Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

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dc.contributor.authorNain, Zulqar
dc.contributor.authorShahana, B.
dc.contributor.authorChaudhry, Shehzad Ashraf
dc.contributor.authorViswanathan, P.
dc.contributor.authorMekala, M. S.
dc.contributor.authorKim, Sung Won
dc.date.accessioned2023-03-28T12:42:55Z
dc.date.available2023-03-28T12:42:55Z
dc.date.issued2023en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractFog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services. In this regard, an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions. Consequently, the development of a reinforcement learning-based flexible x-scheduling (RFXS) approach resolves the service offloading issues, where x = service/resource for producing the low latency and high performance of the network. Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency. A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set of measurements, as well as the behavioural association rate and adulation factor. Our system managed an average 0.89% of the service offloading rate, with 39 ms of delay over complex scenarios (using three servers with a 50% service arrival rate). The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty, and is suitable for simulating heterogeneous CPS frameworks.en_US
dc.identifier.doi10.32604/cmc.2023.033194en_US
dc.identifier.endpage5721en_US
dc.identifier.issn1546-2218
dc.identifier.issn1546-2226
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85145354901en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage5705en_US
dc.identifier.urihttps://hdl.handle.net/11363/4251
dc.identifier.urihttps://doi.org/
dc.identifier.volume74en_US
dc.identifier.wosWOS:000915412500016en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTECH SCIENCE PRESS, 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052en_US
dc.relation.ispartofComputers, Materials & Continuaen_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.subjectFog computingen_US
dc.subjecttask allocationen_US
dc.subjectmeasurement modelsen_US
dc.subjectfeasible node selection methodsen_US
dc.subjectperformance metricsen_US
dc.titleOptimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systemsen_US
dc.typeArticleen_US

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