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Recent developments of load balancing in cloud computing: A review

AIP Conference Proceedings 2025年第1期3233卷

作者： Srishti Agarwal Jitendra Singh M. Izharul Hasan Ansari Department of Computer Science & Engineering SRM Institute of Science &Technology NCR Campus Ghaziabad India Professor Department of Computer Science & Engineering Meerut Institute of Technology Meerut India

Large-scale distributed and parallel computing is denoted as cloud computing. Based on the user needs, it deals with shared resources, storage, and information. A lot of people are becoming interested in cloud computing; thus it is growing quickly due to its user-satisfying services. There are several open research issues and load balancing is one of the challenging task in cloud computing. It has been observed that load-improved cloud system balancing algorithms surge performance through better resource utilization by dispersing the workload among the system’s many nodes in a dynamic manner. This review article examines virtualization, load balancing, problems, cloud computing architectures, and several load balancing techniques that are currently in use.

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A Novel Approach for User Demand-aware Data Center Construction and Service Consolidation 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Lv, Yifei Guo, Kunyin Xia, Yunni Li, Yin Ma, Yong Li, Fan Zeng, Linchengxi Peng, Qinglan Long, Tingyan College of Computer Science Chongqing University Chongqing400030 China Guangzhou Institute of Software Application Technology Guangzhou510000 China School of Computers and Information Jiangxi Normal University Nanchang330022 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610000 China Chongqing China School of Artificial Intelligence Henan University Kaifeng475001 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers (DCs) to handle business, monetary, Internet and commercial applications of different enterprises. A cloud data center encompasses thousands of physical server nodes arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms. More and more cloud data centers are turning for adapting to dynamics of user demands and reducing operational cost. Therefore, in this paper, we propose a user demand-aware (UDA) method for servers selection and a modified adaptive large neighbourhood search (MALNS) algorithm for dynamic service consolidation. Experiments based on real-world datasets demonstrate our approach outperformed conventional strategies in terms of multiple metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Heuristic algorithms

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Analysis and visualization of proxy caching using LRU, AVL tree and BST with supervised machine learning

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Materials Today: Proceedings 2022年 51卷 750-755页

作者： Jitendra Singh Kushwah Deepak Gupta Anurag Shrivastava P. Ambily Pramitha John T. Abraham Munindra Lunagaria Department of Computer Science and Engineering Institute of Technology and Management Gwalior Madhya Pradesh India Principal and Professor ECE Lakshmi Narain College of Technology and Science Indore Madhya Pradesh India Bharathiyar University Coimbatore Tamil Nadu India Bharata Mata College Thrikkakara Kochi Kerala India Computer Engineering Department Marwadi University Rajkot Gujarat India

Proxy cache access speeds and decreases load time. There is an uncertainty regarding cache level 2 which you neglect. This research shall investigate the L1 cache, primary cache, and L2 cache as a secondary proxy server cache. LRU is typically utilised instead of cache. LRU for cold cache removal used to be a time-consuming process, but it isn't particularly efficient today. The performance of the cache L1 used LRU and L2 used LRU_AVL has risen with these solutions. The output is the LRU_AVL among other ways that utilize LRU tables and graphs. proxy cache LRU, LRU_AVL, and LRU_BST have average time of access calculated. Median access time is estimated using Python tools including Pandas, MatPlotLib as well as LRU, LRU_AVL, and LRU_BST. This research will anticipate the average period of usage of LRU, LRU_AVL, and LRU_BST cache algorithms.

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Performance evaluation for ranking-based discretisation 24

Performance evaluation for ranking-based discretisation

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Baron, Grzegorz Stanczyk, Urszula Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Discretisation often constitutes a part of initial data preparation stage. It translates continuous domain of features into granular, by assigning a number of intervals to represent attributes' values by nominal categories. Typically all real-valued features are subjected to transformations, regardless of their characteristics. The paper presents research on discretisation executed with a discerning approach. To all available attributes, feature selection mechanisms were employed, in the form of rankings that order variables based on their importance. Exploiting this discovered knowledge on attributes, discretisation was then driven by a ranking, and either highest or lowest ranking features were selected for transformation. The influence of selective discretisation on the performance of classification systems was studied for three popular inducers. The procedure was employed in the field of stylometry, and a task of authorship recognition, considered as a binary classification with balanced classes. The experiments show that discretisation based on importance of features can lead to better performance than in the case of transformations applied to all attributes. © 2020 The Authors. Published by Elsevier B.V.

