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

J. Anand; B. Karthikeyan

doi:10.1016/j.rineng.2025.105890

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

J. Anand B. Karthikeyan

Year: 2025 Journal: Results in Engineering Vol: 27 Pages: 105890-105890 Publisher: Elsevier BV

DOI: 10.1016/j.rineng.2025.105890

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Keywords:

Reinforcement learning Computer science Cloud computing Scheduling (production processes) Task (project management) Enhanced Data Rates for GSM Evolution Reinforcement Artificial intelligence Distributed computing Psychology Engineering Operations management Social psychology Operating system

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IoT and Edge/Fog Computing

Physical Sciences → Computer Science → Computer Networks and Communications

Cloud Computing and Resource Management

Physical Sciences → Computer Science → Information Systems

Age of Information Optimization

Physical Sciences → Computer Science → Computer Networks and Communications

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

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