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2023 IEEE international conference on Technology Management, Operations and Decisions, ICTMOD 2023

作者： Tahiri, Sara Choukhairi, Mouad Fakhri, Youssef Amnai, Mohamed Ibn Tofail University Faculty of Sciences Lari Department of Computer Science Kenitra Morocco

ISBN: (纸本)9798350313352

The Internet of Things (IoT) has emerged as a transformative paradigm, enabling seamless connectivity among a vast array of intelligent devices. However, the exponential growth of connected things has led to increased strain on communication networks and processing mechanisms, subsequently impacting the Quality of Service (QoS) experienced by end-users. In this paper, we explore the significance of IoT and Big data and their intertwined relationship. We present a comprehensive analysis of IoT's QoS, which comprises three critical dimensions - QoS object, QoS communication, and QoS computing. To mitigate the challenges posed by burgeoning IoT deployments, we propose leveraging Big data techniques for the adaptive management of these QoS dimensions. By harnessing Big data analytics, we aim to optimize the performance and efficiency of IoT networks and computing infrastructure. Our research highlights the vital role of data-driven strategies in sustaining reliable and high-performing IoT ecosystems, ultimately fostering seamless interconnectivity between intelligent devices and improving overall user experience. © 2023 IEEE.

关键词： Internet of things

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Fine-Grained Elasticity for Big data Stream processing of IoT Applications

Fine-Grained Elasticity for Big Data Stream Processing of Io...

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2023 RIVF international conference on computing and Communication Technologies, RIVF 2023

作者： Pham, Linh Manh Tran, Manh-Dong VNU University of Engineering and Technology Hanoi Cau Giay Viet Nam Vietnam Academy of Science and Technology Institute of Information Technology Hanoi Cau Giay Viet Nam

ISBN: (纸本)9798350315844

In an era when technology is developing rapidly like nowadays, there will be a large amount of data generated from technological devices, especially Internet of Things (IoT) devices, and so the problem raised is how to make effective use of that amount of information. To do this, it is necessary to build a system that can handle big data with the ability to scale resources depending on the amount of information received, which is able to bring both work and economic efficiency as well as avoid wasting resources. IoT smart homes are becoming more and more popular and these houses are equipped with many IoT devices to improve people's quality of life. Every second these smart devices generate a lot of data, so a real-time processing system will be needed to monitor and control them to suit different needs. Ensuring optimal resource usage in this context may require fine-grained elasticity, a characteristic of Cloud computing. Therefore, this paper focuses on solving the big data processing and the implementation of fine-grained elasticity on the Apache Storm platform as a solution to improve the efficiency of resource consumption during the operation of the IoT applications. We also tested and evaluated the pros and cons of the proposed solution when applied to Storm worker nodes. Experiments are based on the DEBS 2014 practical problem to find an appropriate solution to process energy usage data sent over MQTT protocol from large number of smart plugs placed at 40 smart buildings in a German city. © 2023 IEEE.

关键词： Elasticity

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A Multi-Connectivity Architecture with data Replication for XR Traffic in mmWave Networks 22

A Multi-Connectivity Architecture with Data Replication for ...

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22nd international-Federation-for-Information-processing (IFIP) Networking conference (IFIP Networking)

作者： Javed, Muhammad Affan Liu, Pei Panwar, Shivendra S. NYU Dept Elect & Comp Engn Tandon Sch Engn Brooklyn NY 10003 USA

ISBN: (纸本)9783903176577

mmWave communications are paving the way for next generation cellular networks due to their inherent ability to provide high data rates and mitigate interference. However, the intermittency of mmWave links and the advent of low-latency eXtended Reality(XR) applications presents a conundrum for satisfying strict QoS constraints. We propose a multi-tiered multi-connectivity network architecture which exploits mmWave macro-diversity to allow users (UEs) to connect to multiple base stations (gNBs) simultaneously and rapidly switch between them. The power of our proposed architecture lies in multiple tiers of multi-connectivity, wherein we selectively replicate UE data at a subset of associated gNBs in order to improve the response time to blockages. We evaluate the performance of XR traffic with standard scheduling algorithms. Our results show that connecting to multiple gNBs and enabling the scheduler to rapidly switch between them shields the UEs from higher handover delays and minimizes data plane interruptions. Although we show that our network architecture allows for much better performance even with conventional scheduling algorithms, we also highlight the need for better scheduling algorithms optimized for the multi-connectivity paradigm.

关键词： multi-connectivity millimeter wave mmWave handover blockages low latency XR applications quality of service

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Running Serverless Function on Resource Fragments in data Center 23rd

Running Serverless Function on Resource Fragments in Data Ce...

