Efficient cloud data center: An adaptive framework for dynamic Virtual Machine Consolidation

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dc.contributor.authorRozehkhani, S.M.
dc.contributor.authorMahan, F.
dc.contributor.authorPedrycz, W.
dc.date.accessioned2024-05-19T14:34:12Z
dc.date.available2024-05-19T14:34:12Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractCloud computing is a thriving and ever-expanding sector in the industry world. This growth has sparked increased interest from organizations seeking to harness its potential. However, the sheer volume of services and offerings in this field has resulted in a noticeable surge in related data. With the rapid evolution and growing demand, cloud computing resource management faces a fresh set of challenges. Resource limitations, such as high maintenance costs, elevated Energy Consumption (EC), and adherence to Service Level Agreements (SLA), are critical concerns for both the cloud computing industry and its user organizations. In this context, taking a proactive approach to resource management and Virtual Machine Consolidation (VMC) has become imperative. The logical management of resources and the consolidation of Virtual Machines (VMs) in a manner that aligns with the requirements and demands of service providers and users have garnered widespread attention. The goal of this proposed paper is to focus on addressing the VMC problem within a unified framework, divided into two main phases. The first phase deals with host workload detection and prediction, while the subsequent phase tackles the selection and allocation of appropriate VMs. In our proposed method, for the first time, we use a Granular Computing (GRC) model, which is an efficient, scalable, and human-centric computational approach. This model exhibits behaviors similar to intelligent human decision-making, as it can simultaneously consider all factors and criteria involved in the problems. We evaluated our proposed method through simulations using CloudSim on various types of workloads. Experimental results demonstrate that our proposed algorithm outperforms other algorithms in all measurement metrics. © 2024 Elsevier Ltden_US
dc.identifier.doi10.1016/j.jnca.2024.103885
dc.identifier.issn1084-8045
dc.identifier.scopus2-s2.0-85191298425en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.jnca.2024.103885
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4435
dc.identifier.volume226en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAcademic Pressen_US
dc.relation.ispartofJournal of Network and Computer Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectResources Allocationen_US
dc.subjectResources Managementen_US
dc.subjectVm Detectionen_US
dc.subjectVm Placementen_US
dc.subjectVm Selectionen_US
dc.subjectVmcen_US
dc.titleEfficient cloud data center: An adaptive framework for dynamic Virtual Machine Consolidationen_US
dc.typeArticleen_US

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