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Resource Allocation Strategies for Edge and Fog computing in the Cloud Continuum

Resource Allocation Strategies for Edge and Fog Computing in...

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2025 international conference on Intelligent control, computing and communications, IC3 2025

作者： Atwal, Shikha Singh, Dheerendra Chhabra, Amit Chandigarh College of Engineering and Technology Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798331527495

The advent of edge and fog computing has revolutionized the traditional cloud computing landscape by enabling data processing closer to the data source. This paradigm shift addresses limitations such as high latency and bandwidth consumption inherent in centralized cloud models. Effective resource allocation in this context is crucial for optimizing performance and ensuring efficient operation across the cloud continuum, which integrates cloud, fog, and edge computing layers. This paper provides a comprehensive review of current resource allocation strategies employed in edge and fog computing, including heuristic-based methods, machine learning approaches, game theory, auction mechanisms, and Quality of Service (QoS)-aware techniques. Each method's strengths and weaknesses are analysed, highlighting the challenges of complexity, scalability, data requirements, and adaptability. Additionally, this review identifies future research directions that promise to enhance resource allocation efficiency. Key areas of focus include the integration of advanced artificial intelligence and edge intelligence, the use of federated learning for collaborative resource optimization, the application of blockchain technology for secure and transparent resource management, and the development of energy-efficient allocation strategies. The potential of hybrid approaches that combine multiple resource allocation strategies is also explored. By addressing these challenges and leveraging emerging technologies, future research can significantly improve resource allocation in the cloud continuum, ensuring robust, scalable, and efficient computing environments capable of meeting the demands of diverse and dynamic applications. This review aims to guide researchers and practitioners in developing innovative solutions for resource allocation in edge and fog computing, driving the evolution of the cloud continuum. © 2025 IEEE.

关键词： Resource allocation

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Flood Prediction and Adaptive Farming Solutions Using IoT, Machine Learning, and Remote Sensing for Climate-Resilient Agriculture

Flood Prediction and Adaptive Farming Solutions Using IoT, M...

引用

2025 international conference on Intelligent control, computing and communications, IC3 2025

作者： Karthiyayini, J. Jain, Alok Prasad, Kashi Sai Abedi, Tamam Ali Abd Ulabbas Prasanna, Yedida Murugesan, Karthikeyan New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Griet Department of Information Technology Bachupally Telangana Hyderabad India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India

ISBN: (纸本)9798331527495

Flood is one of the most disastrous natural disasters to the people of agriculture, people and infrastructure. Mitigation of these risks and promotion of climate resilient agriculture depends on accurate flood predictions and progressive farming strategies. This paper presents an integrated model consisting of Internet of Things (IoT) sensors, Machine Learning (ML) and Remote Sensing technologies to provide precise, real time flood prediction and adaptive farming options. Using advanced deep learning algorithms and real time sensor data to predict and risk assess floods in real time, the model leverages real time satellite based Synthetic Aperture Radar (SAR). Another function of the model is to offer adaptive farming solutions including optimized irrigation, flood resilient crop selection, and disaster recovery recommendations for regions impacted by the disaster. A hybrid AI technique assimilating the social media data, and blockchain for integrity of data is more effective to make decisions for the local authorities and farmers. This study seeks to fill the gaps in interdisciplinary collaboration, data scarcity and governance and provide a solution for flood management and sustainable agriculture as a whole. © 2025 IEEE.

关键词： Deep learning

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Job Generator for Evaluating and Comparing Scheduling Algorithms for Modern GPU Clouds 15

Job Generator for Evaluating and Comparing Scheduling Algori...

引用

15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Konopa, Michal Fesl, Jan Beránek, Ladislav Department of Data Science and Computing Systems Faculty of Agriculture and Technology University of South Bohemia České Budějovice Czech Republic

ISBN: (纸本)9789897587474

The steep technological and performance advances in GPU cards have led to their increasing use in data centers in the recent years, especially in machine learning jobs. However, high hardware performance alone does not guarantee (sub) optimal utilization of computing resources, especially when the cost associated with power consumption also needs to be increasingly considered. As consequence of these realities, various job scheduling algorithms have been and are being developed to optimize the power consumption in data centers with respect to defined constraints. Unfortunately, there is still no known, widely used, parametrizable dataset that serves as a de facto standard for simulating scheduling algorithms and the resulting ability to compare their performance against each other. The goal of this paper is to describe a simple job set generator designed to run on modern GPU architectures and to introduce the newly created data set suitable for evaluation the scheduling algorithms. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Scheduling algorithms

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Literature Review on Cloud-Based Service-Oriented Architecture for IEC 61499 Distributed control Systems 15

Literature Review on Cloud-Based Service-Oriented Architectu...

