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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Jingshuo Feng University of Sheffield UK South Yorkshire England

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This paper aims to explore the influence of digital trade on economic growth in cloud computing environment, and constructs an algorithm analysis model. Based on economic growth theory, international trade theory and information technology economic effect theory, this study selects key variables such as digital trade volume, cloud computing usage, labor productivity, R&D investment as a proportion of GDP and trade openness, and makes an empirical analysis by using multiple linear regression models. By collecting macroeconomic and international trade data from 2000 to 2023, the model is tested by descriptive statistics, stationarity test and cointegration test, which ensures the accuracy of the data and the applicability of the model. Regression analysis shows that digital trade volume, cloud computing usage, labor productivity, $R \& D$ investment as a percentage of GDP and trade openness all have significant positive effects on economic growth rate. In particular, investment in cloud computing and R&D plays a particularly significant role in promoting economic growth. The research results show that cloud computing enhances the role of digital trade in promoting economic growth by improving transaction efficiency, reducing transaction costs and promoting innovation. Based on this, this paper puts forward the corresponding measures that policy makers, enterprise decision makers and academic circles should take to promote the development of digital trade and cloud computing industry, and then promote economic growth. At the same time, this paper also points out the limitations of the research and puts forward some suggestions for future research.

关键词： Economics Productivity cloud computing Analytical models Technological innovation International trade Economic indicators Computational modeling Research and development Investment

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Distributed Maximum Utility Task Offloading for Delay-Sensitive IoT Applications in cloud and Edge computing

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2025年第2期33卷 1-41页

作者： Ghosh, Subhas Kumar Vittamsetti, Vijay Monic Westpac Inst Bank Sydney NSW 2000 Australia

Task offloading in fog computing has emerged as a pivotal solution to address the computational constraints of IoT devices, particularly for delay-sensitive applications. This paper introduces a distributed algorithm for Maximum Utility Task Offloading, where the utility is defined as the inverse of the service delay. Unlike existing centralized approaches, our method leverages a decentralized framework, enabling user devices and access points to collaboratively optimize task assignments without reliance on a central authority. The problem is modelled as a maximum-weight matching problem on bipartite graphs, and we present a deterministic distributed algorithm with a (1/3-epsilon)-approximation ratio for any epsilon>0 under the CONGEST model of computation in O(1/epsilon log(2) (Delta/epsilon)log(1+root epsilon) (1/D))-round, where Delta is the maximum degree of the network graph and D is the minimum service delay in the network. Our approach scales efficiently as it is independent of the network size and adapts to the dynamic nature of IoT environments, incorporating realistic constraints such as communication delays and heterogeneous resource availability. Extensive simulations validate the efficacy of the proposed algorithm across diverse scenarios, including varying workload distributions and network densities. Results demonstrate comparable performance with respect to centralized greedy approaches, while providing substantial benefits in terms of scalability and adaptability.

关键词： cloud computing Edge computing Internet of things Matching Scheduling Distributed approximation algorithms Task offloading Mobile cloud computing

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From Sensors to Data Intelligence: Leveraging IoT, cloud, and Edge computing with AI

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SENSORS 2025年第6期25卷 1763-1763页

作者： Ficili, Ilenia Giacobbe, Maurizio Tricomi, Giuseppe Puliafito, Antonio Univ Messina Dept Engn I-98166 Messina Italy Natl Res Council Italy ICAR CNR Inst High Performance Comp & Networking I-80131 Naples Italy Natl Interuniv Consortium Informat I-00185 Rome Italy

The exponential growth of connected devices and sensor networks has revolutionized data collection and monitoring across industries, from healthcare to smart cities. However, the true value of these systems lies not merely in gathering data but in transforming it into actionable intelligence. The integration of IoT, cloud computing, edge computing, and AI offers a robust pathway to achieve this transformation, enabling real-time decision-making and predictive insights. This paper explores innovative approaches to combine these technologies, emphasizing their role in enabling real-time decision-making, predictive analytics, and low-latency data processing. This work analyzes several integration approaches among IoT, cloud/edge computing, and AI through examples and applications, highlighting challenges and approaches to seamlessly integrate these techniques to achieve pervasive environmental intelligence. The findings contribute to advancing pervasive environmental intelligence, offering a roadmap for building smarter, more sustainable infrastructure.

关键词： edge computing cloud computing sensors IoT AI federated learning

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Quantifying Performance Trade-offs in Network Virtualization for cloud computing Environments

Quantifying Performance Trade-offs in Network Virtualization...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Guruprasad Govindappa Venkatesha Rashmi Sharma Venkatadri Marella Vinay Acharya Krishna Prasath Sivaraj Samarth Shah BMS College of Engineering Basavanagudi Bengaluru Karnataka India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India International Technological University Santa Clara CA USA University of Texas at Dallas Richardson TX United States The University of Toledo United States University at Albany Albany NY USA

