{"?xml":{"@version":"1.0"},"edm:RDF":{"@xmlns:dc":"http://purl.org/dc/elements/1.1/","@xmlns:edm":"http://www.europeana.eu/schemas/edm/","@xmlns:wgs84_pos":"http://www.w3.org/2003/01/geo/wgs84_pos","@xmlns:foaf":"http://xmlns.com/foaf/0.1/","@xmlns:rdaGr2":"http://rdvocab.info/ElementsGr2","@xmlns:oai":"http://www.openarchives.org/OAI/2.0/","@xmlns:owl":"http://www.w3.org/2002/07/owl#","@xmlns:rdf":"http://www.w3.org/1999/02/22-rdf-syntax-ns#","@xmlns:ore":"http://www.openarchives.org/ore/terms/","@xmlns:skos":"http://www.w3.org/2004/02/skos/core#","@xmlns:dcterms":"http://purl.org/dc/terms/","edm:WebResource":[{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:DOC-JPAHTPES/fd1fab01-263a-43c3-9db1-969366826bd1/PDF","dcterms:extent":"2044 KB"},{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:DOC-JPAHTPES/b33f8002-4654-4077-b527-81ccf7e0e92c/TEXT","dcterms:extent":"34 KB"}],"edm:ProvidedCHO":{"@rdf:about":"URN:NBN:SI:DOC-JPAHTPES","dcterms:issued":"2025","dc:creator":["Anuratha, Kesavan","Nandhini, Jembu Mohanram","Saravanan, Kaliaperumal","Uma, Sankar"],"dc:format":[{"@xml:lang":"sl","#text":"številka:3"},{"@xml:lang":"sl","#text":"letnik:55"},{"@xml:lang":"sl","#text":"str. 183-192"}],"dc:identifier":["ISSN:0352-9045","DOI:10.33180/InfMIDEM2025.305","COBISSID_HOST:281505283","URN:URN:NBN:SI:doc-JPAHTPES"],"dc:language":"en","dc:publisher":{"@xml:lang":"sl","#text":"Strokovno društvo za mikroelektroniko, elektronske sestavne dele in materiale"},"dc:source":{"@xml:lang":"sl","#text":"Informacije MIDEM"},"dc:subject":[{"@xml:lang":"sl","#text":"MEC"},{"@xml:lang":"sl","#text":"načrtovanje nalog"},{"@xml:lang":"sl","#text":"okrepljeno učenje"},{"@xml:lang":"sl","#text":"razbremenitev prometa"},{"@xml:lang":"en","#text":"reinforcement learning"},{"@xml:lang":"en","#text":"task scheduling"},{"@xml:lang":"en","#text":"traffic offloading"}],"dc:title":{"@xml:lang":"sl","#text":"Multi-user task offloading for mobile edge computing based on reinforcement learning| Razbremenitev večuporabniških nalog za mobilno robno računalništvo na podlagi okrepljenega učenja|"},"dc:description":[{"@xml:lang":"sl","#text":"Mobile Edge computing (MEC) enables network functions and control programmable and operates key constituents of social networks in terms of increasing user’s support on devices to carry out compute. It requires traffic offloading and task scheduling to improve the storage and fast computing. In this paper, a novel method, including data driven traffic modeling enabled by a Reinforcement learning algorithm (RLTOA), is proposed for offloading traffic and improving the computing speed and minimizing the application latency of the social network. The result of the proposed data driven modeling is compared with existing methods and validate how the data driven traffic modeling for providing the computation offloading service in terms of energy budget and the mobile drop and execution of edge server. The presented computation offloading, and energy management solutions can provide valuable perceptions for practical applications of MEC. Extensive numerical findings are presented to endorse the efficacy of RLTOA and display the effect of the social network requirement"},{"@xml:lang":"sl","#text":"Mobilno robno računalništvo (MEC) omogoča programiranje omrežnih funkcij in nadzora ter upravlja ključne sestavne dele družbenih omrežij z vidika povečanja podpore uporabnikom na napravah za izvajanje računalniških operacij. Za izboljšanje shranjevanja in hitrega računalniškega delovanja je potrebno razbremenjevanje prometa in načrtovanje nalog. V članku je predlagana nova metoda, vključno z modeliranjem prometa na podlagi podatkov, ki ga omogoča algoritem okrepljenega učenja (RLTOA), za razbremenitev prometa in izboljšanje hitrosti računalniškega obdelovanja ter zmanjšanje zakasnitve aplikacij družbenega omrežja. Rezultat predlaganega modeliranja na podlagi podatkov so primerjani z obstoječimi metodami in potrjujejo modeliranje prometa na podlagi podatkov za zagotavljanje storitve razbremenitve računalniških operacij v smislu energijskega proračuna in mobilnega padca ter izvajanja robnega strežnika. Predstavljene rešitve za razbremenitev računalniških operacij in upravljanje z energijo lahko zagotovijo dragocene ugotovitve za praktične aplikacije MEC. Predstavljeni so obsežni numerični rezultati, ki potrjujejo učinkovitost RLTOA in prikazujejo učinek zahtev družbenega omrežja"}],"edm:type":"TEXT","dc:type":[{"@xml:lang":"sl","#text":"znanstveno časopisje"},{"@xml:lang":"en","#text":"journals"},{"@rdf:resource":"http://www.wikidata.org/entity/Q361785"}]},"ore:Aggregation":{"@rdf:about":"http://www.dlib.si/?URN=URN:NBN:SI:DOC-JPAHTPES","edm:aggregatedCHO":{"@rdf:resource":"URN:NBN:SI:DOC-JPAHTPES"},"edm:isShownBy":{"@rdf:resource":"http://www.dlib.si/stream/URN:NBN:SI:DOC-JPAHTPES/fd1fab01-263a-43c3-9db1-969366826bd1/PDF"},"edm:rights":{"@rdf:resource":"http://creativecommons.org/licenses/by/4.0/"},"edm:provider":"Slovenian National E-content Aggregator","edm:intermediateProvider":{"@xml:lang":"en","#text":"National and University Library of Slovenia"},"edm:dataProvider":{"@xml:lang":"sl","#text":"Strokovno društvo za mikroelektroniko, elektronske sestavne dele in materiale"},"edm:object":{"@rdf:resource":"http://www.dlib.si/streamdb/URN:NBN:SI:DOC-JPAHTPES/maxi/edm"},"edm:isShownAt":{"@rdf:resource":"http://www.dlib.si/details/URN:NBN:SI:DOC-JPAHTPES"}}}}