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15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Torres, Tomás Gonçalves, Gil Pinto, Rui Department of Informatics Engineering Faculty of Engineering University of Porto Porto Portugal SYSTEC ARISE Faculty of Engineering University of Porto Porto Portugal

ISBN: (纸本)9789897587474

The 4th Industrial Revolution has driven innovations in integrating Information technologies (IT) with Operations technologies (OT). This integration is essential for developing Cyber-Physical Production Systems (CPPS), which enhance distributed automation and optimize industrial production processes. The IEC 61499 standard facilitates this integration through its modularity, reusability, and interoperability, making it crucial for distributed control and system automation. Despite its advantages, IEC 61499’s application is predominantly at the Edge layer, limiting its functionality in higher layers such as the Cloud. To address this gap, Cloud-based Service-Oriented Architectures (SoA) have emerged as a key study area, offering modular, reusable, and scalable services extending beyond the Edge layer. This paper presents a comprehensive literature review focused on expanding the IEC 61499 applications to reconfigure CPPS, by integrating Cloud layer services through SoA. The review highlights advancements, challenges, and future directions in achieving greater modularity, interoperability, scalability, and abstraction within distributed control systems. By synthesizing current research, this work provides insights into the potential enhancements of CPPS using a Cloud-based SoA approach. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Service oriented architecture (SOA)

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Generating Accessibility: Using AI to Improve Higher Education for the Visually Impaired 17

Generating Accessibility: Using AI to Improve Higher Educati...

引用

17th international conference on Computer Supported Education, CSEDU 2025

作者： Schauer, Sophie Simbeck, Katharina Schüßler, Leon School of Computing Communication and Business HTW University of Applied Sciences Berlin Wilhelminenhofstraße Berlin Germany

ISBN: (纸本)9789897587467

Visually impaired students face many obstacles in higher education, including access to adapted course material. While accessibility is a legal requirement in many countries, its implementation is linked to practical challenges for educators. In this paper, we discuss the adaptation of existing course materials for blind students in a case study and propose using generative Artificial Intelligence to facilitate the creation of accessible educational materials, contributing to a more inclusive learning environment. We introduce and evaluate the tool TRAIL to generate accessible textual descriptions of lecture slides. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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A Greedy Dynamic Task Offloading and Service Management in V2V Networks 11

A Greedy Dynamic Task Offloading and Service Management in V...

引用

11th international conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Xing, Hanyang Chengdu China

ISBN: (纸本)9789897587450

This paper introduces an advanced framework for optimizing task offloading and service caching in Vehicle-to-Vehicle (V2V) communication networks. The proposed approach leverages a greedy algorithm to address key challenges such as offloading latency, energy consumption, and system overhead. By incorporating practical factors such as task size, server storage capacity, and task popularity, the framework efficiently allocates tasks, thereby reducing computational delays and enhancing network performance. The effectiveness of the algorithm is validated through comprehensive simulations that demonstrate significant improvements in both time efficiency and resource utilization compared to existing methodologies. The results underscore the potential for future advancements in V2V networks, particularly in enhancing network stability under high-speed conditions and developing robust communication systems that maximize the use of roadside computational resources. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Decision Tree Game Theory Knapsack Algorithm Vehicle-to-Vehicle communication

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Voice over Internet Protocol: Custom SIP Server with QoS Optimization for Real-Time communication 3

Voice over Internet Protocol: Custom SIP Server with QoS Opt...

