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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71-80 订阅

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Critical Review of cloud Schedulers in Homogeneous and Heterogeneous Environments: Taxonomy, Challenges, and Future Directions 3

Critical Review of Cloud Schedulers in Homogeneous and Heter...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Soni, Sunil Kumar Awasthi, Monisha Uttaranchal School of Computing Science Uttaranchal University Dehradun India

ISBN: (纸本)9798331527549

Scheduling in cloud computing is a paramount activity that not only schedules the jobs but also focuses on resource provisioning, management, allocation, mapping, load balancing, etc., as well as prioritizing the tasks. The critical review through this paper emphasizes different environments in cloud resources, such as heterogeneous and homogeneous, and also discusses different task and resource scheduling approaches for the same. The overview of cloud and cloud schedulers gives a clear idea about the different tasks of schedulers. In this structured review, which selects the appropriate resources by following the effective review methodology with a specific motivation, different existing scheduling approaches are discussed. Based on the analysis of existing studies, the current research challenges and future directions are also presented in this study. Overall, the taxonomy, challenges, and future directions of the cloud schedulers in homogeneous and heterogeneous environments provide a clear understanding to the readers in order to research advancements in this research area. © 2025 ieee.

关键词： cloud platforms

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cloud computing Resource Prediction Based on Adaptive Selection of Multiple Machine Learning Methods 24

Cloud Computing Resource Prediction Based on Adaptive Select...

引用

8th international conference on Electronic Information technology and Computer Engineering, EITCE 2024

作者： Huang, Chao Wang, Xiaogang Yang, Zhihe Xiao, Dun School of Electronic and Information Shanghai Dianji University Shanghai China

ISBN: (纸本)9798400710094

In modern cloud computing environments, accurate prediction of resource demand is critical to optimize resource allocation and reduce costs. Existing methods are often inadequate in dealing with variable workloads. In this research, we introduce an innovative combined multiple machine learning prediction approach to enhance prediction accuracy and stability. First, a comprehensive dataset is built by segmenting historical resource utilization and extracting feature vectors using dynamic variable windows. The variable window automatically adjusts its size according to the volatility, thus saving resources. Second, a number of different algorithms are used for forecasting, and the optimal method is selected to capture resource utilisation patterns and improve forecasting accuracy. Finally, the system calls the optimal algorithm from the adaptive selector for prediction based on the feature vectors. The experimental results show that the method performs well under various workloads and scenarios and significantly outperforms the traditional method, providing a theoretical basis and practical guidance for improving the efficiency and performance of cloud computing resource management. © 2024 Copyright held by the owner/author(s).

关键词： cloud platforms

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DocQueue: A Scheduling Algorithm for Optimizing Healthcare Resources

DocQueue: A Scheduling Algorithm for Optimizing Healthcare R...

引用

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

作者： Joji, Jeff Johnson, Sobin Angayarkanni, V. Rama, P. Kalaivani, J. Srm Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331529833

DocQueue is a cloud-based system that optimizes patient scheduling in order to maximize the delivery of healthcare by prioritizing cases (dynamically) based on real-time information and availability of resources. It uses a decision-tree algorithm to analyze symptoms in real time, and then flags high-priority cases immediately, but uses a round-robin scheduling mechanism to manage low-priority cases so things are fair. And the AWS based architecture of the system allows for scalable deployment throughout the healthcare continuum. In simulations, these changes have shown to drastically cut down patient waiting time and make better use of resources, and therefore provide better patient care and more efficient operations. DocQueue is different from the usual scheduling systems because it is flexible and data-driven, it can change its schedule based on the real time needs of health care. © 2025 ieee.

关键词： Scheduling algorithms

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Secure and Optimized cloud Storage with Privacy Preservation using Improved SHA - 256 2

Secure and Optimized Cloud Storage with Privacy Preservation...

引用

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

作者： Joshi, Aditi Devarajan, Ganesh Gopal Karuppiah, Marimuthu Sharma, Yogesh Kumar Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Uttar Pradesh Ghaziabad201204 India Presidency School of Computer Science and Engineering Presidency University Karnataka Bengaluru560064 India Department of CSE Dewan V S Institute of Technology U.P Meerut India

ISBN: (纸本)9798331530389

With the increased reliance of enterprises on cloud storage solutions for data management, maintaining safe and efficient storage while emphasizing privacy protection is crucial. With an emphasis on user privacy, this study examines the methods, tools, and difficulties currently involved in attaining safe and effective cloud storage. It looks at access control protocols, encryption techniques like SHA-256, and new technologies that are intended to improve the security and effectiveness of cloud storage systems. The study also addresses the changing trends influencing the security of cloud storage in the future and emphasizes the significance of adhering to privacy laws. © 2025 ieee.

