DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

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dc.authoridhttps://orcid.org/0000-0002-1313-285Xen_US
dc.authoridhttps://orcid.org/0000-0003-4878-1988en_US
dc.authoridhttps://orcid.org/0000-0003-4118-2480en_US
dc.authoridhttps://orcid.org/0000-0002-9321-6956en_US
dc.contributor.authorMekala, M. S.
dc.contributor.authorPatan, Rizwan
dc.contributor.authorIslam, S. K. Hafizul
dc.contributor.authorSamanta, Debabrata
dc.contributor.authorMallah, Ghulam Ali
dc.contributor.authorChaudhry, Shehzad Ashraf
dc.date.accessioned2023-07-23T13:28:26Z
dc.date.available2023-07-23T13:28:26Z
dc.date.issued2021en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractThe heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.en_US
dc.identifier.doi10.1155/2021/6688162en_US
dc.identifier.endpage16en_US
dc.identifier.issn1939-0114
dc.identifier.issn1939-0122
dc.identifier.scopus2-s2.0-85107652607en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/11363/5092
dc.identifier.urihttps://doi.org/
dc.identifier.volume2021en_US
dc.identifier.wosWOS:000668988600003en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorChaudhry, Shehzad Ashraf
dc.language.isoenen_US
dc.publisherWILEY-HINDAWI, ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON WIT 5HE, ENGLANDen_US
dc.relation.ispartofSecurity and Communication Networksen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.titleDAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centreen_US
dc.typeArticleen_US

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