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Designing a cloud computing environment is a tradeoff performance between costs and way of providing services that System of virtualized networks may affect it. In order to alleviate this, abstractions have to be developed to quantitively measure and decide data plane abstraction performance costs in the cloud computing spaces. Here we provide a technical summary of the work that was aimed at quantifying performance tradeoffs in cloud computing settings from the angle of network virtualization. This study will consider the performance of two such important factors like network latency and resource utilization. Network latency: This is defined as the time taken for a given network packet to travel from a given source to the given destination. Resource utilization, on the other hand, refers to the amount of network resource that used, e.g. how much bandwidth or computational power is used. This will then be supplemented by control the experiment to perform simulations for understanding network configuration effects and simulation threats. This will help in keeping a picture of how these trade-offs affect the overall performance in network virtualization. This study's findings will help network and cloud computing architects in optimizing their networks and alleviating the performance penalties of virtualization. Consequently, this helps in reducing costs and enhancing performance in cloud computing environments.

关键词： Hands cloud computing Costs Computational modeling Throughput Virtual machines Resource management Virtualization Software defined networking Research and development

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Integrating IoT and cloud computing with Smart Irrigation System

Integrating IoT and Cloud Computing with Smart Irrigation Sy...

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Disruptive Technologies (ICDT), International Conference on

作者： Vivek Kumar Sumit Chaudhary Vishwa Ratan Mishra Sanjeev Kumar Pippal Nikhil Bisht Devendra Singh Uttaranchal Institute of Technology Uttaranchal University Dehradun India G.L. Bajaj Institute of Technology and Management Greater Noida India Division of Research and Innovation Uttaranchal University Dehradun India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

According to an FAO report by 2050, the population may rise to 9 billion, and 9 billion people approx. 60%more plant-based food is required which is obtained by agriculture. In the current scenario 70%of the fresh water is being used in agricultural irrigation. To obtain more food and to increase the productivity of agriculture, irrigation needs to be managed properly using technologies. The study is about the integration of the IoT and cloud computing to make irrigation smart. The smart irrigation system reduces the wastage of water due to overflow while irrigating the field increases the returns on the farmer's inputs. The study collects data such as soil moisture, soil type, soil pH, crop type, and crop stage, this data will be taken using open-source sensors and open-source modules and uploaded to the cloud storage. It provides farmers the freedom of monitoring and real-time access to the field data. After analysis, the study identifies some suggestions for working in future in the technology-based irrigation. In the future crop health, health monitoring, disease detection, fertilization suggestion, and Artificial intelligence and machine learning-based decision-making based on the data.

关键词： Temperature sensors Irrigation cloud computing Temperature distribution Soil moisture Crops Smart systems Real-time systems Sensor systems Monitoring

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Load Balancing in cloud computing based on Ant Colony Optimization and Crow Search Algorithm

Load Balancing in Cloud Computing based on Ant Colony Optimi...

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International Conference on Advanced Communication Technology (ICACT)

作者： Amar N. Alsheavi Xingfu Wang Wei Zhao Ammar Hawbani Naji Alhusaini Ibrahim Abdulrab Ahmed A.S. Ismail School of Computer Science and Technology University of Science and Technology of China Hefei China School of Computer Science and Technology Anhui University of Technology Anhui China School of Computer Science Shenyang Aerospace University Shenyang China School of Computer and Information Engineering Chuzhou University Chuzhou China Computer Department Applied College Najran University Najran Saudi Arabia College of Life Sciences and Oceanography Shenzhen University Shenzhen China Faculty of Science Zagazig University Zagazig Egypt

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

With the rapid increase in cloud services and the increasing shift toward them, balancing the cloud load has become a critical research issue. The increasing demand from customers for technology services worldwide is largely due to its direct impact on performance quality. Therefore, to provide better service quality, it is essential to consider reducing response time and cost in load balancing. This paper addresses the load-balancing problem by optimizing response time and computing cost in cloud computing systems. Specifically, we first formulated the load balancing problem in cloud computing mathematically by designing an objective function that optimizes response time and cost, including constraints to ensure task assignment to a single virtual machine (VM) and resource limits are not exceeded. To solve this optimization problem, we proposed a hybrid algorithm, ACOCSA, that combines Ant Colony Optimization (ACO) and Crow Search Algorithm (CSA). We implemented the ACOCSA algorithm, evaluated the response time, cost, and load balancing metrics, and found that our algorithm performed better in terms of response time and computational cost, and showed good fairness among VMs.

关键词： Measurement cloud computing Ant colony optimization Costs Processor scheduling Heuristic algorithms Quality of service Load management Virtual machines Time factors

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QucloudSim: A Customizable Discrete Event Simulator for Quantum cloud computing Enviroment

QuCloudSim: A Customizable Discrete Event Simulator for Quan...

