Local Rendezvous Hashing: Bounded Loads and Minimal Churn via Cache-Local Candidates
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908737383432192 |
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| author | Guan, Yongjie |
| author_facet | Guan, Yongjie |
| contents | Consistent hashing is fundamental to distributed systems, but ring-based schemes can exhibit high peak-to-average load ratios unless they use many virtual nodes, while multi-probe methods improve balance at the cost of scattered memory accesses. This paper introduces Local Rendezvous Hashing (LRH), which preserves a token ring but restricts Highest Random Weight (HRW) selection to a cache-local window of C distinct neighboring physical nodes. LRH locates a key by one binary search, enumerates exactly C distinct candidates using precomputed next-distinct offsets, and chooses the HRW winner (optionally weighted). Lookup cost is O(log|R| + C). Under fixed-topology liveness changes, fixed-candidate filtering remaps only keys whose original winner is down, yielding zero excess churn. In a benchmark with N=5000, V=256 (|R|=1.28M), K=50M and C=8, LRH reduces Max/Avg load from 1.2785 to 1.0947 and achieves 60.05 Mkeys/s, about 6.8x faster than multi-probe consistent hashing with 8 probes (8.80 Mkeys/s) while approaching its balance (Max/Avg 1.0697). A microbenchmark indicates multi-probe assignment is dominated by repeated ring searches and memory traffic rather than probe-generation arithmetic. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_23434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Local Rendezvous Hashing: Bounded Loads and Minimal Churn via Cache-Local Candidates Guan, Yongjie Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Performance Consistent hashing is fundamental to distributed systems, but ring-based schemes can exhibit high peak-to-average load ratios unless they use many virtual nodes, while multi-probe methods improve balance at the cost of scattered memory accesses. This paper introduces Local Rendezvous Hashing (LRH), which preserves a token ring but restricts Highest Random Weight (HRW) selection to a cache-local window of C distinct neighboring physical nodes. LRH locates a key by one binary search, enumerates exactly C distinct candidates using precomputed next-distinct offsets, and chooses the HRW winner (optionally weighted). Lookup cost is O(log|R| + C). Under fixed-topology liveness changes, fixed-candidate filtering remaps only keys whose original winner is down, yielding zero excess churn. In a benchmark with N=5000, V=256 (|R|=1.28M), K=50M and C=8, LRH reduces Max/Avg load from 1.2785 to 1.0947 and achieves 60.05 Mkeys/s, about 6.8x faster than multi-probe consistent hashing with 8 probes (8.80 Mkeys/s) while approaching its balance (Max/Avg 1.0697). A microbenchmark indicates multi-probe assignment is dominated by repeated ring searches and memory traffic rather than probe-generation arithmetic. |
| title | Local Rendezvous Hashing: Bounded Loads and Minimal Churn via Cache-Local Candidates |
| topic | Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Performance |
| url | https://arxiv.org/abs/2512.23434 |