Chirag Chandrashekar¹, Pradeep Krishnadoss^1,*, Vijayakumar Kedalu Poornachary¹, Balasundaram Ananthakrishnan^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2593-2616, 2024, DOI:10.32604/cmc.2024.046304

Abstract Cloud computing provides a diverse and adaptable resource pool over the internet, allowing users to tap into various resources as needed. It has been seen as a robust solution to relevant challenges. A significant delay can hamper the performance of IoT-enabled cloud platforms. However, efficient task scheduling can lower the cloud infrastructure’s energy consumption, thus maximizing the service provider’s revenue by decreasing user job processing times. The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm (MCWOA), combines elements of the Chimp Optimization Algorithm (COA) and the Whale Optimization Algorithm (WOA). To enhance MCWOA’s identification precision, the Sobol sequence… More >

Ye Lu^1,*, Tao Feng¹, Chunyan Liu², Wenbo Zhang³

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2787-2811, 2024, DOI:10.32604/cmc.2023.046106

Abstract The Access control scheme is an effective method to protect user data privacy. The access control scheme based on blockchain and ciphertext policy attribute encryption (CP–ABE) can solve the problems of single—point of failure and lack of trust in the centralized system. However, it also brings new problems to the health information in the cloud storage environment, such as attribute leakage, low consensus efficiency, complex permission updates, and so on. This paper proposes an access control scheme with fine-grained attribute revocation, keyword search, and traceability of the attribute private key distribution process. Blockchain technology tracks the authorization of attribute private… More >

Nader Abdel Karim^1,*, Osama A. Khashan^2,*, Waleed K. Abdulraheem³, Moutaz Alazab¹, Hasan Kanaker⁴, Mahmoud E. Farfoura⁵, Mohammad Alshinwan^5,6

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2023-2045, 2024, DOI:10.32604/cmc.2023.044304

Abstract As the extensive use of cloud computing raises questions about the security of any personal data stored there, cryptography is being used more frequently as a security tool to protect data confidentiality and privacy in the cloud environment. A hypervisor is a virtualization software used in cloud hosting to divide and allocate resources on various pieces of hardware. The choice of hypervisor can significantly impact the performance of cryptographic operations in the cloud environment. An important issue that must be carefully examined is that no hypervisor is completely superior in terms of performance; Each hypervisor should be examined to meet… More >

Shasha Zhao^1,2,3,*, Huanwen Yan^1,2, Qifeng Lin^1,2, Xiangnan Feng^1,2, He Chen^1,2, Dengyin Zhang^1,2

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1135-1156, 2024, DOI:10.32604/cmc.2024.045660

Abstract Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment. Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios. In this work, the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm (HPSO-EABC) has been proposed, which hybrids our presented Evolutionary Artificial Bee Colony (EABC), and Hierarchical Particle Swarm Optimization (HPSO) algorithm. The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm. Comprehensive testing including evaluations of algorithm convergence speed, resource execution time, load balancing,… More >

Sarah Mustafa Eljack^1,*, Mahdi Jemmali^2,3,4, Mohsen Denden^6,7, Mutasim Al Sadig¹, Abdullah M. Algashami¹, Sadok Turki⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1461-1479, 2024, DOI:10.32604/cmc.2023.040919

Abstract In the cloud environment, ensuring a high level of data security is in high demand. Data planning storage optimization is part of the whole security process in the cloud environment. It enables data security by avoiding the risk of data loss and data overlapping. The development of data flow scheduling approaches in the cloud environment taking security parameters into account is insufficient. In our work, we propose a data scheduling model for the cloud environment. The model is made up of three parts that together help dispatch user data flow to the appropriate cloud VMs. The first component is the… More >

Yurong Luo, Wei You^*, Chao Shang, Xiongpeng Ren, Jin Cao, Hui Li

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2237-2260, 2024, DOI:10.32604/cmes.2023.045679

Abstract The dynamic landscape of the Internet of Things (IoT) is set to revolutionize the pace of interaction among entities, ushering in a proliferation of applications characterized by heightened quality and diversity. Among the pivotal applications within the realm of IoT, as a significant example, the Smart Grid (SG) evolves into intricate networks of energy deployment marked by data integration. This evolution concurrently entails data interchange with other IoT entities. However, there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem. In this paper, we introduce a hierarchical secure data-sharing platform empowered… 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 servers and improve the model’s… More >

Hayder Makki Shakir, Jaber Karimpour^*, Jafar Razmara

Computer Systems Science and Engineering, Vol.48, No.1, pp. 35-55, 2024, DOI:10.32604/csse.2023.038545

Abstract As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource accessibility, and security. In this… More >

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 >

Jun Shu^1,2, Shuai Wang^1,2, Shiqi Yu^1,2, Jie Zhang^3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2677-2697, 2023, DOI:10.32604/cmc.2023.045818

Abstract Traditional models for semantic segmentation in point clouds primarily focus on smaller scales. However, in real-world applications, point clouds often exhibit larger scales, leading to heavy computational and memory requirements. The key to handling large-scale point clouds lies in leveraging random sampling, which offers higher computational efficiency and lower memory consumption compared to other sampling methods. Nevertheless, the use of random sampling can potentially result in the loss of crucial points during the encoding stage. To address these issues, this paper proposes cross-fusion self-attention network (CFSA-Net), a lightweight and efficient network architecture specifically designed for directly processing large-scale point clouds.… More >