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Resource Allocation in cloud computing Using Quasi-Opposition Learning Based Aquila Optimizer

Resource Allocation in Cloud Computing Using Quasi-Oppositio...

引用

international conference on 6G communications Networking and Signal Processing, SGCNSP 2023

作者： Kumari, K. Aruna Muvva, Vijaya Bhaskar Reddy Kumar, K. Sudheer Rai, B. Karunakara Shakir, Aboothar Mahmood Department of CSE SRKR Engineering College Bhimavaram Chinaamiram India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences -Shinas Shinas Oman School of Computer Science and Artificial Intelligence SR University Warangal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9789819778751

The resource allocation is focus on enhancing the service provider proceedings and obtaining customer satisfaction by Service Level Agreements (SLA). cloud technology has significantly transformed the organizations to manage and allocate their resources. The cloud users are choosing sources of application distribution exclusive of cooperating Quality of Service (QoS) necessities. To overcome this issue, this paper proposed a Quasi-Opposition Learning based Aquila Optimizer (QOL-AO) for resource allocation in cloud. The virtualization supports to implement task using resource availability and reduces the response time. The QOL-AO employs the hunting approach for obtain the effective and efficient resource allocation in cloud. The Performance of QOL-AO is estimate through execution time, response time and energy consumption for various number of Virtual Machines (VMs). The QOL-AO attained less execution time of 18.568 ms and response time of 0.095 s than existing algorithms like Emperor Penguin Optimization (EPO) and Particle Swarm Optimization (PSO). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： cloud platforms

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cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming 3

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

引用

3rd international conference on Intelligent Systems, Advanced computing, and communication, ISACC 2025

作者： Ilambirai, R.C. Barkathulla, A. Mahalakshmi, G. Babu, J. Jagan Prabu, K. Srinivasan, C. SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity. © 2025 IEEE.

关键词： Robotic arms

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RL with Balanced Reward and Masking Mechanism for Multi-NUMA Virtual Machine Scheduling 19

RL with Balanced Reward and Masking Mechanism for Multi-NUMA...

引用

19th international conference on Ubiquitous Information Management and communication, IMCOM 2025

作者： Gao, Yucen Cheng, Yunlong Ping, Chan Tin Gao, Xiaofeng Chen, Guihai MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331507817

With the rapid development of data center and cloud computing, the importance of resource management is increasing in recent years. In this paper, we focus on the virtual machine scheduling problem in resource management, that is, how to allocate the limited virtual machine resources when a request arrives. Existing works usually model the virtual machine scheduling problem as a bin-packing problem, but they do not consider the multi-NUMA architecture servers that have been widely used in recent years. Compared with the traditional symmetric multiprocessing architecture, the multi-NUMA architecture has the advantages of faster access time and higher energy efficiency, which is more suitable for dealing with today’s scenarios with a large number of computing requirements. In this paper, we first formalize the multi-NUMA virtual machine scheduling problem as a combinatorial optimization problem and explain why it is suitable to be solved by reinforcement learning. A reinforcement learning model called BM-DQN is then proposed, which designs a balanced reward function suitable for the multi-NUMA architecture and adopts a masking mechanism to screen out the optional servers in each step of reinforcement learning, so as to improve the solution performance. Experimental results on the open-source real-world virtual machine scheduling dataset of Huawei cloud show that BM-DQN is superior to the existing methods in terms of the number of completed requests. © 2025 IEEE.