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Quantum Communications, Networking, and computing (QCNC), International Conference on

作者： Ruilin Zhou Yuhang Gan Lucinda Shen Yi Liu Mignot Messel Chen Qian University of California

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

Current quantum computing service providers operate in a cloud-based model, hosting expensive quantum computers in data centers for remote access. However, in the Noisy Intermediate-Scale Quantum (NISQ) era, quantum hardware is limited in terms of qubit count and connectivity. To address scalability challenges, future quantum cloud infrastructures will likely adopt a modular architecture where Quantum Processing Units (QPUs) are interconnected via quantum networks using various hardware technologies. In such systems, challenges like resource management, scheduling, and placement become critical, yet there is currently a gap in simulation tools capable of modeling quantum computers connected via quantum networks. To address this, we propose QucloudSim, a quantum cloud simulator that adopts a modular design to model various hardware components within a quantum cloud environment. It employs discrete event simulation to replicate real-world scenarios with concurrent user requests, and our evaluation of different scheduling and placement methods demonstrates that QucloudSim effectively models modular quantum cloud systems while providing valuable insights for developing scalable quantum cloud infrastructures.

关键词： Computers cloud computing Quantum computing Processor scheduling Computational modeling Scalability Hardware Discrete event simulation Quantum networks Resource management

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Personalized Compression Therapy Plans using cloud computing and ML Analytics

Personalized Compression Therapy Plans using Cloud Computing...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： S. Srinivasan Pramod Pandey M. Arulprakash K Umapathy R. Amutha M. Rajmohan Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communications Engineering Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University (Deemed University) Kanchipuram Tamil Nadu India Department of Electrical and Electronics Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

For the treatment of diseases like lymphedema and chronic venous insufficiency, compression therapy is crucial, but conventional methods frequently produce less-than-ideal results because they are impersonal. This paper presents a novel approach that uses cloud computing and decision trees to create personalized compression therapy plans in healthcare settings. The proposed system uses cloud infrastructure's scalability and computational power to handle massive amounts of patient data collected in real-time from wearable Internet of Things (IoT) devices. Through device integration, pertinent physiological parameters can be continuously monitored, allowing prompt therapy plan modifications in response to changing patient requirements. Decision Trees, a machine learning (ML) algorithm selected for its capacity to comprehend complicated datasets and produce useful insights, are essential to the system's operation. Decision trees dynamically customize compression therapy regimens using patient data analysis to maximize patient comfort and treatment effectiveness. Early system trials have produced encouraging outcomes, including notable increases in patient satisfaction and treatment outcomes. This strategy prioritizes the delivery of personalized healthcare, which increases therapeutic efficacy and fosters a patient-centered treatment experience. The combination of cloud computing and Decision Trees is a progressive step forward in the healthcare industry, providing scalable solutions that can be tailored to the specific needs of each patient and method for future developments in personalized medical care.

关键词： cloud computing Limbs Data analysis Medical treatment Real-time systems Decision trees Internet of Things Biomedical monitoring Wearable devices Wearable sensors

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Research on the Application of cloud computing-based Dual-Machine Hot Standby System in Railway Signal Safety Management

Research on the Application of Cloud Computing-based Dual-Ma...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Zhe Ji Ding Wang Bing Liu Yiming Liu China Academy of Railway Sciences (Shenzhen) Research and Design Institute Co. Ltd Shenzhen China China Academy of Railway Sciences Co. Ltd Beijing China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This paper investigates the application of cloud computing-based dual-computer hot standby computer security platform in railway signalling security management, aiming to enhance the reliability and security of railway signalling system. With the increasing importance of railway transport to modern society, the safety of railway signal system has become a key factor for public safety and social stability. This paper first analyses the security challenges faced by railway signalling systems and explores the potential of dual-machine hot-standby systems in improving system availability and security. Then, this paper details the design and implementation of a cloud-based dual-machine hot standby architecture, including system architecture, signal monitoring module, data encryption module, security audit module, and event response module. Through experimental validation, this paper compares the performance of the cloud-based dual-machine hot-standby architecture with that of the traditional single-machine system in terms of failure recovery time, resource utilisation and system response time, and the results show that the dual-machine hot-standby architecture has significant advantages in these key performance indicators. Finally, the paper discusses the limitations of the research and proposes directions for future research, including cost-benefit analysis, resource optimisation, technology adaptation research and intelligent development, with a view to providing a more efficient and intelligent solution for railway signal safety management.

关键词： cloud computing Safety management Systems architecture Computer architecture Rail transportation Stability analysis Security Reliability Resource management Time factors

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Applications of cloud computing and Artificial Intelligence in the Oil and Gas Industry: A Review

Applications of Cloud Computing and Artificial Intelligence ...

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International Image Processing, Applications and Systems Conference (IPAS)

作者： Alfitouri Ibrahim Jellah Mohamed. M Moaad. I Oil and Gas Engineering Department Bani-Waleed University Bani-Waleed Libya

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This paper reviews the latest machine learning and artificial intelligence technology, showcasing its potential to revolutionize the oil and gas industry. Despite the current limitations, the industry is on the brink of a digital transformation that promises to enhance operations and increase overall production. With the help of AI and cloud computing, the oil and gas industry's future is one of optimism and potential. The review showed a significant increase in production when applying AI technology, particularly with EOR. The paper summarized the limitations and future challenges of this technology.

关键词： cloud computing Reviews Oils Storage management Production Machine learning Companies Gas industry Reservoirs Petroleum industry

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