Mohammad A Al Khaldy¹, Ahmad Nabot², Ahmad Al-Qerem^3,*, Mohammad Alauthman⁴, Amina Salhi^5,*, 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 >

Kaili Shao¹, Hui Fu¹, Ying Song², Bo Wang^3,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2259-2274, 2023, DOI:10.32604/csse.2023.041454 - 28 July 2023

Abstract To serve various tasks requested by various end devices with different requirements, end-edge-cloud (E2C) has attracted more and more attention from specialists in both academia and industry, by combining both benefits of edge and cloud computing. But nowadays, E2C still suffers from low service quality and resource efficiency, due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility. To address these issues, this paper focuses on task offloading, which makes decisions that which resources are allocated to tasks for their processing. This paper first formulates the problem… More >

Abdulaziz Alorf^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 273-286, 2023, DOI:10.32604/csse.2023.035437 - 20 January 2023

Abstract Nowadays, smart electricity grids are managed through advanced tools and techniques. The advent of Artificial Intelligence (AI) and network technology helps to control the energy demand. These advanced technologies can resolve common issues such as blackouts, optimal energy generation costs, and peak-hours congestion. In this paper, the residential energy demand has been investigated and optimized to enhance the Quality of Service (QoS) to consumers. The energy consumption is distributed throughout the day to fulfill the demand in peak hours. Therefore, an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption. More >

Jaber Almutairi¹, Mohammad Aldossary^2,*

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 129-145, 2021, DOI:10.32604/iasc.2021.018480 - 26 July 2021

Abstract With the increasing number of Internet of Things (IoT) devices connected to the internet, a platform is required to support the enormous amount of data they generate. Since cloud computing is far away from the connected IoT devices, applications that require low-latency, real-time interaction and high quality of service (QoS) may suffer network delay in using the Cloud. Consequently, the concept of edge computing has appeared to complement cloud services, working as an intermediate layer with computation capabilities between the Cloud and IoT devices, to overcome these limitations. Although edge computing is a promising enabler… More >

Jaber Almutairi¹, Mohammad Aldossary^2,*,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4143-4160, 2021, DOI:10.32604/cmc.2021.018145 - 06 May 2021

Abstract Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. However, different service architecture and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents an Edge-Cloud system architecture that supports scheduling offloading tasks of IoT applications in order to minimize the enormous amount of transmitting data in the More >