A recent approach to building consensus protocols on top of Directed Acyclic Graphs (DAGs) shows much promise due to its simplicity and stable throughput. However, as each node in the DAG typically includes a linear number of references to the nodes in the previous round, prior DAG protocols only scale up to a certain point when the overhead of maintaining the graph becomes the bottleneck. To enable large-scale deployments of DAG-based protocols, we propose a sparse DAG architecture, where each node includes only a constant number of references to random nodes in the previous round. We present a sparse version of Bullshark -- one of the most prominent DAG-based consensus protocols -- and demonstrate its improved scalability. Remarkably, unlike other protocols that use random sampling to reduce communication complexity, we manage to avoid sacrificing resilience: the protocol can tolerate up to $f<n/3$ Byzantine faults (where $n$ is the number of participants), same as its less scalable deterministic counterpart. The proposed ``sparse'' methodology can be applied to any protocol that maintains disseminated system updates and causal relations between them in a graph-like structure. Our simulations show that the considerable reduction of transmitted metadata in sparse DAGs results in more efficient network utilization and better scalability.

翻译：近期一种基于有向无环图构建共识协议的方法因其简洁性和稳定的吞吐量展现出巨大潜力。然而，由于DAG中每个节点通常包含对前一轮节点的线性数量引用，当维护图结构的开销成为瓶颈时，现有的DAG协议仅能扩展到一定程度。为实现基于DAG协议的大规模部署，我们提出一种稀疏DAG架构，其中每个节点仅包含对前一轮随机节点的恒定数量引用。我们展示了Bullshark（最著名的基于DAG的共识协议之一）的稀疏版本，并论证了其改进的可扩展性。值得注意的是，与使用随机采样降低通信复杂度的其他协议不同，我们成功避免了牺牲容错性：该协议可容忍多达$f<n/3$的拜占庭故障（其中$n$为参与者数量），与其可扩展性较差的确定性对应协议保持相同水平。所提出的“稀疏”方法可应用于任何在类图结构中维护传播系统更新及其因果关系的协议。我们的模拟表明，稀疏DAG中传输元数据的大幅减少带来了更高效的网络利用率和更优的可扩展性。