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A comprehensive examination of load balancing algorithms in cloud environments: a systematic literature review, comparative analysis, taxonomy, open challenges, and future trends

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Iran Journal of computer Science 2024年第3期7卷 663-698页

作者： Prity, Farida Siddiqi Hossain, Md. Maruf Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali 3814 Bangladesh Department of Information and Communication Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh Department of Computer Science and Engineering Shanto-Mariam University of Creative Technology Dhaka 1230 Bangladesh

Cloud computing is a robust paradigm that empowers users and organizations to procure services tailored to their needs. This model encompasses many offerings, including storage solutions, platforms for seamless deployment, and convenient access to web services. Load balancing, a fundamental pillar in cloud computing, is crucial in distributing requests across multiple servers to optimize resource utilization and reduce response times. However, load balancing presents a common challenge in the cloud environment, as it hampers the ability to maintain optimal application performance while adhering to the stringent requirements of Quality of Service (QoS) measurements and Service Level Agreement (SLA) compliance mandated by cloud providers to enterprises. The equitable workload distribution across servers poses a significant challenge for cloud providers. Hence, an efficient load-balancing technique should optimize resource utilization in Virtual Machines (VMs) to ensure maximum user satisfaction and overall system efficiency. However, existing review papers on load balancing in cloud environments often exhibit limitations, lacking in-depth analyses, graphical representations, and comprehensive evaluations of performance metrics. This review paper aims to fill these gaps by providing a novel taxonomy of load balancing algorithms divided into four categories (types of algorithms, nature of problem, metrics, and simulation tools) and thoroughly examining their objectives, parameters, and operational flows. It evaluates the strengths and weaknesses of these algorithms, considering their nature and type, and employs qualitative QoS parameter-based criteria for effectiveness evaluation. The paper also includes a comparative analysis of simulation tools, visual representations, and experimental results. By offering valuable insights, open issues, recommendations, and future directions, this review paper equips researchers, practitioners, and cloud service providers with the k

关键词： Algorithms Cloud computing Load balancing Taxonomy

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Food ingredient recognition model via image and textual feature extraction and hybrid classification strategy

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International Journal of Reasoning-based Intelligent Systems 2024年第1期16卷 74-90页

作者： Madival, Sharanabasappa A. Jawaligi, Shivkumar S. Department of Computer Science and Engineering Faculty of Engineering and Technology Sharnbasva University India Department of Electronic and Communication Engineering Faculty of Engineering and Technology Sharnbasva University Karanataka Kalburgi India

This research work focuses on food recognition, especially, the identification of the ingredients from food images. Here, the developed model includes two stages namely: 1) feature extraction;2) classification. Initially, the image features and textual features will be extracted, where image features like SIFT and improved CNN-based deep features, textural features are extracted. Then, the hybrid classifier is used for the identification of food ingredients that combines the models like neural network (NN) as well as long short-term memory (LSTM). In order to make the accurate results, the weights of NN and LSTM are fine-tuned via the Chebyshev map evaluated teamwork optimisation (CME-TWO) algorithm. At the final stage, the primacy of the offered scheme is proven concerning varied metrics. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Feature extraction

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Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G communication Environment

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Tsinghua Science and technology 2024年第6期29卷 1728-1751页

作者： Ashish Bagwari Jaganathan Logeshwaran M.Raja P.Devisivasankari Jyotshana Bagwari Vikas Rathi Asma Mohammed Elbashir Saad Department of Electronics and Communication Engineering Uttarakhand Technical UniversityDehradun 248007India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore 641202India Department of Computer Science and Engineering CMR Institute of TechnologyKarnataka 560037India Department of Information Science and Engineering CMR Institute of TechnologyKarnataka 560037India Advanced and Innovative Research Laboratory Dehradun 248001India Department of Electronics and Communication Engineering Graphic Era(Deemed to be University)Dehradun 248007India Department of Physics College of Science and HumanitiesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 16273Saudi Arabia

Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.

