Ambika Aggarwal¹, Sunil Kumar^2,3, Ashok Bhansali⁴, Deema Mohammed Alsekait^5,*, Diaa Salama AbdElminaam^6,7,8

Computer Systems Science and Engineering, Vol.48, No.4, pp. 953-967, 2024, DOI:10.32604/csse.2024.050406

Abstract Existing multiple workflow scheduling techniques focus on traditional Quality of Service (QoS) parameters such as cost, deadline, and makespan to find optimal solutions by consuming a large amount of electrical energy. Higher energy consumption decreases system efficiency, increases operational cost, and generates more carbon footprint. These major problems can lead to several problems, such as economic strain, environmental degradation, resource depletion, energy dependence, health impacts, etc. In a cloud computing environment, scheduling multiple workflows is critical in developing a strategy for energy optimization, which is an NP-hard problem. This paper proposes a novel, bi-phase Energy-Efficient… More >

Danyang Zheng, Jinchen Xv, Feng Yue, Qiming Du, Zhiheng Wang, Zheng Shan^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1957-1974, 2024, DOI:10.32604/cmc.2024.048956

Abstract As cloud quantum computing gains broader acceptance, a growing quantity of researchers are directing their focus towards this domain. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity, which in turn hampers users from achieving optimal satisfaction. Therefore, cloud quantum computing service providers require a unified analysis and scheduling framework for their quantum resources and user jobs to meet the ever-growing usage demands. This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment. The framework addresses the issue of limited quantum computing resources More >

Bin Wu^1,2, Xianyi Chen³, Jinzhou Huang^4,*, Caicai Zhang⁵, Jing Wang⁶, Jing Yu^1,2, Zhiqiang Zhao⁷, Zhuolin Mei^1,2

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3177-3194, 2024, DOI:10.32604/cmc.2023.047147

Abstract In a cloud environment, outsourced graph data is widely used in companies, enterprises, medical institutions, and so on. Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers. Servers on cloud platforms usually have some subjective or objective attacks, which make the outsourced graph data in an insecure state. The issue of privacy data protection has become an important obstacle to data sharing and usage. How to query outsourcing graph data safely and effectively has become the focus of research. Adjacency query is a basic… More >

Baixuan Tang^1,2,#, Xiaolong Xu^1,2,#, Fei Dai³, Song Wang^4,*

Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 35-55, 2023, DOI:10.32604/iasc.2023.036715

Abstract Person re-identification (ReID) aims to recognize the same person in multiple images from different camera views. Training person ReID models are time-consuming and resource-intensive; thus, cloud computing is an appropriate model training solution. However, the required massive personal data for training contain private information with a significant risk of data leakage in cloud environments, leading to significant communication overheads. This paper proposes a federated person ReID method with model-contrastive learning (MOON) in an edge-cloud environment, named FRM. Specifically, based on federated partial averaging, MOON warmup is added to correct the local training of individual edge… More >

Lihong Guo^*, Jie Yang, Haitao Wu

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1929-1946, 2023, DOI:10.32604/cmc.2023.039170

Abstract Attribute-based encryption (ABE) is a technique used to encrypt data, it has the flexibility of access control, high security, and resistance to collusion attacks, and especially it is used in cloud security protection. However, a large number of bilinear mappings are used in ABE, and the calculation of bilinear pairing is time-consuming. So there is the problem of low efficiency. On the other hand, the decryption key is not uniquely associated with personal identification information, if the decryption key is maliciously sold, ABE is unable to achieve accountability for the user. In practical applications, shared… More >

Z. Faizal Khan¹, Saeed M. Alshahrani^2,*, Abdulrahman Alghamdi², Someah Alangari³, Nouf Ibrahim Altamami⁴, Khalid A. Alissa⁵, Sana Alazwari⁶, Mesfer Al Duhayyim⁷, Fahd N. Al-Wesabi⁸

Computer Systems Science and Engineering, Vol.47, No.1, pp. 855-871, 2023, DOI:10.32604/csse.2023.036735

Abstract The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this… More >

A. Richard William^1,*, J. Senthilkumar², Y. Suresh², V. Mohanraj²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 777-790, 2023, DOI:10.32604/csse.2023.031753

Abstract In cloud computing Resource allocation is a very complex task. Handling the customer demand makes the challenges of on-demand resource allocation. Many challenges are faced by conventional methods for resource allocation in order to meet the Quality of Service (QoS) requirements of users. For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work. The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with… More >

Kunfu Wang, Yongsheng Hao, Jie Cao^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1033-1052, 2023, DOI:10.32604/iasc.2023.038036

Abstract Meteorological model tasks require considerable meteorological basis data to support their execution. However, if the task and the meteorological datasets are located on different clouds, that enhances the cost, execution time, and energy consumption of execution meteorological tasks. Therefore, the data layout and task scheduling may work together in the meteorological cloud to avoid being in various locations. To the best of our knowledge, this is the first paper that tries to schedule meteorological tasks with the help of the meteorological data set layout. First, we use the FP-Growth-M (frequent-pattern growth for meteorological model datasets) More >

Yogesh Kumar^1,*, Jitender Kumar¹, Poonam Sheoran²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5785-5800, 2023, DOI:10.32604/cmc.2023.039276

Abstract On-demand availability and resource elasticity features of Cloud computing have attracted the focus of various research domains. Mobile cloud computing is one of these domains where complex computation tasks are offloaded to the cloud resources to augment mobile devices’ cognitive capacity. However, the flexible provisioning of cloud resources is hindered by uncertain offloading workloads and significant setup time of cloud virtual machines (VMs). Furthermore, any delays at the cloud end would further aggravate the miseries of real-time tasks. To resolve these issues, this paper proposes an auto-scaling framework (ACF) that strives to maintain the quality… More >

Tahir Alyas¹, Taher M. Ghazal^2,3, Badria Sulaiman Alfurhood⁴, Ghassan F. Issa², Osama Ali Thawabeh⁵, Qaiser Abbas^6,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4119-4136, 2023, DOI:10.32604/cmc.2023.033916

Abstract Cloud computing makes dynamic resource provisioning more accessible. Monitoring a functioning service is crucial, and changes are made when particular criteria are surpassed. This research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service (QoS), estimating the required resources, and modifying allotted resources depending on workload and parallelism due to resources. Resource allocation is a complex challenge due to the versatile service providers and resource providers. The engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of service. The objective of a coherent and… More >