关键词： Resource allocation

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Optimizing Blood Bank Management with cloud-Hosted Long Short-Term Memory Models for Inventory Forecasting and Utilization 3

Optimizing Blood Bank Management with Cloud-Hosted Long Shor...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Devi, R. Aiyshwariya Sree Southry, S. Rajanarayanan, S. Sekaran, S. Chandra Sivaraman, Audithan Srinivasan, S. R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Department of Computer Science and Engineering Tamil Nadu Salem India P.S.V College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Krishnagiri India Department of Information Technology Jafferpur New Delhi India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

This research presents an innovative method for blood bank management using cloud-based Long Short-Term Memory (LSTM) models for precise inventory forecasting and optimization. The objective of this research is to increase blood bank efficiency by utilizing LSTM models to accurately estimate demand, optimize inventory levels, and enhance overall resource utilization. The system uses cloud computing to provide real-time demand forecasting, minimizing blood waste and assuring a reliable supply for healthcare requirements. LSTM models use historical data to identify long-term trends and variations in blood demand, facilitating accurate inventory forecasting. This predictive system optimizes the allocation process, resulting in improve resource utilization and increased operational efficiency. In compared to traditional techniques, the proposed system provides a more scalable and flexible solution by using cloud technology to provide continuous monitoring and dynamic modifications of inventory levels. It provides a robust framework for management of blood supply and enhancing patient care for improved resource management. Future enhancements may include broadening the system to incorporate real-time multi-source data integration and investigating hybrid machine learning models to better optimize demand forecasts. © 2025 IEEE.

关键词： Efficiency

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IoT based Line of Control Monitoring System 2

IoT based Line of Control Monitoring System

引用

2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Petkar, Taniya Faculty of Engineering and Technology Department of Computer Science And Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughout critical border regions, especially the Line of Control, by utilizing IoT devices, sensors, and communication technologies. The coverage, timeliness, and data quality of conventional border surveillance systems are frequently compromised. As opposed to this, the suggested system makes use of a wirelessly networked network of intelligent sensors, such as infrared, acoustic, and video cameras. By utilizing sophisticated image processing algorithms and machine learning to analyze the gathered data, the system is able to differentiate between regular activities and any security risks. To help security staff make decisions quickly, real-time updates are transmitted to a centralized control center. Integration with cloud computing also makes data analysis, retrieval, and storage simpler and guarantees data accessibility for forensic investigations and trend research. © 2025 IEEE.

关键词： Network security

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Experience Using Systematic Mapping Studies to Foster Knowledge Discovery in Emerging Technology Fields 17

Experience Using Systematic Mapping Studies to Foster Knowle...

引用

17th international conference on Computer Supported Education, CSEDU 2025

作者： Castellanos-Ardila, Julieth Patricia School of Innovation Design and Engineering Mälardalen University Västerås Sweden

ISBN: (纸本)9789897587467

Emergent technology requires fast changes in educational content and more student engagement beyond the classroom. Therefore, this paper proposes a learning methodology to foster knowledge discovery in these fields using a research-based approach. In particular, we promote active learning with the use of Systematic Mapping Studies (SMS), which bring students closer to in-demand topics in emerging technologies. We test our methodology by using it in the project module of a cloud computing course. We also evaluate the methodology in terms of student outcomes, i.e., work products and their opinions. From the analysis of this evaluation, we describe advantages and possible lines of action for future improvements. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Active learning

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Integrated IoT based Smart Agriculture Monitoring System 6

Integrated IoT based Smart Agriculture Monitoring System

引用

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

作者： Saraswathi, R. Vijaya Rahul, L. Lokeswar, S. Varma, R. Vathsav Ajay, U. Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331522667

The adoption of IoT technology in agriculture is revolutionizing traditional farming, making it a data-driven and resource-efficient field. Proposed paper introduces an IoT-based smart agriculture monitoring system designed to provide farmers with accurate real-time insights for improved crop and resource management. The system incorporates a network of sensors to monitor essential environmental variables like soil moisture and temperature. These signals are processed by a microcontroller and securely transmitted to a cloud platform for advanced analytics. A mobile-friendly application offers actionable insights, notifications, and remote access, facilitating informed decision-making. Built on a Wi-Fi-based infrastructure, the system ensures reliable communication even in areas with inconsistent internet connectivity. Field implementations reveal notable advancements in resource utilization, crop yields, and labor efficiency. Additionally, an embedded AI module forecasts crop prices with 92% accuracy, enabling farmers to optimize sale timings for increased profitability in competitive markets. This comprehensive solution highlights the transformative potential of IoT in fostering sustainable and profitable agricultural practices. © 2025 IEEE.

