Core group placement: allocation and provisioning of heterogeneous resources

We present a theoretical and empirical study on a recently introduced combinatorial optimization problem, namely core group placement problem. The problem arises from real-world business requirements as part of resource allocation in cloud management. In particular, it focuses on the allocation and...

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Autor principal:	Serdar Kadıoğlu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2019
Materias:	Primary 68T01 Artificial Intelligence - General Secondary 90C11 Mixed integer programming 90C27 Combinatorial optimization 90C90 Applications of mathematical programming Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/6559e141d1124c1d8fee252cc9c669af
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spelling	oai:doaj.org-article:6559e141d1124c1d8fee252cc9c669af2021-12-02T05:01:12ZCore group placement: allocation and provisioning of heterogeneous resources2192-440610.1007/s13675-018-0095-9https://doaj.org/article/6559e141d1124c1d8fee252cc9c669af2019-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2192440621001179https://doaj.org/toc/2192-4406We present a theoretical and empirical study on a recently introduced combinatorial optimization problem, namely core group placement problem. The problem arises from real-world business requirements as part of resource allocation in cloud management. In particular, it focuses on the allocation and provisioning of a set of heterogeneous resources serving multiple customers each with different service-level agreements. There exist certain business rules that govern the application stemming from privacy, performance, and capacity requirements. From a theoretical point of view, we prove that the problem is intrinsically hard, yet, from a practical point of view, we show how to formulate it as a constrained optimization program using constraint programming (CP), and alternatively, using mathematical programming (MP). Our experimental results demonstrate that the CP solution outperforms its MP counterpart. We then move toward a dynamic setting where the problem manifests itself in the real world. We show that CP model not only addresses the resource allocation problem but it also enables resource provisioning to take future considerations and system growth into account when making decisions. Overall, the CP solution stands out as a high-level, declarative solution that is efficient, easy to maintain and can address multiple scenarios.Serdar KadıoğluElsevierarticlePrimary 68T01 Artificial Intelligence - GeneralSecondary 90C11 Mixed integer programming90C27 Combinatorial optimization90C90 Applications of mathematical programmingApplied mathematics. Quantitative methodsT57-57.97Electronic computers. Computer scienceQA75.5-76.95ENEURO Journal on Computational Optimization, Vol 7, Iss 3, Pp 243-264 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Primary 68T01 Artificial Intelligence - General Secondary 90C11 Mixed integer programming 90C27 Combinatorial optimization 90C90 Applications of mathematical programming Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Primary 68T01 Artificial Intelligence - General Secondary 90C11 Mixed integer programming 90C27 Combinatorial optimization 90C90 Applications of mathematical programming Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95 Serdar Kadıoğlu Core group placement: allocation and provisioning of heterogeneous resources
description	We present a theoretical and empirical study on a recently introduced combinatorial optimization problem, namely core group placement problem. The problem arises from real-world business requirements as part of resource allocation in cloud management. In particular, it focuses on the allocation and provisioning of a set of heterogeneous resources serving multiple customers each with different service-level agreements. There exist certain business rules that govern the application stemming from privacy, performance, and capacity requirements. From a theoretical point of view, we prove that the problem is intrinsically hard, yet, from a practical point of view, we show how to formulate it as a constrained optimization program using constraint programming (CP), and alternatively, using mathematical programming (MP). Our experimental results demonstrate that the CP solution outperforms its MP counterpart. We then move toward a dynamic setting where the problem manifests itself in the real world. We show that CP model not only addresses the resource allocation problem but it also enables resource provisioning to take future considerations and system growth into account when making decisions. Overall, the CP solution stands out as a high-level, declarative solution that is efficient, easy to maintain and can address multiple scenarios.
format	article
author	Serdar Kadıoğlu
author_facet	Serdar Kadıoğlu
author_sort	Serdar Kadıoğlu
title	Core group placement: allocation and provisioning of heterogeneous resources
title_short	Core group placement: allocation and provisioning of heterogeneous resources
title_full	Core group placement: allocation and provisioning of heterogeneous resources
title_fullStr	Core group placement: allocation and provisioning of heterogeneous resources
title_full_unstemmed	Core group placement: allocation and provisioning of heterogeneous resources
title_sort	core group placement: allocation and provisioning of heterogeneous resources
publisher	Elsevier
publishDate	2019
url	https://doaj.org/article/6559e141d1124c1d8fee252cc9c669af
work_keys_str_mv	AT serdarkadıoglu coregroupplacementallocationandprovisioningofheterogeneousresources
_version_	1718400831908216832

Core group placement: allocation and provisioning of heterogeneous resources

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