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Analyzing Dynamic Workload Management and Energy-Efficient Task Scheduling in Cloud computing 9

Analyzing Dynamic Workload Management and Energy-Efficient T...

引用

9th international conference on Communication and Electronics Systems, ICCES 2024

作者： Prashanthi, M. Lalitha, B. Jntua University Department of Cse Ananthapuramu India Jntua College of Engineering Department of Cse Ananthapuramu India

ISBN: (纸本)9798350377972

Cloud computing provides flexible and scalable solutions for diverse applications, but effective task scheduling remains a critical challenge that significantly impacts performance, resource allocation, and operational costs. This paper examines the complexities of task scheduling in dynamic cloud environments, with a particular emphasis on optimizing energy efficiency and improving workload management. Traditional heuristic-based approaches often struggle to address the challenges posed by dynamic, large-scale cloud systems. The paper reviews energy-efficient scheduling strategies for their potential to reduce energy consumption while maintaining system performance. Furthermore, it explores advanced scheduling techniques, including hybrid heuristics, machine learning-based methods, and optimization algorithms. By critically analyzing the current state of research and identifying key challenges, this study aims to propose innovative solutions to enhance resource utilization, minimize energy costs, and improve the overall efficiency of cloud task scheduling. © 2024 ieee.

关键词： Cloud platforms

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Real-time Monitoring System for Safe Drinking Water Safety Using IoT and Cloud computing

Real-time Monitoring System for Safe Drinking Water Safety U...

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2024 international conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Raman, Ramakrishnan Deshpande, Ashish Maharashtra Pune India Maharashtra Pune India

ISBN: (纸本)9798350364828

This research develops an Internet of Things-based system for continuously monitoring and analyzing drinking water quality. The system's main purpose is to provide a reliable and efficient solution to the crucial problem of constantly monitoring and analyzing drinking water to reduce rising concerns about its health. This technology is suggested for real-time analysis of drinking water quality. The NodeMCU platform, an analog TDS sensor, and a waterproof temperature sensor are used to measure various water quality parameters. The sensor information is sent to a cloud server, where it can be stored and analyzed. The system uses advanced cloud computing and data analytics to store, analyze, and evaluate the collected information. These techniques can identify anomalies and patterns and provide valuable insights into water quality. Predictive models are built using historical data to predict changes in water quality and identify risks. An accessible webpage interface stored in the cloud makes the information analyzed and insights available to users. Users such as water authorities, policymakers, and people can view the current water quality status, detect data from past data, and receive alerts in case of abnormal conditions. Contributing to better water management and public health, it provides a secure and efficient method for monitoring and analyzing drinking water quality. © 2024 ieee.

关键词： Water quality

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Efficiency Electrical Power Edge-end computing with Patch-based Low-rank Transformer

Efficiency Electrical Power Edge-end Computing with Patch-ba...

引用

2024 international conference on Energy and Electrical Engineering, EEE 2024

作者： Zhou, Mi Qian, Bin Lin, Xiaoming Zhang, Fan Luo, Yi Wu, Binquan Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangzhou China

ISBN: (纸本)9798350341768

The development of communication technology has made the need to build more reliable and efficient smart power grid systems imminent. The emergence of edge computing has significantly alleviated the pressure of data transmission in systems, enhancing the reliability and stability of cloud-edge based smart grid systems. However, the deep algorithms on which current Edge computing platforms rely increase the overall computation time consumption and memory consumption of the whole system, which is inconsistent with the responsiveness and reliability requirements of smart power grid system services. Therefore, in this paper, we propose Patch-based Low-rank Transformer (PLT) that balances accuracy and efficiency, which can greatly alleviate the computation time consumption and memory consumption of Edge computing platforms. Specifically, we cut the original electric power time series into subsequence-level patches to ensure the accuracy of the model for long and short-term sequential dependencies learning. Then, we introduce a low-rank attention mechanism to reduce the computational consumption of the model, and design a simple but efficient knowledge distillation module that transferring knowledge from the Transformer-based model to a lightweight MLP to speed up the computation of the model at inference phrase. Extensive experiments on 11 public datasets (10 UCR and 1 cluster-trace-v2018 released by Alibaba Cloud) and 13 collected electric power time series datasets show that the proposed PLT outperforms baseline models and reaches the design purpose. © 2024 ieee.

关键词： Power systems computer aided design

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Keyless Lightweight Encipher using Homomorphic and Binomial Coefficients for Smart computing Applications 2

Keyless Lightweight Encipher using Homomorphic and Binomial ...