关键词： cloud storage

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Generative AI for Automated Security Operations in cloud computing 4

Generative AI for Automated Security Operations in Cloud Com...

引用

4th ieee international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Patel, Advait Pandey, Pravin Ragothaman, Hariharan Molleti, Ramasankar Peddinti, Diwakar Reddy Broadcom United States Tiffany Co India Athenahealth United States Options Clearing Corporation United States

ISBN: (纸本)9798331518882

New opportunities in cloud computing have brought many new risks that require effective protection of dynamic distributed environments. Introducing a new formative technology, generative AI, to cloud security has far-reaching benefits for automating threat detection, real-time incident addressing, and vulnerability management. This paper focuses on extending generative AI with cloud security tools like AWS GuardDuty and Google cloud Security Command Center;the contemplation of accuracy enhancement and response efficiency highlights its aim. Concerning actual applications such as SOAR systems, the study demonstrates how media industry giants, such as Netflix and JPMorgan Chase, have used AI to minimize risk factors while increasing operational efficiency. The paper also discusses the significant increase in response time, enhanced detection accuracy, and the shift to proactive security strategies brought by generative AI. Drawing attention to AI systems' opportunities, the study examines the subsequent issues connected with AI applications, including over-dependence on AI tools, adversarial risk to models, and the complex nature of decisionmaking in the context of AI systems. The present study also highlights the importance of generative AI in strengthening the defense of the cloud environment, but, at the same time, it recognizes the significance of preventive efforts and planned action plans to manage these technologies efficiently. © 2025 ieee.

关键词： cloud security

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Implementing Resource-Aware Scheduling Algorithm for Improving Cost Optimization in cloud computing 4

Implementing Resource-Aware Scheduling Algorithm for Improvi...

引用

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

作者： Reddy, Kiran Jagannadha Udayaraju, Pamula Pamaiahgari, Venkata Pradeep Reddy Kumar, V. Dilip Walmart BentonvilleAR United States Srm University Department of Cse AP India Wendys International Llc OH United States SRKR Engineering College Department of CSE India

ISBN: (纸本)9798331523923

An uncountable number of computational resources are shared for various applications using a transformative technology called cloud computing. It is an emerging technology that can offer scalable and on-demand resources. However, cost optimization and resource allocation are critical issues due to the increasing number of cloud user and their request. It can be solved by optimising resource allocation by reducing the task/user queue. It is possible only if the requested resource is free;otherwise, it must schedule the same or relevant resource. This paper demonstrates a Resource-Aware Scheduling (RAS) algorithm to increase the speed of resource allocation by mapping the user request with the resource availability. The proposed algorithm examines the log information of the resources, workload behaviour, resource availability, price, task efficiency, and reduced wastage and allocates the resources dynamically. To do that, it maps user information with resource information and schedules based on availability and priority. The simulation-based experiment is carried out in a private cloud space and demonstrates the resource allocation, utilization, response time, and cost. It is also compared with the traditional scheduling algorithm to evaluate the performance of the RAS. The evaluation found that the RAS model is more suitable for cloud resource allocation. © 2025 ieee.

关键词： cloud computing Cost Optimization Resource Allocation Resource-Aware Scheduling Scheduling

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Annotation and Translation of Functions and Discourse Structures: Implementation Based on Collaborative cloud computing Platform 14th

Annotation and Translation of Functions and Discourse Struct...

引用

14th international conference on Frontier computing, FC 2024

作者： Ju, Yangna Department of Foreign Language Teaching Haojing College of Shaanxi University of Science and Technology Shaanxi Xi’an712046 China

ISBN: (纸本)9789819623907

With the continuous development of cloud computing technology, collaborative cloud computing platform, as a new type of cloud computing service model, is gradually receiving people’s attention and favor. The collaborative cloud computing platform provides users with more convenient, secure and reliable cloud computing services with its efficient collaboration and flexible deployment. In this context, research on the annotation and translation of functions and discourse structures based on collaborative cloud computing platforms is particularly important. Based on the above issues, this article discusses the functions and discourse structures annotation and translation of the collaborative cloud computing platform, and uses the data processing capabilities of the collaborative cloud platform to build a good foreign language corpus, quickly process text data, and help users achieve accuracy and higher text recognition translation. The experimental results prove that when the system simulation time is 15 s–30 s and 40–50 s, the system throughput is 11–18 Mbps. This speed is relatively fast, which usually indicates that the system has a faster network connection, faster data processing speed, or better system resource configuration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： cloud platforms

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Estimation of Response Time with Neural Networks in cloud computing System 1

Estimation of Response Time with Neural Networks in Cloud Co...

