research-article

Semantic-based QoS management in cloud systems

Authors:

Dimitrios Kourtesis,

Jose María Alvarez-Rodríguez,

Iraklis ParaskakisAuthors Info & Claims

Future Generation Computer Systems, Volume 32, Issue C

Pages 307 - 323

Published: 01 March 2014 Publication History

Abstract

Cloud Computing and Service Oriented Architectures have seen a dramatic increase of the amount of applications, services, management platforms, data, etc. gaining momentum for the necessity of new complex methods and techniques to deal with the vast heterogeneity of data sources or services. In this sense Quality of Service (QoS) seeks for providing an intelligent environment of self-management components based on domain knowledge in which cloud components can be optimized easing the transition to an advanced governance environment. On the other hand, semantics and ontologies have emerged to afford a common and standard data model that eases the interoperability, integration and monitoring of knowledge-based systems. Taking into account the necessity of an interoperable and intelligent system to manage QoS in cloud-based systems and the emerging application of semantics in different domains, this paper reviews the main approaches for semantic-based QoS management as well as the principal methods, techniques and standards for processing and exploiting diverse data providing advanced real-time monitoring services. A semantic-based framework for QoS management is also outlined taking advantage of semantic technologies and distributed datastream processing techniques. Finally a discussion of existing efforts and challenges is also provided to suggest future directions. We review the concept of Quality of Service in Cloud and Service Oriented Computing.We review the use of Semantics in Cloud and Service Oriented Computing.We review the existing techniques to deal with Big Data.We propose a Lambda Architecture based on Semantics and Big Data.We discuss and outline future challenges in semantic-based QoS management.

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cover image Future Generation Computer Systems

Future Generation Computer Systems Volume 32, Issue C

March 2014

347 pages

ISSN:0167-739X

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Elsevier Science Publishers B. V.

Netherlands

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Published: 01 March 2014

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Cited By

(2018)Improving scheduling efficiency by probabilistic execution time model in cloud environmentsInternational Journal of Networking and Virtual Organisations10.1504/IJNVO.2018.09365118:4(307-322)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1504/IJNVO.2018.093651
Fan MSun JZhou BChen M(2016)The Smart Health Initiative in ChinaJournal of Medical Systems10.1007/s10916-015-0416-y40:3(1-17)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1007/s10916-015-0416-y
Singh SChana I(2016)Cloud resource provisioningKnowledge and Information Systems10.1007/s10115-016-0922-349:3(1005-1069)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s10115-016-0922-3
Mao CChen JTowey DChen JXie X(2015)Search-based QoS ranking prediction for web services in cloud environmentsFuture Generation Computer Systems10.1016/j.future.2015.01.00850:C(111-126)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.future.2015.01.008
Colomo-Palacios RStantchev VRodríguez-González A(2014)Special issue on exploiting semantic technologies with particularization on linked data over grid and cloud architecturesFuture Generation Computer Systems10.5555/2748143.274837432:C(260-262)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.5555/2748143.2748374
Javanmardi SShojafar MShariatmadari SAbawajy JSinghal M(2014)PGSW-OSThe Journal of Supercomputing10.1007/s11227-014-1221-y69:2(955-975)Online publication date: 1-Aug-2014
https://dl.acm.org/doi/10.1007/s11227-014-1221-y

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