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Enhancing the Kubernetes Platform with a Load-Aware Orchestration Strategy

SN computer Science 2025年第3期6卷 1-15页

作者： Marchese, Angelo Tomarchio, Orazio Dept. of Electrical Electronic and Computer Engineering University of Catania Catania Italy

Microservice architecture is increasingly favored for building large-scale applications designed for deployment in distributed and resource-constrained cloud-to-edge computing environments. As a cloud-native approach, microservices are particularly powerful due to their loosely coupled, independently deployable, and scalable characteristics. These features enable distributed deployment and flexible integration across robust cloud data centers and heterogeneous, often resource-limited, edge nodes. To fully leverage these advantages, there is a need for advanced placement algorithms that can optimize application performance by utilizing the strengths of microservices. In response to these challenges, we propose to extend Kubernetes with a load-aware orchestration strategy, which enhances its capability to deploy microservice applications within shared clusters characterized by dynamic resource usage patterns. Our approach orchestrates applications dynamically, based on real-time resource usage, allowing for continuous adjustment of their placement to match evolving conditions. Evaluation of a prototype system in a testbed environment demonstrates that this approach offers substantial benefits compared to the standard Kubernetes scheduler. © The Author(s) 2025.

关键词： Cloud continuum Container technology Kubernetes descheduler Kubernetes scheduler Microservice architecture

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Review of Smart Document Verification System for Traffic Rule Violation 15

Review of Smart Document Verification System for Traffic Rul...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Deshmane, Shruti Raghatwan, Jyoti Patil, Sagar Kirad, Yash Gupta, Shweta Dept. of Computer Engineering RMD Sinhgad School of Engineering Pune India

ISBN: (纸本)9798331300579

A prevalent issue with missing or expired vehicle paperwork has prompted the development of a digital service utilizing QR codes within the documentation. This innovation allows Rapid Transport Office (RTO) officers to swiftly access vehicle details and necessary documents via smartphone scans. Traffic cameras equipped with QR code capabilities also verify documentation, issuing fines or notifications when needed. Users benefit from renewal reminders, streamlining processes, and lowering fine risks. This service not only saves time and resources but also encourages compliance with documentation standards, effectively addressing the persistent challenges of missing or outdated paperwork in the realm of vehicle management. © Grenze Scientific Society, 2024.

关键词： QR Code RTO Officer Smart Document Verification Traffic Rule Violation

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Elevating Disease Prediction: A Stacking Ensemble Learning Approach 3

Elevating Disease Prediction: A Stacking Ensemble Learning A...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kavitha, T. Trivikram, Kammara Manobhi Ram Reddy, B. Shashank, S. Sushmitha, H. Rahul, B. New Horizon College Of Engineering Dept. Of Computer Engineering Bangalore India

ISBN: (纸本)9798350318609

Timely and accurate disease diagnosis is critical for effective treatment and cost reduction. This project develops a web app utilizing machine learning to predict diseases from user-entered symptoms and recommend nearby specialists. An ensemble model stacking Logistic Regression, Random Forest, KNN, MLP, and SVM classifiers with XGBoost as the meta-learner was created using a diverse dataset of symptoms, diseases, and medical specialists. The Flask-based app features user authentication, symptom input, and Google Maps API integration for doctor recommendations. The ensemble model accurately predicts potential diseases and relevant specialists nearby, providing an interactive platform for instant diagnosis and treatment access to reduce complications and costs. Rigorous testing ensured seamless deployment, empowering early disease identification and access to appropriate healthcare. The ensemble achieved 97% accuracy, outperforming individual base learners, while KNN emerged as the top individual model with 92.3% accuracy. © 2024 IEEE.

关键词： Forecasting

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Multiple Approaches Towards Authentication Using Keystroke Dynamics 2

Multiple Approaches Towards Authentication Using Keystroke D...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Arsh, Aditya Kar, Nirmalya Das, Smita Deb, Subhrajyoti Dept. of Computer Science & Engineering NIT Agartala Tripura India Dept. of Computer Science & Engineering ICFAI University Tripura India

In the realm of global computer security, safeguarding sensitive data and computer systems stands as an ongoing challenge. Striking the delicate balance between convenient access for legitimate users and thwarting imposter attacks is imperative. Traditional authentication methods, primarily reliant on username and password schemes, have been the longstanding cornerstone of cyber system authentication. Yet, this conventional approach is marred by inherent vulnerabilities, including password exchange, shoulder surfng, brute force attacks, dictionary attacks, speculation, phishing, and emerging threats. In response to these shortcomings, biometrics technology has emerged as a reliable alternative for authentication and verification, with Keystroke Dynamics being a well-established method. This research advances Keystroke Dynamics-based authentication through cutting-edge technologies, including machine learning, deep learning, and neural networks. A quantitative assessment demonstrates remarkable results - Machine Learning achieves 99.9% accuracy with the Random Forest model, Deep Learning combines CNN with GRU to reach 99.31% accuracy, and the DBN model registers at 98.01%. In the realm of neural networks, Bi-CNN and FFM-NN excel, with accuracy rates of 96.8% and 94.7%, respectively. These findings lay the foundation for robust, secure authentication systems, ensuring the protection of sensitive data and computer assets in our interconnected world. © 2024 Elsevier B.V.. All rights reserved.

