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Title: Multi-user task offloading for mobile edge computing based on reinforcement learning Authors: ID Nandhini, Jembu Mohanram (Author) ID Saravanan, Kaliaperumal (Author) ID Anuratha, Kesavan (Author) ID Uma, Sankar (Author) Files: PDF - Presentation file, download (2,00 MB) MD5: 54337F661CDB21C3CD396D98CF28D8CB   URL - Source URL, visit \"https://www.midem-drustvo.si/journal_papers/MIDEM_55(2025)3p5.pdf   URL - Source URL, visit https://ojs.midem-drustvo.si/index.php/InfMIDEM/article/view/1949   This document has even more files. Complete list of files is available below. Language: English Typology: 1.01 - Original Scientific Article Organization: MIDEM - Society for Microelectronics, Electronic Components and Materials Abstract: 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. Keywords: MEC, reinforcement learning, traffic offloading, task scheduling Publication status: Published Publication version: Version of Record Publication date: 01.01.2025 Year of publishing: 2025 Number of pages: str. 183-192 Numbering: Vol. 55, no. 3 PID: 20.500.12556/DiRROS-30241  UDC: 004 ISSN on article: 0352-9045 DOI: 10.33180/InfMIDEM2025.305  COBISS.SI-ID: 281505283  Note: Besedilo v angl.; Publication date in DiRROS: 18.06.2026 Views: 123 Downloads: 174 Metadata:    Cite this work Plain text BibTeX EndNote XML EndNote/Refer RIS ABNT ACM Ref AMA APA Chicago 17th Author-Date Harvard IEEE ISO 690 MLA Vancouver : Copy citation

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Title:	Informacije MIDEM : časopis za mikroelektroniko, elektronske sestavne dele in materiale
Shortened title:	Inf. MIDEM
Publisher:	Strokovno društvo za mikroelektroniko, elektronske sestavne dele in materiale
ISSN:	0352-9045
COBISS.SI-ID:	1220612

Licences

License:	CC BY 4.0, Creative Commons Attribution 4.0 International
Link:	http://creativecommons.org/licenses/by/4.0/
Description:	This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:	Slovenian
Title:	Razbremenitev večuporabniških nalog za mobilno robno računalništvo na podlagi okrepljenega učenja
Abstract:	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
Keywords:	MEC, okrepljeno učenje, razbremenitev prometa, načrtovanje nalog

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1. Informacije MIDEM

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