Salam Al-E’mari¹, Yousef Sanjalawe^2,*, Ahmad Al-Daraiseh³, Mohammad Bany Taha⁴, Mohammad Aladaileh²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1-41, 2024, DOI:10.32604/cmes.2023.043627

Abstract Amid the landscape of Cloud Computing (CC), the Cloud Datacenter (DC) stands as a conglomerate of physical servers, whose performance can be hindered by bottlenecks within the realm of proliferating CC services. A linchpin in CC’s performance, the Cloud Service Broker (CSB), orchestrates DC selection. Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck, endangering service quality. To tackle this, deploying an efficient CSB policy becomes imperative, optimizing DC selection to meet stringent Quality-of-Service (QoS) demands. Amidst numerous CSB policies, their implementation grapples with challenges like costs and availability. This article undertakes a holistic… More >

Ming Gao^1,#, Weiwei Cai^1,#, Yizhang Jiang¹, Wenjun Hu³, Jian Yao², Pengjiang Qian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 259-277, 2024, DOI:10.32604/cmes.2023.029015

Abstract Currently, applications accessing remote computing resources through cloud data centers is the main mode of operation, but this mode of operation greatly increases communication latency and reduces overall quality of service (QoS) and quality of experience (QoE). Edge computing technology extends cloud service functionality to the edge of the mobile network, closer to the task execution end, and can effectively mitigate the communication latency problem. However, the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management, and the booming development of artificial neural networks provides us with more powerful methods… More >

Xiangmin Guo^1,2, Guangjun Liang^1,2,*, Jiayin Liu^1,2, Xianyi Chen^3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3305-3323, 2023, DOI:10.32604/cmc.2023.044529

Abstract Cloud storage is widely used by large companies to store vast amounts of data and files, offering flexibility, financial savings, and security. However, information shoplifting poses significant threats, potentially leading to poor performance and privacy breaches. Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms, ensuring businesses can focus on business development. To ensure data security in cloud platforms, this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing (HD2C) model. However, the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things (IoT) in the cloud.… More >

Riza Altunay^1,2,*, Omer Faruk Bay³

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2881-2897, 2023, DOI:10.32604/cmc.2023.042340

Abstract The use of fog computing in the Internet of Things (IoT) has emerged as a crucial solution, bringing cloud services closer to end users to process large amounts of data generated within the system. Despite its advantages, the increasing task demands from IoT objects often overload fog devices with limited resources, resulting in system delays, high network usage, and increased energy consumption. One of the major challenges in fog computing for IoT applications is the efficient deployment of services between fog clouds. To address this challenge, we propose a novel Optimal Foraging Algorithm (OFA) for task placement on appropriate fog… More >

Laila Almutairi¹, Shabnam Mohamed Aslam^2,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2791-2811, 2023, DOI:10.32604/cmc.2023.040268

Abstract The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide. Modern data centres’ operating costs mostly come from back-end cloud infrastructure and energy consumption. In cloud computing, extensive communication resources are required. Moreover, cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user requirements. It is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching buffers. This paper proposes a novel Energy and Communication (EC) aware scheduling (EC-scheduler) algorithm for green cloud computing, which optimizes data centre energy consumption and traffic load. The primary goal… More >

Muwei Jian^1,2,*, Yue Jin¹, Hui Yu³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 3051-3063, 2024, DOI:10.32604/cmes.2023.030236

Abstract Universal lesion detection (ULD) methods for computed tomography (CT) images play a vital role in the modern clinical medicine and intelligent automation. It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks. However, 3D CT blocks necessitate significantly higher hardware resources during the learning phase. Therefore, efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks. In this paper, we propose a ULD network with the enhanced temporal correlation for this purpose, named TCE-Net. The designed TCE module is applied to enrich the discriminate… More >

Rabar H. Faraj^1,*, Hemn Unis Ahmed^2,3, Hardi Saadullah Fathullah⁴, Alan Saeed Abdulrahman², Farid Abed⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2925-2954, 2024, DOI:10.32604/cmes.2023.029392

Abstract Plain concrete is strong in compression but brittle in tension, having a low tensile strain capacity that can significantly degrade the long-term performance of concrete structures, even when steel reinforcing is present. In order to address these challenges, short polymer fibers are randomly dispersed in a cement-based matrix to form a highly ductile engineered cementitious composite (ECC). This material exhibits high ductility under tensile forces, with its tensile strain being several hundred times greater than conventional concrete. Since concrete is inherently weak in tension, the tensile strain capacity (TSC) has become one of the most extensively researched properties. As a… More >

Giuseppe Corrente^1,2,*, Carlo Vincenzo Stanzione^3,4, Vittoria Stanzione⁵

Journal of Quantum Computing, Vol.5, pp. 55-70, 2023, DOI:10.32604/jqc.2023.044786

Abstract The Hamiltonian cycle problem (HCP), which is an NP-complete problem, consists of having a graph G with nodes and m edges and finding the path that connects each node exactly once. In this paper we compare some algorithms to solve a Hamiltonian cycle problem, using different models of computations and especially the probabilistic and quantum ones. Starting from the classical probabilistic approach of random walks, we take a step to the quantum direction by involving an ad hoc designed Quantum Turing Machine (QTM), which can be a useful conceptual project tool for quantum algorithms. Introducing several constraints to the graphs,… More >

Seungwoo Kang¹, Seyha Ros¹, Inseok Song¹, Prohim Tam¹, Sa Math², Seokhoon Kim^1,3,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1967-1983, 2023, DOI:10.32604/cmc.2023.045020

Abstract Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks. To establish… More >

Tariq Qayyum¹, Zouheir Trabelsi^1,*, Asadullah Tariq¹, Muhammad Ali², Kadhim Hayawi³, Irfan Ud Din⁴

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1739-1757, 2023, DOI:10.32604/cmc.2023.043684

Abstract Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed data sources, varying data quality,… More >