Revisiting Consistent Hashing with Bounded Loads

TL;DR

This paper improves consistent hashing for distributed load balancing by addressing cascading overflow issues, leading to better load distribution and significantly fewer cache misses in real-world datasets.

Contribution

It introduces a novel consistent hashing method based on fast minwise hashing that overcomes cascading overflow, enhancing load balancing and efficiency.

Findings

Reduces cache misses by several magnitudes on real datasets

Theoretically proven to be superior and optimal in many senses

Significantly improves load balancing performance

Abstract

Dynamic load balancing lies at the heart of distributed caching. Here, the goal is to assign objects (load) to servers (computing nodes) in a way that provides load balancing while at the same time dynamically adjusts to the addition or removal of servers. One essential requirement is that the addition or removal of small servers should not require us to recompute the complete assignment. A popular and widely adopted solution is the two-decade-old Consistent Hashing (CH). Recently, an elegant extension was provided to account for server bounds. In this paper, we identify that existing methodologies for CH and its variants suffer from cascaded overflow, leading to poor load balancing. This cascading effect leads to decreasing performance of the hashing procedure with increasing load. To overcome the cascading effect, we propose a simple solution to CH based on recent advances in fast minwise hashing. We show, both theoretically and empirically, that our proposed solution is significantly superior for load balancing and is optimal in many senses. On the AOL search dataset and Indiana University Clicks dataset with real user activity, our proposed solution reduces cache misses by several magnitudes.