关键词： Authentication

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Assessment Technology for Physiotherapy Practices using Deep Learning 2

Assessment Technology for Physiotherapy Practices using Deep...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Vineeth, Nandhini Darshan, P.M. Deepu, K. Daragaj, Arihant Prabhu, Ananth G. Bms College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370188

The innovative technology for physiotherapy assessment employing deep learning aims to offer a precise and streamlined approach to evaluating physiotherapy needs. Conventional manual assessment techniques utilized in physiotherapy often consume time and are susceptible to human inaccuracies, resulting in erroneous diagnoses and treatment strategies. This technology tackles these challenges by employing sophisticated deep learning algorithms that identify angles and offer auditory guidance to patients based on their posture. The process commences with capturing the patient's video through a webcam and extracting frames utilizing OpenCV. Following this, the frames undergo processing via a media pipe library, a sophisticated tool designed for body key point detection. These identified key points are subsequently utilized to connect essential body segments for a particular exercise and compute the angular measurements between them. Using these measurements, the system assesses the correctness of the posture, offering audio feedback with repetition counts for accurate postures and tailored guidance for incorrect ones. The audio cues are exercise-specific, delivering precise instructions to aid the patient in refining their posture. The system also keeps track of patient's progress over time and provides a visual display of their improvement. This helps the patient monitor their progress and provides motivation for them to continue with their therapy. The use of learning algorithms and the media pipe library makes this solution an accurate, efficient, and cost-effective method for physiotherapy assessments. © 2024 IEEE.

关键词： Deep learning

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Towards Automated Lip Reading: Developing Marathi Lip Reading Datasets and Neural Network Frameworks 4

Towards Automated Lip Reading: Developing Marathi Lip Readin...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Kulkarni, Apurva Kirange, Dnyaneshwar Khemachandra Ssbt College of Engineering and Technology Dept. of Computer Engineering Jalgaon India

ISBN: (纸本)9798350349900

This paper introduces an innovative method for automating lip-reading, with a specific focus on the Marathi language. Lip-reading plays a crucial role in aiding those with hearing impairments, but automating it presents significant challenges, especially for languages like Marathi lacking sufficient datasets. To tackle this, we propose a novel approach to automatic lip-reading, accompanied by the development of a specialized Marathi dataset. Leveraging advancements in computer vision and deep learning, our model deciphers linguistic content from lip movements, trained on this dataset. We employ various neural network architectures, including feed-forward, recurrent, and convolutional networks, to extract vital visual features crucial for accurate language interpretation Our primary goal is to provide a robust solution for automated lip-reading tailored specifically to regional languages like Marathi, aiming to enhance accessibility for individuals with hearing impairments, particularly in linguistically diverse contexts. This paper primarily discusses detailed exploration of dataset creation, neural network architectures for lip-reading system, we demonstrate the feasibility and potential impact of our approach. Our study underscores the significance of giving priority to regional languages in technological advancements to promote inclusivity for all individuals. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated Exam Script Checking using Zero-Shot LLM and Adaptive Generative AI

Automated Exam Script Checking using Zero-Shot LLM and Adapt...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Himi, Shinthi Tasnim Tanzila Monalisa, Natasha Sultana, Shirin Afrin, Anika Hasib, Khan Md Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The manual grading of exam scripts is a labor-intensive process, often plagued by subjectivity and inconsistency. This study presents an innovative approach to automating the evaluation of exam scripts using the Large Language Model (LLM) integrated with Zero-Shot Learning (ZSL) and Generative AI. Our system leverages the capabilities of GPT-4 to generate and evaluate answers, applying similarity measures to ensure accuracy and fairness in grading. The model's adaptability through ZSL allows it to assess new questions without additional training. The system also incorporates iterative refinement techniques to enhance the quality of standard answers over time. Performance evaluations demonstrate a low error rate, with an average relative error of 1.29% for annotated questions and 1.67% for non-annotated questions compared to human graders. The performance underscores the automated system's transformative potential in revolutionizing educational assessments, offering a consistent, fair, and highly efficient alternative to traditional grading methods. © 2024 IEEE.

