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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...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Zahra Safavifar, Eric Gyamfi, Eleni Mangina, Fatemeh Golpayegani
Format: Artigo
Sprache:Inglês
Veröffentlicht: IEEE 2024-01-01
Schriftenreihe:IEEE Access
Schlagworte:
Edge computing
workload orchestration
deep reinforcement learning (DRL)
resource-constrained environment
extreme edge computing
Online-Zugang:https://ieeexplore.ieee.org/document/10538273/
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