Design of Scenario-based Application-optimized Data Replication Strategies through Genetic Programming

Syed Bokhari, Oliver Theel

Abstract

A distributed system is a paradigm which is indispensable to the current world due to countless requests with every passing second. Therefore, in distributed computing, high availability is very important. In a dynamic environment due to the scalability and complexity of the resources and components, systems are fault-prone because millions of computing devices are connected to each other via communication links. Distributed systems allow many users to access shared computing resources which makes faults inevitable. Replication plays its role in masking failures in order to achieve a fault-tolerant distributed environment. Data replication is an appropriate means to provide highly available data access operations at relatively low operation costs. Although there are several contemporary data replication strategies being used, the question still stands which strategy is the best for a given scenario or application class assuming a certain workload, its distribution across a network, availability of the individual replicas, and cost of the access operations. In this regard, research focuses on analysis, simulation, and machine learning approaches to automatically identify and design such replication strategies that are optimized for a given application scenario based on predefined constraints and properties exploiting a so-called voting structure.

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Paper Citation


in Harvard Style

Bokhari S. and Theel O. (2020). Design of Scenario-based Application-optimized Data Replication Strategies through Genetic Programming.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 120-129. DOI: 10.5220/0008955301200129


in Bibtex Style

@conference{icaart20,
author={Syed Bokhari and Oliver Theel},
title={Design of Scenario-based Application-optimized Data Replication Strategies through Genetic Programming},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={120-129},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008955301200129},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Design of Scenario-based Application-optimized Data Replication Strategies through Genetic Programming
SN - 978-989-758-395-7
AU - Bokhari S.
AU - Theel O.
PY - 2020
SP - 120
EP - 129
DO - 10.5220/0008955301200129