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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Veröffentlicht in:	Journal of cloud computing : advances, systems and applications Jg. 13; H. 1; S. 94 - 24
Hauptverfasser:	Nieto, Gorka, de la Iglesia, Idoia, Lopez-Novoa, Unai, Perfecto, Cristina
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024 Springer Nature B.V SpringerOpen
Schlagworte:	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-Zugang:	Volltext
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