Hash & Adjust: Competitive Demand-Aware Consistent Hashing

TL;DR

This paper introduces H&A, a demand-aware consistent hashing algorithm that adaptively optimizes storage and access times in distributed systems, with proven performance guarantees and empirical evaluation.

Contribution

It presents the first demand-aware consistent hashing algorithm with provable competitiveness and demonstrates its effectiveness through empirical analysis.

Findings

H&A achieves constant competitiveness with theoretical guarantees.

H&A improves storage utilization and reduces access times.

Empirical results validate the theoretical advantages of H&A.

Abstract

Distributed systems often serve dynamic workloads and resource demands evolve over time. Such a temporal behavior stands in contrast to the static and demand-oblivious nature of most data structures used by these systems. In this paper, we are particularly interested in consistent hashing, a fundamental building block in many large distributed systems. Our work is motivated by the hypothesis that a more adaptive approach to consistent hashing can leverage structure in the demand, and hence improve storage utilization and reduce access time. We initiate the study of demand-aware consistent hashing. Our main contribution is H&A, a constant-competitive online algorithm (i.e., it comes with provable performance guarantees over time). H&A is demand-aware and optimizes its internal structure to enable faster access times, while offering a high utilization of storage. We further evaluate H&A empirically.