Simple Load Balancing for Distributed Hash Tables

By John W. Byers, Jeffrey Considine, Michael M. Mitzenmacher. International Workshop on Peer-to-Peer Systems, 2003

325 citations recorded by the source corpus.

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Mercury: supporting scalable multi-attribute range queries (2004, background): “posed in [21] assumes that the load skew results from the standard deviation of random hash function distributions, which is typically very small (O(log n), see [1]).”
Parameter Server for Distributed Machine Learning (2013, methodology): “We use a distributed hash table [9] to allow new server nodes to be dynamically inserted into the set at any time.”
Load balancing in dynamic structured P2P systems (2004, methodology): “Several solutions have been proposed to address the load balancing problem [2], [5], [6].”
Proposal Scaling Distributed Machine Learning with System and Algorithm Co-design (2016, unknown): “Similar to a conventional distributed hash table [24, 118], our PS partitions keys by inserting both keys and server node IDs into the hash ring (Figure 3.”
Balanced allocations: the weighted case (2007, background): “…balls and bins Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial advantage and that copies bear this notice and the full citation on the .rst page.”