Research Article
A Framework for Performance Evaluation of Decentralized Eventual Consistency Algorithms
@ARTICLE{10.4108/eai.30-6-2017.152755, author={Mehdi Ahmed-Nacer and Pascal Urso}, title={A Framework for Performance Evaluation of Decentralized Eventual Consistency Algorithms}, journal={EAI Endorsed Transactions on Collaborative Computing}, volume={3}, number={11}, publisher={EAI}, journal_a={CC}, year={2017}, month={6}, keywords={Distributed Systems, Eventual Consistency, Operational Transformation, Commutative Replicated Data Types, Collaboration, Benchmark, Performance Analysis, Framework, Data Replication}, doi={10.4108/eai.30-6-2017.152755} }- Mehdi Ahmed-Nacer
Pascal Urso
Year: 2017
A Framework for Performance Evaluation of Decentralized Eventual Consistency Algorithms
CC
EAI
DOI: 10.4108/eai.30-6-2017.152755
Abstract
Eventual Consistency (EC) model is adopted by numerous large-scale distributed systems. To ensure performance and scalability, this model allows any replica to accept updates without remote synchronization. Nowadays, many EC algorithms are developed to control the behavior of the replicated data in the face of concurrent updates. Among them, those using a central server to order the updates, while others support the decentralization. In this paper, we focus on decentralized EC algorithms. Suitability of such algorithms under users and devices constraints such as execution time, memory requirements, messages size and quality of the result remains to be investigated under different conditions. Evaluate such algorithms in different context and under different parameters require a framework. In this paper, we propose a generic framework designed to evaluate diferent decentralized EC algorithms, in diferent context by controlling diferent parameters. Our framework provides a generic simulator that generates a runnable data following diferent parameters.
Copyright © 2017 Mehdi Ahmed-Nacer and Pascal Urso, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