关键词： Feature Selection

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Attention Enabled Ensemble Deep Learning Models and its Validation for Depression Detection: A Domain Adoption Paradigm

SSRN

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SSRN 2023年

作者： Singh, Jaskaran Singh, Narpinder Saba, Luca Suri, Jasjit S. Dept of Computer Science Graphics Era Deemed to be University Uttarakhand Dehradun India Department of Food Science and Technology Graphic Era Deemed to be University Uttarakhand Dehradun India Department of Neurology University of Cagliari Cagliari Italy Stroke Monitoring and Diagnostic Division AtheroPoint™ RosevilleCA United States

Background and Motivation: Depression is increasingly prevalent leading to higher suicide risk. Depression detection and sentimental analysis in text inputs in cross-domain frameworks is challenging. Just solo deep learning (SDL) and ensemble deep learning (EDL) models are not robust enough. Recently attention mechanisms were introduced in SDL. We hypothesize that attention-enabled EDL (aeEDL) architectures would be superior compared to attention-not-enabled SDL (aneSDL) or aeSDL ***: We designed EDL-based architectures with attention blocks to build eleven kinds of SDL and five kinds of EDL models on four domain-specific datasets. We scientifically validate our models by comparing 'seen' and 'unseen' paradigms (SUP). We benchmark against SemEval (2016) sentimental dataset and establish reliability ***: The mean increase in accuracy for EDL over their corresponding SDL components was 4.49%. For studying the effect of attention block, the increase in mean accuracy (AUC) of aeSDL over aneSDL was 2.58% (1.73%), and increase in mean accuracy (AUC) of aeEDL over aneEDL was 2.76% (2.80%). When comparing EDL vs. SDL for non-attention and attention, the mean aneEDL was greater than aneSDL by 4.82% (3.71%) and mean aeEDL was greater than aeSDL by 5.06% (4.81%). On benchmarking dataset (SemEval), the best-performing aeEDL model (ALBERT+BERT-BiLSTM) was superior to the best aeSDL (BERT-BiLSTM) model by 3.86%. Our scientific validation and robust design showed a difference of only 2.7% in SUP, thereby meeting regulatory ***: We validated all our hypotheses and further demonstrated that aeEDL is a very effective and generalized method for detecting symptoms of depression in cross-domain settings. © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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Nuwa: An Authoring Tool for Graph Visualizations

Nuwa: An Authoring Tool for Graph Visualizations

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Dongming Han Haiyang Zhu Wei Chen Jiacheng Pan Ran Chen Xumeng Wang Zhen Wen Luoxuan Weng Minfeng Zhu Yingcai Wu Rüdiger Westermann State Key Lab of CAD&CG Zhejiang University China Hithink RoyalFlush Information Network Co. Ltd. China Wuchan Zhongda Digital Technology Co. Ltd. China Laboratory of Art and Archaeology Image (Zhejiang University) Ministry of Education China College of Computer Science Nankai University China Zhejiang University China Computer Graphics and Visualization Technical University of Munich Germany

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

Authoring graph visualization requires advanced programming skills, expert domain knowledge, and significant workload. Existing authoring tools either support limited templates of graph visualization, or suffer from a high learning cost. We analyze the design requirements on a tool for graph visualizations, and contribute Nuwa, a user-friendly declarative authoring tool for the interactive specification of graph visualizations in terms of data, entity, change, and encoding. Our implementation empowers users to conveniently create, compare, and modulate comprehensive graph visualizations with a wide range of styles. We showcase various examples to verify the expressiveness of Nuwa. Via an expert interview and the analysis on cognitive dimensions we evaluate the usability of Nuwa.

