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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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An Essay on Detailed Performance Assessment Ensemble-Based Predictive Modelling in Network Intrusion Detection Systems

An Essay on Detailed Performance Assessment Ensemble-Based P...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Khajgiwale, Parth International Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350374711

Network Intrusion Detection Systems (NIDS) play a critical role in safeguarding computer networks against malicious activities and cyber threats. To improve the accuracy and robustness of NIDS, this research explores the effectiveness of ensemble-based predictive modelling. Ensembles combine multiple individual machine learning models to make collective predictions, potentially outperforming single models. The study involves designing and implementing various ensemble techniques, such as Random Forest, Gradient Boosting, and Stacking, and comparing their performance against individual models using a well-curated NIDS dataset. The research findings demonstrate that ensembles show promising results in enhancing NIDS accuracy, especially in detecting various types of network intrusions. The analysis sheds light on the strengths and limitations of ensemble approaches and provides valuable insights for deploying machine learning ensembles in real-world NIDS scenarios to bolster network security and protect against emerging cyber threats. © 2024 IEEE.

关键词： Network intrusion

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An ensemble framework of decision trees for class imbalance using partitioning

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International Journal of Innovative Computing and Applications 2024年第1期15卷 1-13页

作者： Kadappa, Vijayakumar Guggari, Shankru Veerabhaskar, Rajeshwari Devi Doddapoojari Department of Computer Applications B.M.S. College of Engineering Bengaluru560019 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India Department of Electronics and Communication Engineering Global Academy of Technology Bengaluru560098 India

Decision tree classifiers are widely used in machine learning and data mining due to their intuitiveness. However, they do not perform well for class-imbalanced data due to bias creation towards the majority class. Therefore, handling class-imbalanced data is an active research area in many applications of machine learning. Feature set partitioning paradigm is proven to be effective for classification by many researchers. In this paper, we extend the ideas of partitioning to propose a decision tree ensemble to overcome class-imbalance issues. The framework consists of balancing the data, partitioning the feature set, building local decision trees, then combining decisions. Some instances of the proposed framework (e.g., Ferrer diagram-based approach) are used to validate the performance of the framework. An empirical study is carried out using University of California Irvine (UCI), Knowledge Extraction Evolutionary Learning (KEEL), and other datasets. The performance of the instances of the proposed framework demonstrates improved performance compared to other benchmark machine learning approaches. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Adversarial machine learning

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Biomarkers to Predictive Modeling: Leveraging Feature Selection and Ensemble Learning for Ovarian Cancer Prognosis 2024

Biomarkers to Predictive Modeling: Leveraging Feature Select...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Srivastava, Jaya Rathi, Megha Department of Computer Science & Engineering Jaypee Institute of Information Technology India

ISBN: (纸本)9798400709722

Treatment outcomes and patient survival rates are greatly improved by early identification of ovarian cancer. However, to increase diagnostic accuracy, effective predictive modeling is required due to the biomarkers' variability and complexity. This research proposes a complex methodology that combines feature selection and ensemble learning approaches to create a forecast model for the diagnosis of ovarian cancer. Recursive feature elimination (RFE), a thorough feature selection technique, was originally used to identify important biomarkers in order to lower dimensionality and improve model interpretability. We then used ensemble learning techniques, which fuse together several machine learning models, to boost the prognosis's stability and predictive performance. Using a dataset containing clinical and genetic data from ovarian cancer patients, ensemble techniques called Adaboost, Catboost, Random Forest, and Gradient boosting were put into practice and validated. Performance metrics like precision, accuracy, F1-score, recall and AUC-ROC curve were used to measure the techniques' efficacy. The findings show that our approach not only performs better than conventional single-predictor models, with results that are roughly 99% correct, but it also offers important insights into the biomarkers which are most symptomatic of the course of ovarian cancer. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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DeepFish: A Comprehensive Exploration of Deep Learning Methods for Accurate Fish Species 8

DeepFish: A Comprehensive Exploration of Deep Learning Metho...

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8th IEEE International Conference on Computing, communication, Control and Automation, ICCUBEA 2024

作者： Bhand, Aarohi More, Priyanka Gawande, Shreyash Deshmane, Swaraj Bharsakale, Pranjal Bhand, Rahi Sakhare, Sachin Thombare, Vipin Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350391770

The DeepFish exploration aims to develop a web-based application that leverages deep learning algorithms to accurately identify and provide detailed information about various fish species based on user-uploaded images, specifically for the unique fish species in Maharashtra. The system utilizes state-of-the-art computer vision techniques to process the uploaded fish images and employs a trained model to predict the fish species. The paper presents a rigorous comparative analysis of deep learning algorithms, to accurately identify the most efficient model for our application with minimum response time and maximum accuracy for identification. The Fish Prediction website provides users with a brief description and relevant information about the predicted fish species including common habitat, diet, physical characteristics, and consumption status, enhancing their knowledge and understanding of the diverse aquatic life. Overall, it provides an accessible and user-friendly platform for fish identification and information retrieval, benefiting researchers, fishing enthusiasts, and conservationists © 2024 IEEE.

