Multi-Objective Deep Reinforcement Learning for Efficient Workload Orchestration in Extreme Edge Computing

Workload orchestration at the edge of the network has become increasingly challenging with the ever-increasing penetration of resource demanding mobile, and heterogeneous devices offering low latency services. Literature has addressed this challenge assuming the availability of multi-access Mobile E...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Zahra Safavifar, Eric Gyamfi, Eleni Mangina, Fatemeh Golpayegani
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	IEEE 2024-01-01
Sarja:	IEEE Access
Aiheet:	Edge computing workload orchestration deep reinforcement learning (DRL) resource-constrained environment extreme edge computing
Linkit:	https://ieeexplore.ieee.org/document/10538273/
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