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3rd international conference on Intelligent Data communication technologies and Internet of Things, IDCIoT 2025

作者： Dinesh Krishna, B.V. Yogendrasai, P. Rajeev, S. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

VoIP is a revolutionary technology for transmission of voice over internet protocol than from plain telephony systems. But current VoIP solutions have some important issues which are packet loss, latency, and jitter especially when the network is in motion. This work outlines the design, implementation and performance of a locally developed VoIP system which includes a SIP Server Node, Sender Node and a Receiver Node. The SIP Server Node was designed nearly from the ground up to handle session initiation, packet forwarding, and adaptive QoS control. Both Sender and Receiver Nodes had facilities to record, compress and playback audio. The system’s performance was evaluated in a LAN environment, achieving significant improvements in Quality of Service (QoS) metrics: achieving low latency under 150 ms, 95% packet recovery and achieving a good audio quality with the MOS of 4.2. Evaluating the obtained results, it can be concluded that the proposed system is efficient in handling the issues of the packet-switched network and providing high-quality real time voice communication. © 2025 IEEE.

关键词： Packet loss

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A Comprehensive End-to-End Solution for Web Security with Cryptography, Multi-Factor Authentication, and Secure communication 3

A Comprehensive End-to-End Solution for Web Security with Cr...

引用

3rd international conference on Intelligent Data communication technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Kanderi Johith Sai, Kandlapalli Aravind Reddy, Adapala Dharshan Reddy, Chinthamreddy Pranay Daiwik Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In today’s digital landscape, securing sensitive information has become an essential priority in the modern digital era for both individuals and organizations. This project introduces a comprehensive web application that integrates advanced encryption and decryption functionalities alongside multifactor authentication (MFA) and secure payment processing. The application supports various cryptographic algorithms, including AES, DES, Triple DES, Blowfish, etc., allowing users to securely encrypt and decrypt data through dedicated routes accessed over secure HTTPS connections. To further enhance security, robust multifactor authentication mechanisms, such as OTP-based challenge-response and QR-code-enabled 2FA, have been implemented to prevent unauthorized access to sensitive operations. The platform also incorporates PGP encryption powered by Keybase, enabling users to securely exchange encrypted emails. Additionally, a secure payment gateway is integrated using Stripe, ensuring sensitive payment data is encrypted and handled safely. With a strong focus on data integrity, user privacy, and modern web security standards, this project delivers an all-encompassing solution for safeguarding sensitive information and facilitating secure communication. By addressing common vulnerabilities and security risks, the platform stands as a reliable and scalable tool for data protection. © 2025 IEEE.

关键词： Authentication Protocol

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Crop Disease Detection using Hybrid Model of CNN and Capsule Networks 3

Crop Disease Detection using Hybrid Model of CNN and Capsule...

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3rd international conference on Intelligent and Innovative technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Chayadevi, M.L. Shetty, Sameeksha Shetty, Shriddhi Bnm Institute of Technology Affiliated to Vtu Dept. of Cse Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Incorporation of DL techniques have greatly advanced in accuracy of prediction in various fields. In this research, our goal is to diagnose crop damage in fields by enabling farmers to identify the crop disease and take suitable actions. The proposed solution leverages hybrid model of CNN and CapsNets for image-based classification, accessible via a web interface, to facilitate this accurate crop disease prediction. After reviewing over 50 CNN articles, we identified that usage of Capsule Networks along with CNN enhances image classification acquiring better spatial relationships. © 2025 IEEE.

关键词： Productivity Accuracy Computational modeling Crops Predictive models Pest control Feature extraction Object recognition Diseases Image classification

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Data Orchestration Platform for AI Workflows Execution Across computing Continuum 15

Data Orchestration Platform for AI Workflows Execution Acros...

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15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Arcas, Gabriel Ioan Cioara, Tudor Engineering and Data Solutions Department Bosch Engineering Center Cluj-Napoca Romania Computer Science Department Technical University of Cluj Napoca Romania

ISBN: (纸本)9789897587474

Cloud AI technologies have emerged to exploit the vast amount of data produced by digitized activities. However, despite these advancements, they still face challenges in several areas, including data processing, achieving fast response times, and reducing latency. This paper proposes a data orchestration platform for AI workflows, considering the computing continuum setup. The edge layer of the platform focuses on immediate data collection, the fog layer provides intermediate processing, and the cloud layer manages long-term storage and complex data analysis. The orchestration platform incorporates the Lambda Architecture principles for flexibility in managing batch processing and real-time data streams, enabling effective management of large data volumes for AI workflows. The platform was used to manage an AI workflow dealing with the prediction of household energy consumption, showcasing how each layer supports different stages of the machine learning pipeline. The results are promising the models are being trained, validated, and deployed effectively, with reduced latency and use of computational resources. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Data streams

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