Mohammad A Al Khaldy1, Ahmad Nabot2, Ahmad Al-Qerem3,*, Mohammad Alauthman4, Amina Salhi5,*, Suhaila Abuowaida6, Naceur Chihaoui7
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 673-697, 2025, DOI:10.32604/cmes.2025.070004
- 30 October 2025
Abstract The exponential growth of Internet of Things (IoT) devices has created unprecedented challenges in data processing and resource management for time-critical applications. Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems, while pure edge computing faces resource constraints that limit processing capabilities. This paper addresses these challenges by proposing a novel Deep Reinforcement Learning (DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments. Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency. The framework introduces three key innovations: (1)… More >
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