Computation offloading based on incomplete information in edge computing networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258634" target="_blank" >RIV/61989100:27240/25:10258634 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s10586-025-05560-1" target="_blank" >https://link.springer.com/article/10.1007/s10586-025-05560-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10586-025-05560-1" target="_blank" >10.1007/s10586-025-05560-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Computation offloading based on incomplete information in edge computing networks

Popis výsledku v původním jazyce

Mobile edge computing meets stringent latency requirements by offloading computational tasks to edge servers. However, in dynamic and uncertain environments, assigning tasks to multiple users becomes complex. To address this challenge, we design a multi-user task offloading framework that allows users to initiate service requests in a distributed manner. Specifically, we propose an online learning offloading algorithm based on a distributed auction multi-armed bandit, which can adapt to stochastically changing environments and gradually reduce computational latency. We then transform the dynamic task allocation problem into an online multi-user multi-armed bandit problem and develop an offloading algorithm based on heterogeneous distributed multi-armed bandit (HD-MAB) to optimize user rewards subject to network latency. We demonstrate that the HD-MAB algorithm can achieve optimal task allocation, thereby providing near-optimal service performance with linear regret. Simulation results show that our offloading method performs well in optimizing latency-sensitive tasks, and that user participation in the decision-making process of the HD-MAB algorithm does not affect the asymptotic optimality of the algorithm.

Název v anglickém jazyce

Computation offloading based on incomplete information in edge computing networks

Popis výsledku anglicky

Mobile edge computing meets stringent latency requirements by offloading computational tasks to edge servers. However, in dynamic and uncertain environments, assigning tasks to multiple users becomes complex. To address this challenge, we design a multi-user task offloading framework that allows users to initiate service requests in a distributed manner. Specifically, we propose an online learning offloading algorithm based on a distributed auction multi-armed bandit, which can adapt to stochastically changing environments and gradually reduce computational latency. We then transform the dynamic task allocation problem into an online multi-user multi-armed bandit problem and develop an offloading algorithm based on heterogeneous distributed multi-armed bandit (HD-MAB) to optimize user rewards subject to network latency. We demonstrate that the HD-MAB algorithm can achieve optimal task allocation, thereby providing near-optimal service performance with linear regret. Simulation results show that our offloading method performs well in optimizing latency-sensitive tasks, and that user participation in the decision-making process of the HD-MAB algorithm does not affect the asymptotic optimality of the algorithm.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Cluster Computing-The Journal of Networks Software Tools and Applications

ISSN

1386-7857

e-ISSN

1573-7543

Svazek periodika

28

Číslo periodika v rámci svazku

14

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

nestránkováno

Kód UT WoS článku

001586153500048

EID výsledku v databázi Scopus

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