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2nd ieee international conference on Vision Towards Emerging trends in Communication and Networking Technologies, ViTECoN 2023

作者： Sultana, Sk. Arshiya Ch, Rupa. Malleswari, R. Pavana VR Siddhartha Engineering College Dept. of CSE Vijayawada India

ISBN: (纸本)9798350347982

Globally, advanced technologies play a vital role to offer services to the user. But the major issue raised here is that the data utilization rate increasing day by day which causes attacks on data. With the increasing use of the internet and digital media, data theft, and piracy making became easy due to the lack of proper security services offered techniques. Existing techniques such as DES, AES, RSA are techniques that failed to achieve immutable, and decentralized features with Lightweight enciphers. In spite of this, attackers continue to attack sensitive data due to the technology's fast progress and the inherent fragility. As a result, a homomorphic encryption method was suggested based on the transformed ASCII values assigned to each character in plain text. In this proposed system, the secret key generated is a tetrahedral-based key, a Pentatope-based key. There is no need for the transmission of keys between users. This work proposes novel encryption using Homomorphic operations and binomial coefficients to secure sensitive information related to the academic industry. © 2023 ieee.

关键词： Sensitive data

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A Bird's Eye View on Quantum computing: current and Future trends 20

A Bird's Eye View on Quantum Computing: Current and Future T...

引用

20th international conference on Smart Technologies, EUROCON 2023

作者： Branchini, Beatrice Conficconi, Davide Peverelli, Francesco Sciuto, Donatella Santambrogio, Marco D. Informatica e Bioingegneria Politecnico di Milano Dipartimento di Elettronica Milan Italy

ISBN: (纸本)9781665463973

Quantum computing is a potentially highly-disruptive technology for several domains across the Computer Science field. However, many technological challenges still prevent the construction of a fault-tolerant and reliable quantum computer. These challenges sparked enormous interest in the scientific community, which led to the development of several independent strands of research. Consequently, research in the quantum computing domain is fragmented, making it highly specialized. This can lead to missed opportunities in identifying valuable research directions and contributions from neighboring research areas. For these reasons, this paper provides an overview of the current state of play in quantum computing for different quantum research areas at different layers of the development stack. Furthermore, for each of them, we highlight possible new outlooks that could be pivotal in the near future. © 2023 ieee.

关键词： Quantum theory

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Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural Network for Sports Activity Recognition with Wearable Inertial Sensors 4

Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural...

引用

4th international conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Imran, Hamza Ali Muhammad, Shaida Wazir, Saad Ikram, Ataul Aziz Arshad, Obaidullah National University of Sciences & Technology Department of Computing Islamabad Pakistan Korea Advanced Institute of Science & Technology School of Computing Daejeon Korea Republic of National University of Computer & Emerging Sciences School of Engineering Islamabad Pakistan

ISBN: (纸本)9798350391862

The current prevalent approach of the Internet of Health and Medical Things entails proactively preventing disease onset through routine monitoring of individuals' physical activities, making Human Activity Recognition (HAR) and Behaviour Analysis an important field of study. Sports Activity Recognition (SAcR) is a subset of HAR that focuses on identifying athletic movements. Computer Vision, Environmental Sensors, and Wearable Sensors are the three main methods for monitoring such activities. When the merits and limitations of these methods are considered, Wearable Sensors emerge as the most practical option. This study introduces the Mukhtasir-Khail-Net, a model with only 651 parameters that efficiently uses addition layers. When applied to six sports activities, the model achieves an impressive average accuracy of 98.865% using inertial sensor data generated by wearable sensors. For benchmark purposes, the presented model is also evaluated on the WISDM 11 dataset which is a Human Activity Recognition dataset from Fordham University and has achieved an accuracy of 94.24%. © 2024 ieee.

关键词： Convolutional neural networks

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Utilizing Deep Learning to Enhanced Security in the Internet of Medical Things via Intrusion Detection Systems

Utilizing Deep Learning to Enhanced Security in the Internet...

引用

2024 ieee international conference on Emerging Technologies in Computer Science for Interdisciplinary Applications, ICETCS 2024

作者： Revathi, T. Anbazhagan, Kannagi Kavitha, R. Computing Alliance University Alliance School of Advanced Bangalore India School of CSandIT Bangalore India

ISBN: (纸本)9798350372502

The incorporation of the Internet based Things (IoT) into medical applications has significantly improved healthcare operations and patient treatment. Real-time patient monitoring systems, coupled with remote diagnostics using Internet based Medical Things (IoMT) technology, empower physicians to efficiently handle more cases and potentially save lives. However, IoMT devices are susceptible to cybersecurity threats, posing risks to data security and privacy. Due to constraints in computing power and memory utilization of IoMT devices, implementing traditional security measures becomes impractical. This article introduces a groundbreaking system, ParticleSwarmNetGuard (PS-NG), which combines Element Swarm Optimization with a Deep Neural based Network structure to establish a robust intrusion detection system in IoMT. This innovative system exceeds current data-security standards and achieves an impressive 96% accuracy in detecting network intrusions by utilizing a combined dataset of network traffic and patient sensing data. Furthermore, the performance analysis conducted compares numerous Machine Learning type mechanism (ML) techniques aimed at network intrusion detection in IoMT, confirming the superior performance of DL models over ML models. © 2024 ieee.

