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Hash & Adjust: Competitive Demand-Aware Consistent Hashing

Authors Arash Pourdamghani , Chen Avin , Robert Sama , Maryam Shiran , Stefan Schmid

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ACM Subject Classification
  • Theory of computation → Online algorithms
  • Theory of computation → Data structures design and analysis
Keywords
  • Consistent hashing
  • demand-awareness
  • online algorithms

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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.

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Arash Pourdamghani, Chen Avin, Robert Sama, Maryam Shiran, and Stefan Schmid. Hash & Adjust: Competitive Demand-Aware Consistent Hashing. In 28th International Conference on Principles of Distributed Systems (OPODIS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 324, pp. 24:1-24:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.OPODIS.2024.24

Author Details

Arash Pourdamghani
  • TU Berlin, Germany
Chen Avin
  • Ben-Gurion University of the Negev, Beersheba, Israel
Robert Sama
  • University of Vienna, Austria
Maryam Shiran
  • TU Berlin, Germany
Stefan Schmid
  • TU Berlin, Germany
  • Fraunhofer SIT, Berlin, Germany

Funding

This project has received funding from the European Research Council (ERC) under grant agreement No. 864228 (AdjustNet), 2020-2025.

Supplementary Materials

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