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检索条件"任意字段=IEEE International Conference on Cloud Computing Technology and Science"

共 110967 条记录，以下是51-60 订阅

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Collaborative and Integrated Platform to Support Distributed Manufacturing System Using a Service-Oriented Approach Based on cloud computing Paradigm

Collaborative and Integrated Platform to Support Distributed...

引用

2025 ieee international conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Nainwal, Ankita Sharma, Vishal Varshney, Neeraj Shirish Boob, Nandini Reddy, N.V. Uma Reddy, K. Jyothsna Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bangalore India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Increasing interest in distributed manufacturing systems is a result of the dynamic nature of today's business environment and with that comes the popularity of their use. This has given rise in the interest in distributed manufacturing systems. The indirect consequence of this has led to much higher use of distributed manufacturing systems. A large number of production processes, dispersed across a multitude of locations geographically separate, must be controlled and integrated in order to achieve production agility, flexibility and efficiency of costs. In our most recent project, we developed an integrated, collaborative manufacturing platform, the centerpiece of which was LAYMOD. To address the issues posed by such systems, this research presents a collateral and integrated platform based on a service oriented approach that can operate within the cloud computing paradigm. These systems present problems which the study is conducted in order to solve. Utilising the features of cloud computing, it aims to serve as a platform in solving process coordination and hence improving efficiency and scalability of distant manufacturing processes and potential real time communications among various stake holders. Furthermore, statistical research on the rates of platform module adoption in different settings provides specifics of (a) use patterns of some platform modules and (b) what capabilities are provided by some platform modules. When extrapolated to a wider size, our work is applied towards the creation of collaborative and integrated platforms for a number of the various manufacturing processes which are currently in use. © 2025 ieee.

关键词： cloud platforms

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Transforming Hospital Visitor Screening with cloud-Powered Deep Learning for Safety and Security

Transforming Hospital Visitor Screening with Cloud-Powered D...

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2025 ieee international Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Joseph Rajan, D. Antony Sivanesan, T.M. Mohankumar, N. Srinivasan, M. Sivaraman, Audithan Velmurugan, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Symbiosis Institute of Technology Pune India P. S. v College of Engineering and Technology Department of Information Technology Tamilnadu Krishnagiri India Department of Information Technology Jafferpur New Delhi110073 India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331529833

This research describes a novel approach to hospital visitor screening utilizing cloud-powered deep learning using convolutional neural networks (CNNs). Our proposed approach will fill this need by improving threat detection accuracy and decreasing false positives. Our system can successfully detect possible dangers among visitors by using CNNs, renowned for their ability to perform image recognition tasks. Integrating cloud computing also makes scalability, real-time processing, and centralized administration possible across several hospital locations. This rigorous testing and evaluation technique improves hospital security requirements reliably and resiliently. It presents novel methods to transform visitor screening fundamentally, solving the challenge of keeping patients, staff, and facilities secure from evolving dangers. The proposed method greatly improves hospital security since it might increase safety and reduce dangers. © 2025 ieee.

关键词： Convolutional neural networks

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Function Scheduling with Data Security in Serverless computing Systems 17

Function Scheduling with Data Security in Serverless Computi...

引用

17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Saha, Srestha Pandey, Arvind Addya, Sourav Kanti Brata Nath, Shubha Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India National Institute of Technology Karnataka Department of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798331531195

In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user's QoS. © 2025 ieee.

关键词： Simulated annealing

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A Semantic Driven Framework for Energy Efficient Task Scheduling in Dynamic cloud Environment 4

A Semantic Driven Framework for Energy Efficient Task Schedu...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Aarav Kannan, J. Amshavalli, R.S. Venkateswara Reddy, D. Varma, Vegesna Surya Vinay Kavitha, G. Srm Institute of Science and Technology Dept of Biomedical Engineering Chennai India Sathyabama Institute of Science and Technology Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331523923

