Yuwei Xu, Baokang Zhao^*, Huan Zhou, Jinshu Su

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 609-629, 2024, DOI:10.32604/cmes.2024.053462 - 20 August 2024

Abstract The rapid expansion of artificial intelligence (AI) applications has raised significant concerns about user privacy, prompting the development of privacy-preserving machine learning (ML) paradigms such as federated learning (FL). FL enables the distributed training of ML models, keeping data on local devices and thus addressing the privacy concerns of users. However, challenges arise from the heterogeneous nature of mobile client devices, partial engagement of training, and non-independent identically distributed (non-IID) data distribution, leading to performance degradation and optimization objective bias in FL training. With the development of 5G/6G networks and the integration of cloud computing… More >

V. Dhilip Kumar¹, J. Praveenchandar², Muhammad Arif^3,*, Adrian Brezulianu⁴, Oana Geman⁵, Atif Ikram^3,6

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2179-2188, 2023, DOI:10.32604/cmc.2023.034764 - 29 November 2023

Abstract Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage, processing power, and other computer system resources. It is also referred to as a system that will let the consumers utilize computational resources like databases, servers, storage, and intelligence over the Internet. In a cloud network, load balancing is the process of dividing network traffic among a cluster of available servers to increase efficiency. It is also known as a server pool or server farm. When a single node is overwhelmed, balancing the workload is needed to manage unpredictable workflows. The More >

Xinfei Li², Xiaolan Xie^1,2,*, Yigang Tang², Qiang Guo^1,2

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2707-2724, 2023, DOI:10.32604/csse.2023.037351 - 28 July 2023

Abstract Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters. We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition (VMD)-Permutation entropy (PE) and long short-term memory (LSTM) neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data. The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components, which solves the signal decomposition algorithm’s end-effect and modal confusion problems.… 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 - 31 March 2023

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 >

Tahir Alyas¹, Nadia Tabassum², Muhammad Waseem Iqbal^3,*, Abdullah S. Alshahrani⁴, Ahmed Alghamdi⁵, Syed Khuram Shahzad⁶

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1165-1179, 2023, DOI:10.32604/iasc.2023.028815 - 06 June 2022

Abstract Kubernetes, a container orchestrator for cloud-deployed applications, allows the application provider to scale automatically to match the fluctuating intensity of processing demand. Container cluster technology is used to encapsulate, isolate, and deploy applications, addressing the issue of low system reliability due to interlocking failures. Cloud-based platforms usually entail users define application resource supplies for eco container virtualization. There is a constant problem of over-service in data centers for cloud service providers. Higher operating costs and incompetent resource utilization can occur in a waste of resources. Kubernetes revolutionized the orchestration of the container in the cloud-native… More >

Haiyang Zhang¹, Guolong Chen², Xianwei Li^2,3,*

Computer Systems Science and Engineering, Vol.34, No.4, pp. 249-254, 2019, DOI:10.32604/csse.2019.34.249

Abstract As a new computing paradigm, cloud computing has received much attention from research and economics fields in recent years. Cloud resources can be priced according to several pricing options in cloud markets. Usage-based and reserved pricing schemes are commonly adopted by leading cloud service providers (CSPs) such as Amazon and Google. With more and more CSPs entering cloud computing markets, the pricing of cloud resources is an important issue that they need to consider. In this paper, we study how to segment cloud resources using hybrid pricing schemes in order to obtain the maximum revenue More >