关键词： Analytical models Authoring systems Costs Semantics Data visualization Prototypes Programming

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Periocular Biometrics: A Modality for Unconstrained Scenarios

arXiv

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arXiv 2022年

作者： Alonso-Fernandez, Fernando Bigun, Josef Fierrez, Julian Damer, Naser Proencą, Hugo Ross, Arun School of Information Technology Halmstad University Sweden School of Engineering Universidad Autonoma de Madrid Spain Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Instituto de Telecomunicações University of Beira Interior Portugal Department of Computer Science and Engineering Michigan State University United States

Periocular refers to the externally visible region of the face that surrounds the eye socket. This feature-rich area can provide accurate identification in unconstrained or uncooperative scenarios, where the iris or face modalities may not offer sufficient biometric cues due to factors such as partial occlusion or high subject-to-camera distance. The COVID-19 pandemic has further highlighted its importance, as the ocular region remained the only visible facial area even in controlled settings due to the widespread use of masks. This paper discusses the state of the art in periocular biometrics, presenting an overall framework encompassing its most significant research aspects, which include: (a) ocular definition, acquisition, and detection;(b) identity recognition, including combination with other modalities and use of various spectra;and (c) ocular soft-biometric analysis. Finally, we conclude by addressing current challenges and proposing future directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Biometrics

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Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition

arXiv

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arXiv 2022年

作者： Huber, Marco Terhörst, Philipp Kirchbuchner, Florian Damer, Naser Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Technical University of Darmstadt Darmstadt Germany Norwegian University of Science and Technology Gjøvik Norway Paderborn University Paderborn Germany

Face Recognition (FR) is increasingly used in critical verification decisions and thus, there is a need for assessing the trustworthiness of such decisions. The confidence of a decision is often based on the overall performance of the model or on the image quality. We propose to propagate model uncertainties to scores and decisions in an effort to increase the transparency of verification decisions. This work presents two contributions. First, we propose an approach to estimate the uncertainty of face comparison scores. Second, we introduce a confidence measure of the system's decision to provide insights into the verification decision. The suitability of the comparison scores uncertainties and the verification decision confidences have been experimentally proven on three face recognition models on two datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Face recognition

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

SynFacePAD 2023: Competition on Face Presentation Attack Det...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Marco Huber Julian Fierrez Raghavendra Ramachandra Naser Damer Alhasan Alkhaddour Maksim Kasantcev Vasiliy Pryadchenko Ziyuan Yang Huijie Huangfu Yingyu Chen Yi Zhang Yuchen Pan Junjun Jiang Xianming Liu Xianyun Sun Caiyong Wang Xingyu Liu Zhaohua Chang Guangzhe Zhao Juan Tapia Lazaro Gonzalez-Soler Carlos Aravena Daniel Schulz Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norwegian University of Science and Technology (NTNU) Norway ID R&D Inc New York US School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

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Responsible AI on National Cybersecurity Strategy with Enhancement of Cybersecurity in Education

Responsible AI on National Cybersecurity Strategy with Enhan...

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Intelligent & Innovative Practices in Engineering & Management (IIPEM), International Conference on

作者： Bhagyashree Ashok Tingare G. Prasanna Lakshmi Vijayshri Khedkar Rakesh Thoppaen Suresh Babu Tarun Dhar Diwan Pravin B Khatkale Department of Artificial Intelligence and Data Science D Y Patil College of Engineering Akurdi Pune School of Computer Science and Engineering Sandip University Nashik Maharashtra Department of Computer Science & Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Senior Technical Architect HEXAWARE TECHNOLOGIES INC New Jersey Assistant Professor and Controller of Examination (COE) Atal Bihari Vajpayee University Bilaspur India Sanjivani University Kopargaon Maharashtra India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

For national cybersecurity to keep up with the digital transition, AI is more important than ever. Responsible AI improves cybersecurity and education by emphasizing openness and ethics. Ethical design is stressed from the outset in responsible AI. This article analyzes how artificial intelligence (AI) influences the national cybersecurity strategy, focusing on cybersecurity education development and effectiveness. The National Cybersecurity Strategy will frame this article. The major results show that responsible AI improves threat detection and response, cybersecurity infrastructure, talent shortage filling, collaborative research, and public trust. Educational institutions may now offer innovative training programs thanks to artificial intelligence. Completing this aim requires closing the gap between conventional methodologies and modern challenges. Closing gaps is vital. Functional artificial intelligence improves national cybersecurity and educational quality. This analysis shows that this conclusion is possible. This allows experts to properly react to shifting dangers. This is why it matters.

关键词： Training Ethics Focusing Collaboration Threat assessment Filling Artificial intelligence computer security

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