAG BFT协议，其公平性层将主导图构建成本并行化分摊至已提交的子DAG上。Herring将共识后图构建与显式缺失边解析技术（附着于DAG的可靠广播层）相结合，这一组合将公平排序从每轮串行瓶颈转变为受CPU限制的任务。我们还发现了FairDAG-RL和DoD中此前未被报告的活性漏洞：恶意客户端可触发该漏洞无限期阻塞公平性层，并提出了集成于我们重实现方案中的补丁。我们在Narwhal & Tusk的Rust实现基础上实现了Herring，并针对FairDAG-RL、DoD-W和Themis进行评估。Herring在吞吐量上紧密追踪Narwhal & Tusk（最高约10,000笔交易/秒），饱和吞吐量较FairDAG-RL提升约90%，较DoD-W提升100%，并在饱和状态下显著降低了执行延迟。