关键词： Invertebrates

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Temperature Reliability Analysis of Ge Source-Based Heterogate Tunnel FET 3rd

Temperature Reliability Analysis of Ge Source-Based Heteroga...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Swain, Sukanta Kumar Raj, Abhishek Kumar, Rishu Sharma, Shashi Kant Department of Electronics and Communication Engineering Indian Institute of Information Technology Jharkhand Ranchi834004 India

ISBN: (纸本)9789819784639

This paper reports Ge source-based heterogate TFET on SELBOX substrate. The proposed TFET structure has been simulated based on analysis of DC parameters like transfer characteristic, sub-threshold swing (SS), ION/IOFF ratio, and capacitance over a temperature range from 300 to 500 K for Ge source heterogate TFET. The value of subthreshold swing and ION/IOFF ratio was found to be 30 mV/dec and 1.08 × 1012 that has shown a significant improvement as compared to recent reports. The effect of temperature on DC parameter has also been studied to ensure reliability of the proposed device. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Germanium compounds

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Highly Isolated Hexagonal Shaped DGS based MIMO Antenna for C-Band Applications 14

Highly Isolated Hexagonal Shaped DGS based MIMO Antenna for ...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Kumar, Praveen Sinha, Rashmi Choubey, Arvind Mahto, Santosh Kumar Pal, Pravesh Kumar, Ranjeet Singh, Ajit Kumar National Institution of Technology Department of Electronics & Communication Engineering Jamshedpur India Indian Institute of Information Technology Department of Electronics & Communication Engineering Bhagalpur India Indian Institute of Information Technology Department of Electronics & Communication Engineering Ranchi India

In this article, we present a miniaturized wideband multiple-input multiple-output antenna with high isolation for C band use. It consists of 2×1 hexagonal motifs which serves as radiating elements. Defected ground structure (DGS) technique is employed to attain an efficient isolation between the antenna elements. Two inverted L-shaped stubs are incorporated to enhance the isolation and reduce the antenna size enlargement simultaneously, by suppressing the surface waves. The overall dimension of the designed MIMO antenna is 30 × 20 mm2. The proposed DGS structure provides - 40.81 dB of isolation at 7.88 GHz and a gap of 4 mm between adjacent antenna elements. Envelope correlation coefficient (ECC) is less than 0.01, diversity gain (DG) greater than 9.95 dB, total active reflection coefficient (TARC) less than 0.4, mean effective gain (MEG) less than -3 dB, and channel capacity loss (CCL) less than 0.4 is achieved. Besides, scattering matrix such as return loss S11 and transmission coefficient S21 between two elements, radiation pattern, and current distribution was also favorable for C-band and X-band applications. © Grenze Scientific Society, 2023.

关键词： Defected ground structures

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Deployment of Co-operative Farming Ecosystems Using Blockchain 22nd

Deployment of Co-operative Farming Ecosystems Using Blockcha...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on information Assurance and Security, IAS 2022

作者： Mahapatra, Aishwarya Gupta, Pranav Swarnkar, Latika Gupta, Deeya Kar, Jayaprakash Centre for Cryptography Cyber Security and Digital Forensics Department of Computer Science & Engineering Department of Communication & Computer Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9783031274084

Blockchain has helped us in designing and developing decentralised distributed systems. This, in turn, has proved to be quite beneficial for various industries grappling with problems regarding a centralised system. So, we thought of exploring blockchain’s feasibility in the agricultural industry. India is a country where a large part of the population is still dependent on agriculture, however, there’s no proper system in use as yet that can help the farmers get the right price for their farm products and help the consumers get an affordable price for their ***, we propose a blockchain based decentralized marketplace where we will implement a collaborative model between farmers and consumers. This model will allow the farmers to record their potential crops and the expected output on decentralised ledger besides, enabling them to showcase their integrity and credibility to consumers. The consumers, on the other hand, can actually check everything about the farmers with the help of their information based on the previous supplies. This open and full proof digital market framework will thus reduce black marketing, hoarding, adulteration, etc. In this research paper, we have explored one possible blockchain model by creating a breach proof ledger of records. We have used solidity and ethereum frameworks for the working of the model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

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computers, Materials & Continua 2023年第6期75卷 6305-6322页

作者： Muhammad Imran Khan Khalil Syed Adeel Ali Shah Izaz Ahmad Khan Mohammad Hijji Muhammad Shiraz Qaisar Shaheen Department of Computer Science&Information Technology University of Engineering&TechnologyPeshawar25000Pakistan Department of Computer Science Bacha Khan UniversityCharsadda24541Pakistan Computer Science Department University of TabukTabuk47512Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and Technology Islamabad44000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion *** CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload *** research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains *** this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed *** primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save *** the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming *** results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

关键词： String matching optimization geo-distributed data centers geographical load balancing green energy

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