关键词： hybrid energy storage system 4G digital growth 5G downloading Subscriber Identity Module(SIM) generation network energy efficiency Artificial Intelligence(Al)automation

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Accelerated Curve Fitting Approach for Denoised NQR Signal Parameter Estimation

Electronics

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Electronics 2024年第1期28卷 22-29页

作者： Varma, K. Thulasiram Peesapati, Rangababu Department of Electronics and Communication Engineering National Institute of Technology Meghalaya Shillong793003 India Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kurnool518 008 India

Signal parameters such as amplitude, frequency, decay constant, and phase play an important role in identifying the signal’s origin in many real-life applications. Standard gradientbased curve-fitting approaches are computationally complex to implement on hardware because of large matrix multiplication and partial derivatives. The current work demonstrates a feasible approach to extract parameters from exponential decay Nuclear Quadrupole Resonance (NQR) signals using derivative-free optimization approaches on a PYNQ-Z2 FPGA board. The hardware implementation results indicate 0.12 seconds is required to extract the amplitude, frequency, decay constant, and phase from 5x10-3 sec signals with the residual error in the order of 10-5. © (2023), (University of Banja Luka). All Rights Reserved.

关键词： Nuclear quadrupole resonance

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of Electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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A tensile wearable SHF antenna with efficient communication in defense beacon technology

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Defence technology（防务技术） 2024年第11期41卷 198-210页

作者： Pooja Naresh Bhatt Rashmi Pandhare Department of Electronics and Communication Engineering Indian Institute of Information Technology(IIIT)NagpurMaharashtra 440001India Department of Electrical and Electronics Engineering IPS AcademyInstitute of Engineering and ScienceIndoreMP 440001 India

The study projects a flexible and compact wearable pear-shaped Super High Frequency(SHF)antenna that can provide detailed location recognition and tracking applicable to defense beacon *** mini aperture with electrical dimensions of 0.12λ_(0)×0.22λ_(0)×0.01λ_(0)attains a vast bandwidth over 3.1-34.5 GHz Super High Frequency(SHF)frequency band at S_(11)≤-10 dB,peak gain of 7.14 dBi and proportionately homogeneous radiation *** fractional bandwidth(%BW)acquired is 168%that envelopes diversified frequency spectrum inclusive of X band specifically targeted to all kinds of defense and military *** proposed antenna can be worn on a soldier's uniform and hence the Specific Absorption Rate simulation is *** Peak SAR Value over 1 g of tissue is 1.48 W/kg and for 10 g of tissue is 0.27 W/kg well under the safety *** flexibility is proven by analyzing the full electromagnetic simulations for various bending *** response analysis is attained with its Fidelity Factor and Group *** excellence is determined using Link Budget Analysis and it is seen that margin at 100 Mbps is 62 m and at 200 Mbps is 59 *** is fabricated along with experimental *** the results show harmony in shaping the antenna to provide critical situational awareness and data sharing capabilities required in defense beacon technology for location identification.

关键词： Compact Defense beacon technology Link budget SAR Time domain Wearable

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PLANAR ANTENNA WITH PATTERN RECONFIGURABILITY FOR IOT APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第7期83卷 85-96页

作者： Srinag, A. Aruna, S. Naik, K. Srinivasa Department of Electronics and Communication Engineering Andhra University College of Engineering Andhra Pradesh India Department of Electronics and Communication Engineering Vignan’s Institute of Information Technology Andhra Pradesh Visakhapatnam India

This paper introduces an economical and space-efficient radiation pattern reconfigurable planar antenna system designed for Internet of Things (IoT) applications operating at the 5 GHz frequency range. The proposed system combines a Rotman lens and series-feed array antennas. Featuring a four-input, six-output Rotman lens, the system facilitates the generation of four switchable beams directed at angles of –25°, 0°, +15°, and –15°. The series-feed array ensures minimized side lobe levels and a compact form factor suitable for Wi-Fi frequencies, utilizing a six-port 3 × 3 series-feed planar antenna array design. The Rotman lens and series-feed array prototypes were separately fabricated using a cost-effective FR4 substrate with dimensions of 102 × 102 × 1.6 mm3 and 92 × 92 × 1 mm3, respectively. Both the beam forming Rotman lens and the series-feed microstrip array demonstrate a gain of 10 dBi at 5 GHz. The measured and simulated results exhibit good agreement, emphasizing the antenna’s cost-effectiveness, ease of fabrication, and simplicity, making it wellsuited for IoT applications. The antenna simulation in our research leveraged the sophisticated CST Studio Suite software. This study constitutes a significant stride in comprehending the intricate interplay among diverse antenna components, offering a crucial breakthrough in addressing the complex challenges inherent in designing and manufacturing antennas for IoT applications. Our research unveils cost-effective and efficient solutions tailored for industrial antenna production. The incorporation of a Rotman lens with antenna array techniques not only streamlines design and manufacturing but also substantially cuts costs, utilizing FR4 material. This innovative approach holds tremendous potential for advancing antenna technology in both IoT applications and industrial manufacturing, marking a pivotal step toward more accessible and efficient solutions in the realm of wireless communication. © 2024 by Begell House