The rapid expansion of cloud computing has led to increased energy consumption, raising concerns about its environmental impact. Despite advancements in scheduling algorithms, existing methods face critical challenges, including suboptimal energy efficiency, limited adaptability to dynamic cloud environments, and insufficient context-aware decision-making. Recent techniques, such as Meta Reinforcement Learning, Genetic Algorithms, and Energy-Aware Scheduling, show potential but often struggle with high computational complexity, static resource allocation, or scalability issues. These shortcomings result in inefficient resource utilization, elevated energy usage, and difficulties in meeting Service Level Agreement (SLA) requirements. To address these challenges, this study introduces the Energy Efficient Semantic Scheduling Framework (ESSF), a novel approach combining semantic reasoning and energy profiling to optimize task scheduling in dynamic cloud environments. The framework integrates three key components: (1) Dynamic Energy Profiling (DEP) for real-time energy monitoring, (2) Semantic Resource Matching (SRM) utilizing knowledge graphs for context-aware allocation, and (3) Adaptive Workload Balancing (AWB) to dynamically redistribute tasks for improved resource utilization and energy efficiency. Experiments using the Google Cluster Trace Dataset demonstrate that ESSF reduces energy consumption by up to 25% compared to traditional methods while achieving 90% resource utilization and 97% SLA compliance. These results highlight the potential of the proposed framework to overcome existing limitations, offering a scalable, adaptive, and environmentally sustainable solution for task scheduling in cloud computing. © 2025 ieee.

关键词： cloud computing Dynamic Energy Profiling Energy Efficiency Google Cluster Trace Dataset Knowledge Graphs Semantic Scheduling

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Healthcare Data Protection and Biometric Access on cloud Platforms 6

Healthcare Data Protection and Biometric Access on Cloud Pla...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Gayathri, S. Devaraj, S. Muthupandi, B. Vishwaraj, S. Department of Artificial Intelligence and Data Science K S Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798331522667

This paper discusses about various fingerprint recognition methods, each founded on different principles, in search of a robust and efficient technique. The research concentrates on designing an easy yet efficient alternative for fingerprint recognition. The technique is divided into two main stages: the first is the acquisition of human fingerprint samples, and the second is the development and implementation of an advanced fingerprint recognition system. One of the features of the method is feature extraction using multiple iterations of the two-dimensional discrete cosine transform (DCT) to extract robust features. This method combines thumbprints from both hands for better recognition. The experiments show that the proposed technique is capable of yielding a good recognition accuracy. © 2025 ieee.

关键词： cloud platforms

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Analysis of cloud Service Providers and computing Services in Modern IT Infrastructure 2

Analysis of Cloud Service Providers and Computing Services i...

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2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Himanshu Chopra, Taruna School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (纸本)9798331530389

In recent years, a growth of cloud computing services has revolutionized modern IT infrastructure and left organizations with difficult choices of what service provider to use and how to implement such services. Much research is done on the level of individual cloud services, yet an important gap still exists with regard to comprehensive comparative analyses that evaluate major cloud service providers across multiple dimensions. This research systematically assesses market leaders: Amazon Web Services (AWS), Microsoft Azure, and Google cloud Platform (GCP)-by taking into account their computing services, pricing models, metrics for performance, and security frameworks. Utilizing a mixed methods approach, we analyzed the available quantitative performance data of 2,500 cloud deployments across 12 regions from all over the world, in addition to carrying out qualitative assessments by structured interviews with 150 IT decision-makers. Our results have large differences in service reliability (±15%), cost-effectiveness (23% variance in similar workloads), and integration capabilities across providers. Of course, in compute-intensive workloads, AWS did better, but in integrating enterprises, Azure won, and in machine learning, one could see clear advantages with GCP. The study concludes that optimal cloud service selection is workload-specific rather than provider-centric, so that organizations can benefit from the multi-cloud strategy. Such insights define a decision frame for organizations that are using or planning to adopt cloud services, thereby contributing to the growing body of knowledge in cloud computing infrastructure management. © 2025 ieee.

关键词： cloud platforms

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Smart Environmental Monitoring System for Air and Water Quality Index

Smart Environmental Monitoring System for Air and Water Qual...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Tripathi, Ansh Dubry, Saransh Rama, P. Angayarkanni, V. Kalaivani, J. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Srmist Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies India

