Ahmad Zia¹, Nazia Azim², Bekarystankyzy Akbayan³, Khalid J. Alzahrani⁴, Ateeq Ur Rehman^5,*, Faheem Ullah Khan⁶, Nouf Al-Kahtani⁷, Hend Khalid Alkahtani^8,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073818 - 12 January 2026

Abstract The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks. Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay. In this network, the task processed at fog nodes reduces transmission delay. Still, it increases energy consumption, while routing tasks to the cloud server saves energy at the cost of higher communication delay. Moreover, the… More >

Ahmed Awad Mohamed¹, Eslam Abdelhakim Seyam^2,*, Ahmed R. Elsaeed³, Laith Abualigah⁴, Aseel Smerat^5,6, Ahmed M. AbdelMouty⁷, Hosam E. Refaat⁸

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073171 - 12 January 2026

Abstract In recent years, fog computing has become an important environment for dealing with the Internet of Things. Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing. Task scheduling is crucial for efficiently handling IoT user requests, thereby improving system performance, cost, and energy consumption across nodes in cloud computing. With the large amount of data and user requests, achieving the optimal solution to the task scheduling problem is challenging, particularly in terms of cost and energy efficiency. In this paper, we develop novel strategies to save energy consumption across… More >

Olanrewaju Lawrence Abraham^1,2,*, Md Asri Ngadi¹, Johan Bin Mohamad Sharif¹, Mohd Kufaisal Mohd Sidik¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072279 - 12 January 2026

Abstract Task scheduling in cloud computing is a multi-objective optimization problem, often involving conflicting objectives such as minimizing execution time, reducing operational cost, and maximizing resource utilization. However, traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems. To address this limitation, we introduce MDMOSA (Multi-objective Dwarf Mongoose Optimization with Simulated Annealing), a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service (IaaS) cloud environments. MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization (DMO) with the exploitation strengths of Simulated Annealing (SA), More >

Sai Xu^1,*, Jun Liu^1,*, Shengyu Huang¹, Zhi Li²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071865 - 12 January 2026

Abstract In scenarios where ground-based cloud computing infrastructure is unavailable, unmanned aerial vehicles (UAVs) act as mobile edge computing (MEC) servers to provide on-demand computation services for ground terminals. To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs, this paper presents PER-MATD3, a multi-agent deep reinforcement learning algorithm with prioritized experience replay (PER) into the Centralized Training with Decentralized Execution (CTDE) framework. Specifically, PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution, while leveraging a shared replay buffer with More >

Ferzat Anka¹, Ghanshyam G. Tejani^2,3,*, Sunil Kumar Sharma⁴, Mohammed Baljon⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2691-2724, 2025, DOI:10.32604/cmes.2025.061522 - 03 March 2025

Abstract Due to the intense data flow in expanding Internet of Things (IoT) applications, a heavy processing cost and workload on the fog-cloud side become inevitable. One of the most critical challenges is optimal task scheduling. Since this is an NP-hard problem type, a metaheuristic approach can be a good option. This study introduces a novel enhancement to the Artificial Rabbits Optimization (ARO) algorithm by integrating Chaotic maps and Levy flight strategies (CLARO). This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed. It is designed for task scheduling… More >

Adil Yousif^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2869-2892, 2025, DOI:10.32604/cmes.2025.059786 - 03 March 2025

Abstract The Internet of Things (IoT) has emerged as an important future technology. IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data. In IoT-Fog computing, resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers. The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem. This study proposes an Adaptive Firefly Algorithm (AFA) for… More >

Jeng-Shyang Pan^1,2, Na Yu¹, Shu-Chuan Chu^1,*, An-Ning Zhang¹, Bin Yan³, Junzo Watada⁴

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2495-2520, 2025, DOI:10.32604/cmc.2024.058450 - 17 February 2025

Abstract The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s… More >

Zhiguo Liu^1,#, Yuqing Gui^1,#, Lin Wang^2,*, Yingru Jiang¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 863-879, 2025, DOI:10.32604/cmc.2024.057353 - 03 January 2025

Abstract Satellite edge computing has garnered significant attention from researchers; however, processing a large volume of tasks within multi-node satellite networks still poses considerable challenges. The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers, making it necessary to implement effective task offloading scheduling to enhance user experience. In this paper, we propose a priority-based task scheduling strategy based on a Software-Defined Network (SDN) framework for satellite-terrestrial integrated networks, which clarifies the execution order of tasks based on their priority. Subsequently, we More >

Abdulrahman M. Abdulghani^*

Journal on Artificial Intelligence, Vol.6, pp. 241-259, 2024, DOI:10.32604/jai.2024.056259 - 16 October 2024

Abstract Cloud computing has rapidly evolved into a critical technology, seamlessly integrating into various aspects of daily life. As user demand for cloud services continues to surge, the need for efficient virtualization and resource management becomes paramount. At the core of this efficiency lies task scheduling, a complex process that determines how tasks are allocated and executed across cloud resources. While extensive research has been conducted in the area of task scheduling, optimizing multiple objectives simultaneously remains a significant challenge due to the NP (Non-deterministic Polynomial) Complete nature of the problem. This study aims to address… More >

Prasanna Kumar Kannughatta Ranganna¹, Siddesh Gaddadevara Matt², Chin-Ling Chen^3,4,*, Ananda Babu Jayachandra⁵, Yong-Yuan Deng^4,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2557-2578, 2024, DOI:10.32604/cmc.2024.051634 - 15 August 2024

Abstract In recent decades, fog computing has played a vital role in executing parallel computational tasks, specifically, scientific workflow tasks. In cloud data centers, fog computing takes more time to run workflow applications. Therefore, it is essential to develop effective models for Virtual Machine (VM) allocation and task scheduling in fog computing environments. Effective task scheduling, VM migration, and allocation, altogether optimize the use of computational resources across different fog nodes. This process ensures that the tasks are executed with minimal energy consumption, which reduces the chances of resource bottlenecks. In this manuscript, the proposed framework… More >