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Enabling Workload Engineering in Edge, Fog, and Cloud Computing through OpenStack-based Middleware

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dc.contributor.author Merlino G.
dc.contributor.author Dautov R.
dc.contributor.author Distefano S.
dc.contributor.author Bruneo D.
dc.date.accessioned 2020-01-15T21:47:52Z
dc.date.available 2020-01-15T21:47:52Z
dc.date.issued 2019
dc.identifier.issn 1533-5399
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/156020
dc.description.abstract © 2019 Association for Computing Machinery. To enable and support smart environments, a recent ICT trend promotes pushing computation from the remote Cloud as close to data sources as possible, resulting in the emergence of the Fog and Edge computing paradigms. Together with Cloud computing, they represent a stacked architecture, in which raw datasets are first pre-processed locally at the Edge and then vertically offloaded to the Fog and/or the Cloud. However, as hardware is becoming increasingly powerful, Edge devices are seen as candidates for offering data processing capabilities, able to pool and share computing resources to achieve better performance at a lower network latency-a pattern that can be also applied to Fog nodes. In these circumstances, it is important to enable efficient, intelligent, and balanced allocation of resources, as well as their further orchestration, in an elastic and transparent manner. To address such a requirement, this article proposes an OpenStack-based middleware platform through which resource containers at the Edge, Fog, and Cloud levels can be discovered, combined, and provisioned to end users and applications, thereby facilitating and orchestrating offloading processes. As demonstrated through a proof of concept on an intelligent surveillance system, by converging the Edge, Fog, and Cloud, the proposed architecture has the potential to enable faster data processing, as compared to processing at the Edge, Fog, or Cloud levels separately. This also allows architects to combine different offloading patterns in a flexible and fine-grained manner, thus providing new workload engineering patterns. Measurements demonstrated the effectiveness of such patterns, even outperforming edge clusters.
dc.relation.ispartofseries ACM Transactions on Internet Technology
dc.subject Big data
dc.subject Cloud computing
dc.subject Edge
dc.subject Fog
dc.subject IoT
dc.subject Offloading
dc.subject Smart environment
dc.subject Stack4Things
dc.title Enabling Workload Engineering in Edge, Fog, and Cloud Computing through OpenStack-based Middleware
dc.type Article
dc.relation.ispartofseries-issue 2
dc.relation.ispartofseries-volume 19
dc.collection Публикации сотрудников КФУ
dc.source.id SCOPUS15335399-2019-19-2-SID85065748278

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  • Публикации сотрудников КФУ Scopus [20180]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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