ISBN: (纸本)9798331529833

The quality of air and water is essential for human health, environmental sustainability, and ecosystem balance. However, pollution from industrial activities, urbanization, and agricultural practices has made effective environmental monitoring a global priority. Traditional monitoring methods, which rely on manual sampling and laboratory analysis, are often time-consuming and lack real-time capabilities. This paper presents a Smart Environmental Monitoring (SEM) system leveraging IoT and sensor technology to monitor air and water quality in real time using two key sensors: the MQ135 for Air Quality Index (AQI) and the pH sensor for Water Quality Index (WQI). By focusing on these two critical parameters, the SEM system offers a streamlined yet robust solution for assessing environmental quality. The system collects data continuously, transmits it to a cloud platform for processing, and employs machine learning algorithms to forecast future pollution trends. A user-friendly dashboard displays real-time AQI and WQI levels, historical trends, and predictive insights, empowering users to respond proactively to environmental risks. Deployment results demonstrate the SEM system's capability in detecting pollution patterns, identifying high-risk areas, and predicting pollution spikes during peak traffic and in industrial zones. Comparative analysis with traditional monitoring highlights the SEM system's superior accuracy, scalability, and predictive abilities. Although the SEM system has higher initial setup costs, its operational efficiency and long-term benefits in mitigating environmental risks make it a sustainable choice for public health and environmental management. © 2025 ieee.

关键词： Environmental monitoring

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cloud computing Adoption as IT Strategy in Organizations: A Short Systematic Review 16

Cloud Computing Adoption as IT Strategy in Organizations: A ...

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16th international conference on ENTERprise Information Systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Reis, Ana Fraga, Cristina Gouveia, António Jorge UTAD Quinta de Prados Vila Real5000 Portugal C-MADE/UTAD Quinta de Prados Vila Real5000 Portugal

Over the past decade, cloud computing has solidified its position as the primary platform for managing and processing extensive datasets within organizations. Despite its manifold advantages, integrating cloud computing into an organization's IT strategy poses considerable challenges. This study undertakes a systematic literature review spanning the years 2018 to 2023 to comprehensively understand and categorize the hurdles associated with cloud computing adoption. These challenges span organizational, technical, legal, and financial domains. Notably, technical challenges emerge as the most prevalent, with concerns regarding "privacy and data protection"featuring prominently. Strategically addressing these obstacles is paramount for organizations to optimize the benefits of cloud computing while overcoming potential hindrances during the adoption journey. By navigating these challenges adeptly, organizations can enhance operational efficiency, facilitate innovation, and achieve competitive advantage in today's dynamic business landscape. © 2025 The Author(s).

关键词： cloud platforms

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A Framework for Evaluating Integration Testing Criteria in Serverless Applications 15

A Framework for Evaluating Integration Testing Criteria in S...

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15th international conference on cloud computing and Services science, CLOSER 2025

作者： Winzinger, Stefan Wirtz, Guido Distributed Systems Group University of Bamberg An der Weberei 5 Bamberg96047 Germany

ISBN: (纸本)9789897587474

Serverless applications are based on Function-as-a-Service (FaaS) platforms where serverless functions interact with other cloud-specific services. The integration of these components is crucial for the application’s functionality and must be adequately tested. Testing criteria can help here by supporting developers in evaluating test suites and identifying missing test cases. This paper presents a framework for evaluating integration testing criteria in serverless applications by showing the processes needed for evaluating the criteria. The process defined is shown for some control and data flow criteria and applied to some serverless applications showing the feasibility of the approach. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Coverage Criteria Data Flow FaaS Integration Testing Serverless computing

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Source IP Hashing Algorithm for Load Balancing in cloud Server 6

Source IP Hashing Algorithm for Load Balancing in Cloud Serv...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Gokul, M. Rajkumar, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

The high trend in the adoption of online banking makes complex load balancing highly crucial to maximize computing capacity in the cloud, data management, and security. This research focuses on analyzing the Source IP Hashing method of load balancing where client requests are evenly distributed depending on their source IP address to provide persistence of client sessions and protect against session hijacking among other risks. The proposed framework, captured in Java, follows a systematic procedure of directing the client requests to the most appropriate servers using hash values generated from the IP address of the clients reducing server resource consumption. The paper also yields numerical results comparing throughput and system responsiveness with pretested significant enhancement affirming that even under high traffic the system achieves high responsiveness and reliability. This analysis proves that Source IP Hashing helps deliver high success rates in request routing while maintaining a 99% retention of session persistence and improving server utility. Thus, the study also proves that Source IP Hashing algorithm enhances the safety and security of data even when distributing the practical load among different servers, which makes it a more innovative solution for easing the problems of cloud-based financial services of the present days. © 2025 ieee.

关键词： Information management

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