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On the Combination of Multi-Cloud and Network Coding for Cost-Efficient Storage in Industrial Applications

机译:多云与网络编码相结合在工业应用中实现经济高效的存储

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摘要

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.
机译:网络物理系统(CPS)和物联网(IoT)的采用使向所谓的工业4.0的演进成为可能。这些技术与云计算和人工智能一起,创造了新的商机。此外,一些工业应用需要立即做出决策,雾计算正逐渐成为解决此类需求的有希望的解决方案。为了实现具有成本效益的系统,我们建议利用现货实例的优势,即云提供商提供的一项新服务,该服务以较低的价格提供资源。这些实例的主要缺点是它们不能确保服务的连续性,并且可能会遭受中断的困扰。结合了雾和多云部署以及网络编码(NC)技术的体系结构,可以保证云环境所需的容错能力,还可以减少提供可靠服务所需的冗余数据量。在本文中,我们分析了基于多云基础架构的NC如何实际帮助降低存储成本并提高工业应用程序的资源效率。成本分析是使用真实的AWS EC2现货实例价格进行的,并且作为补充,使用了基于基于有限马尔可夫链的模型的价格,这些模型是根据实际测量得出的。我们已经根据不同的参数分析了整个系统的成本,结果表明,由于存储成本的巨大影响,寻求最小化存储的配置可降低成本。

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