EADRL: Efficiency-aware adaptive deep reinforcement learning for dynamic task scheduling in edge-cloud environments

EADRL: Efficiency-aware adaptive deep reinforcement learning for dynamic task scheduling in edge-cloud environments

In dynamic edge–cloud environments, task scheduling must adapt to fluctuations in workload and resource conditions. This paper presents Efficiency-Aware Adaptive Deep Reinforcement Learning (EADRL), a framework that introduces two key mechanisms: an adaptive learning rate and a dynamic confidence-aw...

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Bibliographic Details
Main Authors: J. Anand, B. Karthikeyan
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Results in Engineering
Subjects:
Adaptive learning rate
Deep Reinforcement Learning (DRL)
Dynamic reward scaling
Edge computing
IoT
Real-time load balancing
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025019619
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