关键词： Patient treatment

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Gen AI for Real- Time Traffic Prediction and Autoscaling in Cloud computing Education 4.0 13

Gen AI for Real- Time Traffic Prediction and Autoscaling in ...

引用

13th international conference on System Modeling and Advancement in Research trends, SMART 2024

作者： Vadisetty, Rahul Polamarasetti, Anand Wayne State University Electrical Engineering DetroitMI United States Andhra University Computer Science AP Visakhapatnam India

ISBN: (纸本)9798350380583

The rise of Education 4.0 has brought the growing need for intelligent, adaptive cloud-based learning environments that can manage variable traffic and resource demands efficiently in real-time. This paper will introduce a generative AI -driven model to take advantage of enhancements in real-time traffic prediction and auto-scaling in cloud computing platforms to meet the needs of Education 4.0. This framework picks up a pattern in network traffic and foretells surges in demand by using GAN s so that proactive resource allocation can be done and the system's scalability can be improved. Our proposed approach combines AI-based predictions with an optimized autoscaling strategy, which dynamically adjusts cloud resources according to predicted traffic fluctuations. Experimental tests provide promising results regarding improvement in traffic prediction accuracy, resource usage optimization, and latency reduction toward achieving improved performance and reliable digital education services. The contribution goes to developing an agile, scalable cloud infrastructure that can support personalized and continuous learning according to the adaptive and data-driven goals of Education 4.0. Future work will refine prediction models and explore advanced auto scaling algorithms for further optimization. © 2024 ieee.

关键词： Cloud platforms

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Impact of Demand and Supply in Decision Making for E-Commerce Application

Impact of Demand and Supply in Decision Making for E-Commerc...

引用

2023 international conference on advanced computing and Communication Technologies, ICACCTech 2023

作者： Rajak, Pappu Kumar Tigga, Alka Divya Talukdar, Veera Praveenkumar, S. Namdev, Arpit Gupta, Ankur Doranda College Dept. of Commerce Jharkhand Ranchi India D y Patil International University Dept. of Computer Science MaharashtraAkurdi Pune India Madurai Kamaraj University Centre for Tourism and Hotel Management TN Madurai India University Institute of Technology Rgpv Dept. of It MP Bhopal India Vaish College of Engineering Dept. of Cse Haryana Rohtak India

ISBN: (纸本)9798350380880

In the rapidly evolving realm of e-commerce, the equilibrium between demand and supply is a linchpin for operational success and financial viability. This abstract encapsulates the core challenge of optimizing decision-making processes within e-commerce applications to navigate the intricate landscape of supply and demand dynamics. With consumer preferences in a state of constant flux and market trends evolving at an unprecedented pace, the challenge lies in comprehending and responding adeptly to these fluctuations. The primary objective is to devise dynamic, analytics-driven decision-making frameworks that empower e-commerce platforms to optimize their demand and supply dynamics, simultaneously ensuring customer satisfaction and sustainable business growth. The stakeholders most affected by the efficacy of these strategies include e-commerce managers, supply chain professionals, and consumers, further emphasizing the urgency and relevance of this issue in the contemporary e-commerce landscape. In the current study, an Analysis of Variance (ANOVA) approach has been used to examine the potential influence of employment on high, moderate, and low levels of demand and supply. ANOVA, or analysis of variance, is a statistical method often used in workforce diversity research. Its purpose is to evaluate if there are statistically significant disparities in a certain outcome variable among various employee groups, as determined by their diversity characteristics. © 2023 ieee.

关键词： Decision making

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VE: Modeling Multivariate Time Series Correlation with Variate Embedding

VE: Modeling Multivariate Time Series Correlation with Varia...

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2024 international conference on advanced Mechatronic Systems, ICAMechS 2024

作者： Wang, Shangjiong Man, Zhihong Cao, Zhenwei Zheng, Jinchuan Ge, Zhikang Swinburne University of Technology School of Science Computing and Engineering Technologies VIC3122 Australia Zhejiang University ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China

ISBN: (纸本)9798350366204

Multivariate time series forecasting relies on accurately capturing the correlations among variates. current channel-independent (CI) models and models with a CI final projection layer are unable to capture these dependencies. In this paper, we present the variate embedding (VE) pipeline, which learns a unique and consistent embedding for each variate and combines it with Mixture of Experts (MoE) and Low-Rank Adaptation (LoRA) techniques to enhance forecasting performance while controlling parameter size. The VE pipeline can be integrated into any model with a CI final projection layer to improve multivariate forecasting. The learned VE effectively groups variates with similar temporal patterns and separates those with low correlations. The effectiveness of the VE pipeline is demonstrated through experiments on four widely-used datasets. The code is available at: https://***/swang-song/VE. © 2024 ieee.

关键词： Time series

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