{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/consistent-hashing-with-bounded-loads","title":"Consistent Hashing with Bounded Loads","arxiv_id":"1608.01350","date":"2016-08-03","proceeding":null,"authors":["Vahab Mirrokni","Mikkel Thorup","Morteza Zadimoghaddam"],"abstract":"Designing algorithms for balanced allocation of clients to servers in dynamic settings is a challenging problem for a variety of reasons. Both servers and clients may be added and/or removed from the system periodically, and the main objectives of allocation algorithms are: the uniformity of the allocation, and the number of moves after adding or removing a server or a client. The most popular solution for our dynamic settings is Consistent Hashing. However, the load balancing of consistent hashing is no better than a random assignment of clients to servers, so with $n$ of each, we expect many servers to be overloaded with $\\Theta(\\log n/ \\log\\log n)$ clients. In this paper, with $n$ clients and $n$ servers, we get a guaranteed max-load of 2 while only moving an expected constant number of clients for each update. We take an arbitrary user specified balancing parameter $c=1+\\epsilon>1$. With $m$ balls and $n$ bins in the system, we want no load above $\\lceil cm/n\\rceil$. Meanwhile we want to bound the expected number of balls that have to be moved when a ball or server is added or removed. Compared with general lower bounds without capacity constraints, we show that in our algorithm when a ball or bin is inserted or deleted, the expected number of balls that have to be moved is increased only by a multiplicative factor $O({1\\over \\epsilon^2})$ for $\\epsilon \\le 1$ (Theorem 4) and by a factor $1+O(\\frac{\\log c}c)$ for $\\epsilon\\ge 1$ (Theorem 3). Technically, the latter bound is the most challenging to prove. It implies that we for superconstant $c$ only pay a negligible cost in extra moves. We also get the same bounds for the simpler problem where we instead of a user specified balancing parameter have a fixed bin capacity $C$ for all bins.","url_abs":"http://arxiv.org/abs/1608.01350v3","url_pdf":"http://arxiv.org/pdf/1608.01350v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"consistent-hashing-with-bounded-loads","repo_url":"https://github.com/buraksezer/consistent","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}