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Authors: Abdullah Algarni and Daniel Kudenko

Affiliation: University of York, United Kingdom

ISBN: 978-989-758-220-2

Keyword(s): Machine Learning, Reinforcement Learning, Multiple Cloud Computing Storage, File Access Pattern.

Related Ontology Subjects/Areas/Topics: Agent Models and Architectures ; Agents ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Cloud Computing ; Computational Intelligence ; Data Engineering ; Databases and Data Security ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: Storing data on a single cloud storage service may cause several potential problems for the data owner such as service continuity, availability, performance, security, and the risk of vendor lock-in. A promising solution to tackle some of these issues is to distribute the data across multiple cloud storage services (MCSS). However, the distinguishing characteristics of different cloud providers, in terms of pricing schemes and service performance, make it difficult to optimise the cost and the performance concurrently on MCSS. This paper proposes a framework for automatically tuning the data distribution policies across MCSS from the client side based on file access patterns. The aim of this work is to optimise the average cost and the average service performance (mainly latency time) on MCSS. To achieve this goal, two different machine learning algorithms are used in this work: (1) supervised learning to predict file access patterns, and (2) reinforcement learning to control data dis tribution parameters based on the prediction of file access pattern. The framework was tested on a cloud storage emulator, where its was set to act like several common cloud storage services. The result of testing this framework shows a significant improvement in the cost and performance of storing data in multiple clouds, as compared to the commonly used uniform file distribution. (More)

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Paper citation in several formats:
Algarni A. and Kudenko D. (2017). Distribution Data Across Multiple Cloud Storage using Reinforcement Learning Method.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 431-438. DOI: 10.5220/0006124804310438

@conference{icaart17,
author={Abdullah Algarni and Daniel Kudenko},
title={Distribution Data Across Multiple Cloud Storage using Reinforcement Learning Method},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={431-438},
publisher={ScitePress},
organization={INSTICC},
doi={10.5220/0006124804310438},
isbn={978-989-758-220-2},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Distribution Data Across Multiple Cloud Storage using Reinforcement Learning Method
SN - 978-989-758-220-2
AU - Algarni A.
AU - Kudenko D.
PY - 2017
SP - 431
EP - 438
DO - 10.5220/0006124804310438

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