引用

1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： More, Rajesh Suresh, Sanjna Veerappampalayam Easwaramoorthy, Sathish Kumar Rizvi, Haider Ali Kunnathully, Kannan Gupta, Sahil K. Cloudwerx Wordwide Departmen of AIML Hyderabad India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India School of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Yogananda School of Artificial Intelligence Shoolini University Department of Artificial Intelligence and Machine Learning India Veltech University Department of Research Chennai India Tula's Institute Department of Computer Science and Engineering Dehradun248197 India

ISBN: (纸本)9798331518578

Resource allotment, responsiveness, connection, research of unexplored assets, related resource modeling, and asset scheduling are just a few of the numerous factors and concerns that need to be considered to enhance cloud computing efficiency. The functionality of the cloud has a significant impact on the propagation of cloud architecture. Consequently, it is important to determine the components that influence cloud function to analyze it. Response duration is the primary aspect that significantly affects cloud computation functioning, according to the research. The response duration assistance is among the numerous different variables that may be used to organize resource applications. Artificial neural networks (ANNs) were employed as a technique for evaluating the parameter of pursuit, and a fork-join framework or an architecture with demand partitioning was selected as the queuing paradigm. As a result, this study suggests a load-managing method depending on cloud demand response duration using the ARIMA Estimation of Response Duration Method. The fundamental concept is to employ ARIMA methods to estimate the future response duration, providing a more efficient method of allocating resources with limited values. The results of the investigation demonstrated that the estimations were more precise compared to those that had been recently established. The results of the study indicate that estimation is an outstanding path for load balance and has possibility and value for load balance with displayed response duration. Additionally, the suggested method makes it feasible to acquire estimations for a wider range of cloud structure functioning signs compared to earlier feasible and also to extend the evaluation of the cloud platform to the instance of a structure with a non-Poisson data flow and non-exponential assistance duration. © 2025 ieee.

关键词： cloud platforms

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Enhancing cloud Infrastructure Security with GraphSAGE-Based Intrusion Detection Systems 1

Enhancing Cloud Infrastructure Security with GraphSAGE-Based...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kumar, G. Nanda Kishor Syed, Shakir Mogal, Amit Karbhari Abid, Tahera Perada, Anvesh Suneetha, Sampathirao University of South Florida United States Malla Reddy University Department of Computer Science and Engineering Hyderabad India Bargersville IN United States Mvp Samaj's Cmcs College Department of Computer Science and Application Nashik India Nawab Shah Alam Khan College of Engineering and Technology Department of Information Technology Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798331529246

cloud computing provides scalable and adaptable infrastructure however is significantly susceptible to cyber-attacks. This work presents a hybrid methodology that integrates the GOA and GA for feature selection, alongside the Residual GraphSAGE model for intrusion detection. The hybrid GOA-GA method enhances feature selection by optimizing the balance between exploration and exploitation, mitigating overfitting, and augmenting classification performance. The Residual GraphSAGE model alleviates over smoothing while effectively capturing node and edge interactions for precise intrusion classification. Assessment on a benchmark dataset reveals exceptional performance, attaining an accuracy of 91.74%, surpassing established models such as GCN and ResNet. This study emphasizes the need of hybrid optimization and sophisticated graph-based learning in improving cloud infrastructure security. © 2025 ieee.

关键词： Network intrusion

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Efficient Load Balancing in Software Defined Networks Using Unsupervised Meta-Reinforcement Learning

Efficient Load Balancing in Software Defined Networks Using ...

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

作者： Kulkarni, Prerita Doohan, Nitika Vats Medicaps University Department of Computer Science Indore India Sage University Department of Computer Science Indore India

ISBN: (纸本)9798331512965

New technologies, such as cloud computing and big data, make traditional network management more difficult, while the network's user base continues to expand. Consequently, changes to the traditional network architecture are required. To address this issue and enhance network management compliance, a new idea called software-defined network (SDN) has been proposed. Due to limited network resources and to satisfy the criteria of quality of service, it is necessary to address numerous difficulties, one of which is load balancing (LB), which distributes data traffic among several resources to maximize their efficiency and reliability. The local information of a network is used to design LB in a typical network. Consequently, it is not very accurate. Still, software-defined network (SDN) controllers can see the network from anywhere in the world and make better LB. We provide a set of unsupervised meta- RL algorithms to build smart optimization architecture for SDN LB. © 2025 ieee.

关键词： load balancing Optimization Software-defined networking (SDN) Unsupervised meta-reinforcement learning

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