关键词： Internet of things

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Adversarial-Learning-Based Taguchi Convolutional Fuzzy Neural Classifier for Images of Lung Cancer

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IEEE Access 2024年 12卷 72766-72776页

作者： Lin, Cheng-Jian Lin, Xue-Qian Jhang, Jyun-Yu National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung41170 Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taichung40401 Taiwan

Deep learning technology has extensive application in the classification and recognition of medical images. However, several challenges persist in such application, such as the need for acquiring large-scale labeled data, configuring network parameters, and handling excessive network parameters. To address these challenges, in this study, we developed an adversarial-learning-based Taguchi convolutional fuzzy neural classifier (AL-TCFNC) for classifying malignant and benign lung tumors displayed in computed tomography images. In the framework of the developed AL-TCFNC, a fuzzy neural classifier replaces a conventional fully connected network, thereby reducing the number of network parameters and the training duration. To reduce experimental cost and training time, the Taguchi method was used. This method helps to identify the optimal combination of model parameters through a small number of experiments. The transfer learning of models across databases often results in subpar performance because of the paucity of labeled samples. To resolve this problem, we used a combination of maximum mean discrepancy and cross-entropy for adversarial learning with the proposed model. Two data sets, namely the SPIE-AAPM Lung CT Challenge data set and LIDC-IDRI Lung Imaging Research data set, were used to validate the AL-TCFNC model. When the AL-TCFNC model was used for transfer learning, it exhibited an accuracy rate of 89.55% and outperformed other deep learning models in terms of classification performance. © 2013 IEEE.

关键词： Fuzzy neural networks

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ML: Generalized Additive Model for Spectrum Sensing in Nakagami-m Fading Channel With Complex Generalized Gaussian Distribution Noise

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IEEE Access 2025年 13卷 84488-84498页

作者： Ahmed, Md. Tofail Haque, Mousumi Sugiura, Yosuke Shimamura, Tetsuya Saitama University Department of Information and Computer Sciences Saitama338-8570 Japan University of Rajshahi Department of Information and Communication Engineering Rajshahi6205 Bangladesh

Spectrum sensing in cognitive radio presents a challenge in overcoming the spectrum scarcity caused by the rapid growth of wireless devices. Deep learning (DL) and machine learning (ML) are trending approaches to enhance the spectrum sensing performance. This paper introduces the generalized additive model (GAM) as an ML-based spectrum sensing method for cognitive radio networks. In our proposed method, the received signals are transformed using fast Fourier transform (FFT) and the powers of both the real and imaginary components of the FFT signals are calculated separately to construct a dataset. In practical communication channels, spectrum sensing is a critical task for cognitive radio in the presence of non-Gaussian noise, such as noise with a heavy-tailed distribution. We consider the complex generalized Gaussian distribution (CGGD) as a noise model to evaluate the detection performance of the ML-based method in both Gaussian and non-Gaussian noise environments. Furthermore, generalized Gaussian distribution (GGD) noise is used to compare the results of the GAM algorithm with existing methods. As a channel model, the Nakagami-m fading distribution is used for its versatile characteristics, which enable it to model a wide range of fading channels by varying the value of a parameter m in its equation. The effectiveness and practical usefulness of the proposed method are validated through simulations. The simulation results exhibit higher performance compared to the state-of-the-art DL and existing conventional methods. © 2013 IEEE.

关键词： Gaussian noise (electronic)

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