A self-supervised deep reinforcement learning for Zero-Shot Task scheduling in mobile edge computing environments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04274644%3A_____%2F25%3A%230001252" target="_blank" >RIV/04274644:_____/25:#0001252 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S1570870525002252" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S1570870525002252</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.adhoc.2025.103977" target="_blank" >10.1016/j.adhoc.2025.103977</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A self-supervised deep reinforcement learning for Zero-Shot Task scheduling in mobile edge computing environments

Popis výsledku v původním jazyce

The rising need for swift response times makes it essential to use computing resources and network capacities efficiently at the edges of the networks. Mobile Edge Computing (MEC) handles this by processing user data near where it is generated rather than always relying on remote cloud centres. Yet, scheduling tasks under these conditions can be difficult because workloads shift, resources vary, and network performance is unstable. Traditional scheduling strategies often underperform in such rapidly changing settings, and even Deep Reinforcement Learning (DRL) solutions usually require extensive retraining whenever they encounter unfamiliar tasks. This paper proposes a self-supervised DRL framework for zero-shot task scheduling in MEC environments. The system integrates self-supervised learning to generate task embeddings, enabling the model to classify tasks into clusters based on resource requirements and execution complexity. A Soft Actor-Critic (SAC)-based scheduler then optimally assigns tasks to MEC nodes while dynamically adapting to network conditions. The training process combines contrastive learning for task representation and policy optimization to enhance scheduling decisions. Simulations demonstrate that the proposed approach reduces task completion time by up to 22 %, lowers energy consumption by 29 %, and improves latency by 18 % over baseline methods.

Název v anglickém jazyce

A self-supervised deep reinforcement learning for Zero-Shot Task scheduling in mobile edge computing environments

Popis výsledku anglicky

The rising need for swift response times makes it essential to use computing resources and network capacities efficiently at the edges of the networks. Mobile Edge Computing (MEC) handles this by processing user data near where it is generated rather than always relying on remote cloud centres. Yet, scheduling tasks under these conditions can be difficult because workloads shift, resources vary, and network performance is unstable. Traditional scheduling strategies often underperform in such rapidly changing settings, and even Deep Reinforcement Learning (DRL) solutions usually require extensive retraining whenever they encounter unfamiliar tasks. This paper proposes a self-supervised DRL framework for zero-shot task scheduling in MEC environments. The system integrates self-supervised learning to generate task embeddings, enabling the model to classify tasks into clusters based on resource requirements and execution complexity. A Soft Actor-Critic (SAC)-based scheduler then optimally assigns tasks to MEC nodes while dynamically adapting to network conditions. The training process combines contrastive learning for task representation and policy optimization to enhance scheduling decisions. Simulations demonstrate that the proposed approach reduces task completion time by up to 22 %, lowers energy consumption by 29 %, and improves latency by 18 % over baseline methods.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Ad Hoc Networks

ISSN

1570-8705

e-ISSN

—

Svazek periodika

178

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

001532401900001

EID výsledku v databázi Scopus

2-s2.0-105010520146