Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum

The integration of new Internet of Things (IoT) applications and services heavily relies on task offloading to external devices due to the constrained computing and battery resources of IoT devices. Up to now, Cloud Computing (CC) paradigm has been a good approach for tasks where latency is not crit...

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Bibliographic Details
Published in:Journal of cloud computing : advances, systems and applications Vol. 13; no. 1; pp. 94 - 24
Main Authors: Nieto, Gorka, de la Iglesia, Idoia, Lopez-Novoa, Unai, Perfecto, Cristina
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024
Springer Nature B.V
SpringerOpen
Subjects:
Algorithms
Cloud computing
Computation offloading
Computer Communication Networks
Computer Science
Computer System Implementation
Computer Systems Organization and Communication Networks
Deep learning
Edge computing
Energy consumption
Information Systems Applications (incl.Internet)
Internet of Things
Mobile computing
Mobile Edge Computing Meets AI
Multi-access Edge Computing (MEC)
Performance evaluation
Quality-of-Experience (QoE)
Reinforcement Learning (RL)
Software Engineering/Programming and Operating Systems
Special Purpose and Application-Based Systems
Task offloading
Performance evaluation
Energy consumption
Internet of Things (IoT)
Quality-of-Experience (QoE)
Multi-access Edge Computing (MEC)
Edge-Cloud-Continuum
Task offloading
Reinforcement Learning (RL)
ISSN:2192-113X, 2192-113X
Online Access:Get full text
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Summary:The integration of new Internet of Things (IoT) applications and services heavily relies on task offloading to external devices due to the constrained computing and battery resources of IoT devices. Up to now, Cloud Computing (CC) paradigm has been a good approach for tasks where latency is not critical, but it is not useful when latency matters, so Multi-access Edge Computing (MEC) can be of use. In this work, we propose a distributed Deep Reinforcement Learning (DRL) tool to optimize the binary task offloading decision, this is, the independent decision of where to execute each computing task, depending on many factors. The optimization goal in this work is to maximize the Quality-of-Experience (QoE) when performing tasks, which is defined as a metric related to the battery level of the UE, but subject to satisfying tasks’ latency requirements. This distributed DRL approach, specifically an Actor-Critic (AC) algorithm running on each User Equipment (UE), is evaluated through the simulation of two distinct scenarios and outperforms other analyzed baselines in terms of QoE values and/or energy consumption in dynamic environments, also demonstrating that decisions need to be adapted to the environment’s evolution.
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ISSN:2192-113X
2192-113X
DOI:10.1186/s13677-024-00658-0