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23rd international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Chu, Yukang Huang, Wenhao Zhao, Laiping Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (纸本)9789819707973;9789819707980

Repetitive scheduling of resources in data centers leads to a large number of transient resource fragments. Serverless computing can decompose long-period mixed deployment applications into small-volume and short-period functions. Fine-grained functions can effectively respond to the transient fluctuation behavior of fragments. However, the fluctuation characteristics and predictability of resource fragments in the cloud environment vary greatly, which may still interfere with the execution process of functions. In this paper, we introduce Tianxuan, a function-level resource scheduler. It classifies the resource fragments in the cloud environment according to their usage forms into unallocated and unused fragments and extracts the fluctuation characteristics from open-source datasets of each type of fragment. Then, it builds a fragment feature classification-aware spatiotemporal prediction model based on these characteristics and schedules functions on spatiotemporally complementary fragments to achieve resource efficiency by reducing the probability of resource contention. Experimental results show that Tianxuan improves the prediction accuracy of resource fragments by 15% compared to existing techniques and increases server CPU utilization by 16%-25% and function throughput by 17%-37%.

关键词： transient resource serverless spatiotemporal prediction

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An Adaptive method for solving differential equations 2

An Adaptive method for solving differential equations

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2nd international conference on Statistics, Applied Mathematics, and computing Science, CSAMCS 2022

作者： Li, Li School of Data Science and Intelligent Media Communication University of China Beijing100024 China

ISBN: (纸本)9781510663183

In this paper, the idea of equal arc length method is proposed, and the solver is designed successfully. Through a series of numerical experiments, I found that the equal arc length method can effectively solve the initial value problem of the first order ordinary differential equation, and it is more efficient than other algorithms. © 2023 SPIE.

关键词： Ordinary differential equations

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Analyzing data Scientist Salaries dataset Through Machine Learning algorithms Using Tool 'Orange' 7

Analyzing Data Scientist Salaries Dataset Through Machine Le...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mittal, Shikha Monga, Chetna Bansal, Ankit Singla, Neeraj Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304268

The buzz surrounding artificial intelligence has made machine learning a hot topic right now. Although there are many tools for visualising data, scripting languages are primarily used for model training. The Orange data Mining tool gives you a lot of ways to change how data is preprocessed, how it is displayed, how models are trained, and how models are tested. In order to establish which strategy has the best Classification Accuracy and Precision, the proposed research uses machine learning techniques to predict the size of an organization based on a variety of parameters, including employee experience, income, job type, employee type, etc. The effectiveness of various machine learning techniques, including Naive Bayes, random forests, support vector machines, neural network, logistic regression, was evaluated. Classification Accuracy evaluations are performed by cross validation. For this paper, we consulted the Kaggle dataset. © 2023 IEEE.

关键词： classification confusion matrix cross validation employee dataset machine learning orange tool for data mining

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QoS and Energy-aware Resource Allocation in Cloud computing data Centers using Particle Swarm Optimization Algorithm and Fuzzy Logic System

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international JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第10期14卷 902-912页

作者： Wang, Yu Zhu, Lin Xian Aeronaut Inst Sch Comp Sci Xian 710077 Shaanxi Peoples R China China South Ind Grp Corp Motorcycle Test Technol Inst Xian 710032 Shaanxi Peoples R China

computing has become a viable option for many organizations due to its flexibility and scalability in providing virtualized resources via the Internet. It offers the possibility of hosting pervasive applications in the consumer, scientific, and business domains utilizing a pay-as-you-go model. This makes cloud computing a cost-effective solution for businesses as it eliminates the need for large investments in hardware and software infrastructure. Furthermore, cloud computing enables organizations to quickly and easily scale their services to meet the demands of their customers. Resource allocation is a major challenge in cloud computing. It is known as the NP-hard problem and can be solved using meth-heuristic algorithms. This study optimizes resource allocation using the Particle Swarm Optimization (PSO) algorithm and fuzzy logic system developed under the proposed time and cost models in the cloud computing environment. Receiving, processing, and waiting time are included in the time model. The cost model incorporates processing and receiving costs. Two experiments demonstrate the performance of the proposed algorithm. The simulation results demonstrate the potential of our mechanism, demonstrating improved performance over previous approaches in aspects such as providers' total income, users' total revenue, resource utilization, and energy consumption.

关键词： Cloud computing resource allocation scheduling PSO fuzzy logic

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Predictive Modeling of Cardiovascular Disease Using ML algorithms

Predictive Modeling of Cardiovascular Disease Using ML Algor...

