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  • Open Access

    ARTICLE

    A Comprehensive Study of Resource Provisioning and Optimization in Edge Computing

    Sreebha Bhaskaran*, Supriya Muthuraman

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5037-5070, 2025, DOI:10.32604/cmc.2025.062657 - 19 May 2025

    Abstract Efficient resource provisioning, allocation, and computation offloading are critical to realizing low-latency, scalable, and energy-efficient applications in cloud, fog, and edge computing. Despite its importance, integrating Software Defined Networks (SDN) for enhancing resource orchestration, task scheduling, and traffic management remains a relatively underexplored area with significant innovation potential. This paper provides a comprehensive review of existing mechanisms, categorizing resource provisioning approaches into static, dynamic, and user-centric models, while examining applications across domains such as IoT, healthcare, and autonomous systems. The survey highlights challenges such as scalability, interoperability, and security in managing dynamic and heterogeneous infrastructures. More >

  • Open Access

    ARTICLE

    Provable Data Possession with Outsourced Tag Generation for AI-Driven E-Commerce

    Yi Li1, Wenying Zheng2, Yu-Sheng Su3,4,5,*, Meiqin Tang6

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2719-2734, 2025, DOI:10.32604/cmc.2025.059949 - 16 April 2025

    Abstract AI applications have become ubiquitous, bringing significant convenience to various industries. In e-commerce, AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy development. However, if the data used for AI applications is damaged or lost, it will inevitably affect the effectiveness of these AI applications. Therefore, it is essential to verify the integrity of e-commerce data. Although existing Provable Data Possession (PDP) protocols can verify the integrity of cloud data, they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers, which More >

  • Open Access

    ARTICLE

    An Efficient and Secure Data Audit Scheme for Cloud-Based EHRs with Recoverable and Batch Auditing

    Yuanhang Zhang1, Xu An Wang1,2,*, Weiwei Jiang3, Mingyu Zhou1, Xiaoxuan Xu1, Hao Liu1

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1533-1553, 2025, DOI:10.32604/cmc.2025.062910 - 26 March 2025

    Abstract Cloud storage, a core component of cloud computing, plays a vital role in the storage and management of data. Electronic Health Records (EHRs), which document users’ health information, are typically stored on cloud servers. However, users’ sensitive data would then become unregulated. In the event of data loss, cloud storage providers might conceal the fact that data has been compromised to protect their reputation and mitigate losses. Ensuring the integrity of data stored in the cloud remains a pressing issue that urgently needs to be addressed. In this paper, we propose a data auditing scheme… More >

  • Open Access

    ARTICLE

    Modified Neural Network Used for Host Utilization Predication in Cloud Computing Environment

    Arif Ullah1, Siti Fatimah Abdul Razak2,*, Sumendra Yogarayan2, Md Shohel Sayeed2

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5185-5204, 2025, DOI:10.32604/cmc.2025.059355 - 06 March 2025

    Abstract Networking, storage, and hardware are just a few of the virtual computing resources that the infrastructure service model offers, depending on what the client needs. One essential aspect of cloud computing that improves resource allocation techniques is host load prediction. This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues, which add several minutes to response times. To solve this issue and accurately predict cloud capacity, cloud data centers use prediction algorithms. This permits dynamic cloud scalability while maintaining superior service quality. For host prediction, we therefore present a… More >

  • Open Access

    ARTICLE

    GPU Usage Time-Based Ordering Management Technique for Tasks Execution to Prevent Running Failures of GPU Tasks in Container Environments

    Joon-Min Gil1, Hyunsu Jeong1, Jihun Kang2,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2199-2213, 2025, DOI:10.32604/cmc.2025.061182 - 17 February 2025

    Abstract In a cloud environment, graphics processing units (GPUs) are the primary devices used for high-performance computation. They exploit flexible resource utilization, a key advantage of cloud environments. Multiple users share GPUs, which serve as coprocessors of central processing units (CPUs) and are activated only if tasks demand GPU computation. In a container environment, where resources can be shared among multiple users, GPU utilization can be increased by minimizing idle time because the tasks of many users run on a single GPU. However, unlike CPUs and memory, GPUs cannot logically multiplex their resources. Additionally, GPU memory… More >

  • Open Access

    ARTICLE

    MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

    Ruoxin Li1,*, Shaoxiong Wu1, Fengping Deng1, Zhongli Tian1, Hua Cai1, Xiang Li1, Xu Xu1, Qi Liu2,3

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2969-2984, 2025, DOI:10.32604/cmc.2025.060230 - 17 February 2025

    Abstract Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and… More >

  • Open Access

    ARTICLE

    Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

    Jeng-Shyang Pan1,2, Na Yu1, Shu-Chuan Chu1,*, 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 >

  • Open Access

    ARTICLE

    Intrumer: A Multi Module Distributed Explainable IDS/IPS for Securing Cloud Environment

    Nazreen Banu A*, S.K.B. Sangeetha

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 579-607, 2025, DOI:10.32604/cmc.2024.059805 - 03 January 2025

    Abstract The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic. Cloud environments pose significant challenges in maintaining privacy and security. Global approaches, such as IDS, have been developed to tackle these issues. However, most conventional Intrusion Detection System (IDS) models struggle with unseen cyberattacks and complex high-dimensional data. In fact, this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system, named INTRUMER, which offers balanced accuracy, reliability, and security in cloud settings by multiple modules working together within it. The traffic captured… More >

  • Open Access

    ARTICLE

    Enhancing Private Cloud Based Intrusion Prevention and Detection System: An Unsupervised Machine Learning Approach

    Theophile Fozin Fonzin1,2, Halilou Claude Bobo Hamadjida2, Aurelle Tchagna Kouanou2,3,*, Valery Monthe4, Anicet Brice Mezatio5, Michael Sone Ekonde6

    Journal of Cyber Security, Vol.6, pp. 155-177, 2024, DOI:10.32604/jcs.2024.059265 - 09 January 2025

    Abstract Cloud computing is a transformational paradigm involving the delivery of applications and services over the Internet, using access mechanisms through microprocessors, smartphones, etc. Latency time to prevent and detect modern and complex threats remains one of the major challenges. It is then necessary to think about an intrusion prevention system (IPS) design, making it possible to effectively meet the requirements of a cloud computing environment. From this analysis, the central question of the present study is to minimize the latency time for efficient threat prevention and detection in the cloud. To design this IPS design… More >

  • Open Access

    ARTICLE

    AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

    Praveena Nuthakki1, Pavan Kumar T.1, Musaed Alhussein2, Muhammad Shahid Anwar3,*, Khursheed Aurangzeb2, Leenendra Chowdary Gunnam4

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4743-4756, 2024, DOI:10.32604/cmc.2024.058266 - 19 December 2024

    Abstract Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manufacturing environments, enabling scalable and flexible access to remote data centers over the internet. In these environments, Virtual Machines (VMs) are employed to manage workloads, with their optimal placement on Physical Machines (PMs) being crucial for maximizing resource utilization. However, achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives, particularly in scenarios involving inter-VM communication dependencies, which are common in smart manufacturing applications. This manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle More >

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