DxHash：基于伪随机序列的可扩展一致性哈希
DxHash: A Scalable Consistent Hash Based on the Pseudo-Random Sequence
AI总结：
本文提出基于伪随机序列的可扩展一致性哈希算法DxHash及加权变体，在负载均衡、最小扰动等六项指标上全面优于现有方案，百万节点集群50%故障率下仍达每秒1330万次查询。
摘要：
一致性哈希（CH）在分布式数据库、云基础设施和对等网络等多个领域中，作为数据路由器和负载均衡器一直发挥着关键作用。然而，现有的一致性哈希算法往往无法同时满足负载均衡、最小扰动、无状态性、高查找速率、小内存占用和低更新开销等多项关键要求。为解决这些局限，我们提出了DxHash，一种基于伪随机序列的可扩展一致性哈希算法。为调整异构节点上的工作负载并增强灵活性，我们进一步提出了加权DxHash。通过全面评估，DxHash在全部六项要求上相较于最先进的替代方案均展现出显著改进。值得注意的是，即使在包含一百万个节点的集群中面临50%的故障率时，DxHash仍能保持出色的处理能力，每秒可处理高达1330万次查询。
英文摘要：
Consistent hashing (CH) has been pivotal as a data router and load balancer in diverse fields, including distributed databases, cloud infrastructure, and peer-to-peer networks. However, existing CH algorithms often fall short in simultaneously meeting various critical requirements, such as load balance, minimal disruption, statelessness, high lookup rate, small memory footprint, and low update overhead. To address these limitations, we introduce DxHash, a scalable consistent hashing algorithm based on pseudo-random sequences. To adjust workloads on heterogeneous nodes and enhance flexibility, we propose weighted DxHash. Through comprehensive evaluations, DxHash demonstrates substantial improvements across all six requirements compared to state-of-the-art alternatives. Notably, even when confronted with a 50% failure ratio in a cluster of one million nodes, DxHash maintains remarkable processing capabilities, handling up to 13.3 million queries per second.
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