Efficient Renaming in Sequence CRDTs - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Parallel and Distributed Systems Année : 2022

Efficient Renaming in Sequence CRDTs

Résumé

To achieve high availability, large-scale distributed systems have to replicate data and to minimise coordination between nodes. For these purposes, literature and industry increasingly adopt Conflict-free Replicated Data Types (CRDTs) to design such systems. Conflict-free Replicated Data Types (CRDTs) are new specifications of existing data types, e.g., Set or Sequence. While CRDTs have the same behaviour as previous specifications in sequential executions, they actually shine in distributed settings as they natively support concurrent updates. To this end, CRDTs embed in their specification conflict resolution mechanisms. These mechanisms usually rely on identifiers attached to elements of the data structure to resolve conflicts in a deterministic and coordination-free manner. Identifiers have to comply with several constraints, such as being unique or belonging to a dense total order. These constraints may hinder the identifier size from being bounded. Identifiers hence tend to grow as the system progresses, which increases the overhead of CRDTs over time and leads to performance issues. To address this issue, we propose a novel Sequence CRDT which embeds a renaming mechanism. It enables nodes to reassign shorter identifiers to elements in an uncoordinated manner. Experimental results demonstrate that this mechanism decreases the overhead of the replicated data structure and eventually minimises it.
Fichier principal
Vignette du fichier
main.pdf (1.23 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03772633 , version 1 (08-09-2022)

Identifiants

Citer

Matthieu Nicolas, Gerald Oster, Olivier Perrin. Efficient Renaming in Sequence CRDTs. IEEE Transactions on Parallel and Distributed Systems, 2022, 33 (12), pp.3870-3885. ⟨10.1109/TPDS.2022.3172570⟩. ⟨hal-03772633⟩
80 Consultations
111 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More