关键词： Zero-shot learning

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Face Generation and Recognition in Forensic Science 11

Face Generation and Recognition in Forensic Science

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Ravi, Gayathri Joy, Heynes Jitto, Jeffin Joshy, Jocelyn Jose, Jisha Mary Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350379136

Suspect identification can be challenging for forensic investigations since standard procedures are time-consuming and prone to mistakes. This calls for the creation of novel approaches utilizing developments in machine learning (ML) and artificial intelligence (AI). In order to overcome these obstacles, the proposed Face Generation and Recognition in Forensic Science will make use of sophisticated recognition algorithms and AI-based face generation models. Fully trained Stable Diffusion model is applied to generate high-quality face images from textual descriptions. Image Generation, Text Guided Image Manipulation using Denoising Diffusion Probabilistic Models (DDPMs), and Dataset Matching are the three primary components of the process. Using a stable diffusion model, Image Generation quickly creates high-resolution images from word prompts by combining an autoencoder (VAE), U-Net, and text encoder. With the introduction of an alternate noise space for DDPMs, Text Guided picture Manipulation makes it possible to do meaningful picture altering tasks in response to text prompts. VGG-16 , a convolutional neural network architecture is used in dataset matching to extract features and calculate similarity, which makes dataset alignment and comparison easier. The suggested methodology gives law enforcement authorities effective tools for identifying suspects, which represents a substantial development in forensic investigations. The project intends to increase the efficiency of criminal investigations, accelerate the matching process with large datasets, and enhance the accuracy of facial sketches by utilizing AI and ML approaches. The approach's ability to produce coherent and contextually relevant face images is validated by experimental results, which also show the approach's potential for speeding up the conclusion of criminal cases, particularly unsolved cold cases. All things considered, Face Generation and Recognition in Forensic Science is a promising step in st

关键词： Convolutional neural networks

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5G User Equipment (UE) Positioning and Localization Estimation using Machine Learning

5G User Equipment (UE) Positioning and Localization Estimati...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Survase, Sanket Diwan, Raman Lal, Kumari Nidhi Kumar, Shishupal VNIT Nagpur Dept. of Computer Science and Engineering Nagpur India IIIT Nagpur Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350378092

The emergence of 5G technology has ushered in a new era of connectivity, making communications faster and cheaper. One of the main challenges of 5G networks is accuracy and efficiency, which are important for many applications such as driver less cars, smart cities and virtual reality. This research paper explores the integration of machine learning techniques to improve the accuracy and reliability of 5G infrastructure. The plan uses rich data generated by 5G networks, combining various parameters such as signal strength, flight time and angle of arrival. Machine learning algorithms are used to process this data and create good models for the correct location. The system is designed to adapt to dynamic and complex urban environments where traditional methods often encounter limitations. The main contribution of this research includes the development of a new machine learning-based distribution algorithm suitable for 5G networks, a comprehensive evaluation of performance comparison with the existing system, and evaluation of recommendations for feasibility and implementation. The results showed significant improvements in accuracy and reliability, demonstrating the potential of machine learning to revolutionize the 5G workplace. © 2024 IEEE.

关键词： Adversarial machine learning

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Gaze Detection and Text Extraction from Images using CNN with DBSCAN-based Filter Design 11

Gaze Detection and Text Extraction from Images using CNN wit...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Jose, Jithin Ramanujan, Ajeesh College Of Engineering Trivandrum Dept. Of Computer Science And Engineering Trivandrum India

ISBN: (纸本)9798350379136

Gaze detection and text extraction are pivotal technologies in the domain of human-computer interaction and computer vision, enabling applications such as assistive technologies, user interface optimization, and automated content analysis. This paper presents a novel method that identifies the user's gaze direction and extracts the necessary portion of text, which constitutes the user's region of interest, using a Convolutional Neural Network (CNN) integrated with a spatial attention mechanism. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based filter is incorporated into the system to remove textual noise from the extracted image, thereby improving the model's accuracy and reducing computation time. The proposed method demonstrates reduced dependency on the CNN compared to previous approaches, resulting in enhanced real-time operation capability and lower latency. Computational optimization and real-time performance were thoroughly evaluated, showing that our system not only achieves high accuracy in gaze detection and text extraction but also maintains efficient processing speeds suitable for dynamic applications. This advancement offers significant improvements for applications in assistive technology, educational tools, and interactive media, where precise and efficient gaze detection and text extraction are critical. © 2024 IEEE.

关键词： Convolutional neural networks

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