关键词： Smart agriculture

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1st international conference on Innovation in Electrical Power engineering, communication, and computing Technology, IEPCCT 2019

1st International Conference on Innovation in Electrical Pow...

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1st international conference on Innovation in Electrical Power engineering, communication, and computing Technology, IEPCCT 2019

ISBN: (纸本)9789811523045

The proceedings contain 58 papers. The special focus in this conference is on Innovation in Electrical Power engineering, communication, and computing Technology. The topics include: Studies on off-grid solar photovoltaic-powered micro-irrigation system in aerobic rice cultivation for sustainable agriculture and mitigating greenhouse gas emission;application of iot in predictive maintenance using long-range communication (LoRa);PID-based electronic load controller for three-phase synchronous generator;survey of sentiment analysis of political content on twitter;Load frequency control incorporating electric vehicles using FOPID controller with HVDC link;automatic generation control in deregulated power market using sunflower optimization algorithm;implementation of monte carlo simulation to the distribution network for its reliability assessment;retinal image segmentation using gabor transform with preprocessing and hysteresis thresholding;Multi-level asymmetrical inverters using two DC ports with less number of switches and conversion stages;Blind feature-based steganalysis with and without cross validation on calibrated JPEG images using support vector machine;fuzzy granular computing-based controller design for inverted pendulum system with delay;electrical faults in photovoltaic modules: Analysis, characterization and detection;detection and classification of voltage sag causes based on S-transform and extreme learning machine;accelerated deep learning in proteomics—A review;DSTATCOM using limit cycle oscillator FLL with optimized gains of voltage error controllers;global horizontal irradiance prediction using deep neural network framework;A hybrid approach for path planning of multiple AUVs;a hybrid framework for fault classification and location in power distribution system using wavelet and support vector machine;effect of soiling on power output of a photovoltaic system.

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3rd international conference on Machine Learning, cloud computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd international conference on Machine Learning, cloud computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819616930

The proceedings contain 27 papers. The special focus in this conference is on Machine Learning, cloud computing and Intelligent Mining. The topics include: Decentralized Optimal Tracking Control of MRMs Under Health Indicator-Based Event-Triggered Mechanism;iterative Learning Control for Nonlinear Switched Continuous-Time Systems with Time-Varying Delay;Model Migration and Joint communication and computing Resource Allocation in Native AI Wireless Networks;FTB-RRP Control for 2-D Continuous-Discrete Systems in Roesser Model;Optimal UAVs Placement for TDOA Localization Method Based On An Improved Grey Wolf Optimization Algorithm;A Bat-BP Optimization Algorithm Based System of Dry Ice Decontamination for UAV;Optimal Resource Allocation Algorithm for Multi-user AI Tasks in Native AI Networks;study on Non-intrusive Load Monitoring Method Based on K-means Clustering and Dual Convolutional Neural Networks;research and Application of Computer Vision and Predictive Maintenance in Health Management of Conveying Equipment;attitude Stabilization Control for Unmanned Aerial Vehicle with Robotic Arm Based on Fixed-Time Observer Active Disturbance Rejection Control;state Estimation and Control of Switched Neural Networks with Mode-Dependent Hybrid Dwell Time;Distributed Consensus Control Research of Unmanned Aerial Vehicle (UAV) Swarms Based on Lennard-Jones Potential;lifetime Prediction of Fuel Cell Composite Operating Conditions Based on Genetic Algorithm Optimized Elman Neural Network;a Tiered Native Digital Twin for Telco Networks;defender: The Possibility of Repairing Jailbreak Defects;research on Special Vision Recognition and Fault Alarm System for Vertical Roller;malicious Traffic Classification Algorithm Based on Multimodal Fusion;Enhanced Affine Formation Control in Multi-agent Systems with Disturbance Rejection Using RISE Controller;model Predictive Control for Contour Control in Biaxial System.

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3rd international conference on Machine Learning, cloud computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd international conference on Machine Learning, cloud computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819624676

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