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2023 international conference on data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Vijayalakshmi, M. Cherukuvada, Srikanth Chinnappa, A. Kavitha, G. Soujanya, A. Nareshkumar, R. Care College of Engineering Artificial Intelligence and Data Science Trichy India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chennai Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Kattankulathur India Department of Mathematics Namakkal India Ibrahimpatnam India

ISBN: (纸本)9798350348910

One of the most significant contributors to morbidity in the global population is the ability to forecast a heart attack. Clinical data analysis is considered one of the most significant aspects for making a forecast, and that aspect is cardiovascular illness, a highly important disease. This can be a very useful tool. Using ML to its full potential, this investigation aims to achieve improved accuracy in diagnosing cardiac disease. In this research, we provide a machine knowledge heart attack diagnosis (MLHAP) technique that involves the study of different risk indicators, and prediction for heart attacks is carried out via ML methodologies, including Support Vector Machines, Logistic Regression, Naive Bayes, Knn, and XGBoost. In this method, the ML-HAP method predicts heart attacks. This approach aims to reduce the mortality rate from heart attacks. The forecast has been optimized as much as possible based on many parameters, which has been the primary focus. KNN, with its parameters optimized, made the most accurate prediction of any of the algorithms. The accuracy obtained using KNN Optimization is 85.24, while the accuracy obtained using Logistic Regression is 78.68. The prediction made by machine learning models for recognizing the signs of a heart attack is extremely effective, particularly when using boosting algorithms. © 2023 IEEE.

关键词： Support vector machines

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Energy Saving Techniques for Cloud data Centres: An Empirical Research Analysis 3rd

Energy Saving Techniques for Cloud Data Centres: An Empirica...

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3rd international conference on Machine Learning, Image processing, Network Security and data Sciences, MIND 2021

作者： Shehloo, Arif Ahmad Butt, Muheet Ahmed Zaman, Majid Research Scholar Mewar University Rajasthan India P.G. Deparment of Computer Science University of Kashmir Srinagar India Directorate of IT & Support System University of Kashmir Srinagar India

ISBN: (纸本)9789811958670

Platforms for cloud computing make it possible for end users to get access to computing resources online. The emergence of cloud computing has led to an increase in the use of Apache Hadoop in cloud data centres. MapReduce and the open-source framework Hadoop have generated great interest among both researchers and corporates. Optimising Hadoop’s energy consumption has been identified as a critical issue, with numerous studies undertaken on the topic. FIFO, capacity, and fair scheduling algorithms are the most commonly used scheduling algorithms in Hadoop cloud data centres. This paper examines the nature and non-nature-oriented energy-efficient scheduling techniques that are frequently used in cloud data centres. The studies that have been explored are classified into node management, virtualisation and consolidation, task scheduling, data placement, and infrastructure change categories. A brief discussion of the rationale for each category is included, along with its strengths and weaknesses. Additionally, for computation and I/O-intensive workloads, the paper examines the CPU energy consumption of three commonly used cloud workload scheduling algorithms: FIFO, fair, and capacity. Our goal is to see if the energy usage among the three schedulers differs significantly between two types of workloads and different job sizes. Finally, the cloud computing research community has been provided with a futuristic perspective in the area, based on current trends in energy-efficient algorithms. The findings and directions presented in this study may be helpful to researchers interested in optimising cloud data centre energy consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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Research and Design of Cloud-Based Digital Three-Dimensional Scanning System for Denture Digitization 3

Research and Design of Cloud-Based Digital Three-Dimensional...

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2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Wu, Bangyuan Wuxi Boston International School Wuxi China

ISBN: (纸本)9798350341546

This study delves into the design of a cloud-based digital three-dimensional scanning system for denture digitization. Addressing the requirements of digital denture manufacturing, this article presents a multi-tiered system architecture comprising a user interface, cloud servers, data transmission module, data processing module, and data storage module. Through this system, patients can upload oral model scanning data via the user interface, achieving precise three-dimensional reconstruction and personalized denture design facilitated by cloud server processing and storage. The article also outlines data transmission protocols and security measures, ensuring secure data transmission and privacy protection. Employing a comprehensive array of techniques, including data encryption, end-to-end encryption, data anonymization, and access control, the system safeguards patient privacy and sensitive data. This design furnishes patients with enhanced denture solutions while providing medical professionals with an efficient platform for data sharing and collaboration. © 2023 IEEE.

关键词： Cloud computing technology data transmission denture manufacturing three-dimensional scanning system

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