On the combination of multi-cloud and network coding for cost-efficient storage in industrial applications
Keywords: 
Cost-efficiency
Data availability
Distributed storage
Industry 4.0
Multi-cloud
Network coding
Reliability
Spot instances
Issue Date: 
2019
Publisher: 
MDPI AG
ISSN: 
1424-8220
Note: 
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Citation: 
Peralta, G. (Goiuri); Garrido, P. (Pablo); Bilbao, J. (Josu); et al. "On the combination of multi-cloud and network coding for cost-efficient storage in industrial applications". Sensors. 19 (7), 2019, 1673
Abstract
The adoption of both Cyber–Physical Systems (CPSs) and the Internet-of-Things (IoT) has enabled the evolution towards the so-called Industry 4.0. These technologies, together with cloud computing and artificial intelligence, foster new business opportunities. Besides, several industrial applications need immediate decision making and fog computing is emerging as a promising solution to address such requirement. In order to achieve a cost-efficient system, we propose taking advantage from spot instances, a new service offered by cloud providers, which provide resources at lower prices. The main downside of these instances is that they do not ensure service continuity and they might suffer from interruptions. An architecture that combines fog and multi-cloud deployments along with Network Coding (NC) techniques, guarantees the needed fault-tolerance for the cloud environment, and also reduces the required amount of redundant data to provide reliable services. In this paper we analyze how NC can actually help to reduce the storage cost and improve the resource efficiency for industrial applications, based on a multi-cloud infrastructure. The cost analysis has been carried out using both real AWS EC2 spot instance prices and, to complement them, prices obtained from a model based on a finite Markov chain, derived from real measurements. We have analyzed the overall system cost, depending on different parameters, showing that configurations that seek to minimize the storage yield a higher cost reduction, due to the strong impact of storage cost.

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