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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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The Integration and Implementation of the Healthcare Internet of Things and Its Comprehensive Analysis of Benefits, Challenges, and Future Prospects

The Integration and Implementation of the Healthcare Interne...

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2024 International Conference on Advances in Computing Research on Science Engineering and technology, ACROSET 2024

作者： Goyal, Manish Kumar Kannagi, A. Desai, Karishma Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Atlas SkillTech University Department of UGDX Mumbai India

ISBN: (纸本)9798350388800

Healthcare Internet of Things (HIoT) integration and deployment promise safe, real-time, and networked healthcare solutions, and as such, offer a fundamental paradigm change in modern healthcare. This study made use of a systematic examination of the suggested HIoT approach and a comparison to more conventional methodologies, each of which has its own advantages and disadvantages. Evaluation criteria including accuracy, precision, recall, and F1-Score were used to put a numerical value on the quality of the solutions. The story that the research uncovered is rather fascinating. The suggested HIoT technique beat traditional methods across all assessment criteria. In comparison to more established approaches like 'Blockchain Encryption' (8.4) and 'Data Validation' (7.9), the suggested method achieved a far higher accuracy rating (9.2). The recommended strategy demonstrated its excellence across several categories, receiving ratings of 8.8 for interoperability and 8.9 for real-time monitoring. Data accuracy, which had a score of 9.1, was another advantage of the recommended strategy that showed how helpful it was in avoiding medical errors. The study's findings demonstrate how HIoT may completely transform the way healthcare is provided. Although they are still there, worries about cyber security and data privacy are manageable. Only by working together will lawmakers, regulators, IT companies, and healthcare providers be able to fully reap the benefits of HIoT. With the introduction of tailored treatment plans, data-driven decision making, and real-time monitoring, the future of health information technology seems bright. The study's findings indicate that the recommended strategy has the potential to greatly enhance patient-centeredness, efficacy, and efficiency in healthcare. Healthcare has a bright future with the potential to save lives and enhance the quality of treatment thanks to the Internet of Things. © 2024 IEEE.

关键词： Data accuracy

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A Review on Breast Cancer Classification using Neural Networks 10

A Review on Breast Cancer Classification using Neural Networ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Rajput, Aashita Muskan Yadav, Sachin Chhabra, Megha Agarwal, Arun Prakash School of Engineering and Technology Sharda University Department of Computer Science and Application Greater Noida India

ISBN: (纸本)9789380544472

Breast cancer is the second most common type of cancer due to which females have such a high mortality rate. Several computer-assisted diagnostic systems have recently been proposed to operate with the least amount of data necessary due to the high number of unnecessary breast biopsies. The following review paper discusses the entire process of breast cancer classification along with various kinds of neural networks used to build such models. It further includes the various to-date research done in this domain and the different accuracy obtained by them. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Diseases

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Performance-Based Evaluation for Detection and Classification of Breast Cancer in Mammograms 4th

Performance-Based Evaluation for Detection and Classificat...

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4th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2023

作者： Pati, Dakshya Prasad Panda, Sucheta Department of Computer Application Veer Surendra Sai University of Technology Odisha Burla768018 India

ISBN: (纸本)9789819912025

The breast cancer is considered as one of the deadly disease since last three decades. Among several types of cancer, breast cancer causes death of millions of women in every year. The effective death rate of breast cancer is still in increasing order irrespective of having assistance from highly developed computational tools and techniques. Sometimes, noise or micro-calcification in mammary gland in breast misleads the detection procedure. In this research, the enhanced mammogram images are segmented and the intensity of segmentation is compared using both conventional and optimal threshold techniques to identify the affected breast tissue or dense lesion patch on breast. The performance of both threshold segmentation technique is evaluated by comparing the obtained results using some segmentation score parameters as accuracy, DICE, sensitivity specificity, and Matthews correlation coefficient (MCC). The performance analysis recommends that optimum threshold is better than conventional threshold segmentation method, which can be implemented for computer aided detection (CAD) for detection abnormal patches in low contrast mammogram images toward classification of normal breast to benign or malignant types. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Research on the Network Transmission technology of Integrating the Resource Demand of computer Cloud Computing 2

Research on the Network Transmission Technology of Integrati...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Ou, Wenjisi Zheng, Wei Zhou, Qiang Jiangxi Vocational and Technical College of Information Application Department of Computer Technology Nanchang330043 China

ISBN: (纸本)9798350347456

The establishment of a network environment under the network environment under the network environment of the network environment. First of all, in view of the periodic characteristics of business requirements in the PaaS software system, the business cycle characteristics of the business system and the business cycle characteristics of the business system are integrated based on the business cycle characteristics of the business, and the business model of the business system is formed. The multi-objective optimization method based on multi-objective optimization is used to solve the multi-objective optimization problem. This paper proposes a Paas-oriented master-slave service model, and develops and applies it. Experiments show that if a finite resource is used in the whole system, then the whole system is limited, this method can maximize the utilization of resources in the system. This paper presents a new security threat assessment method based on cloud computing environment, and it has strong recognition. © 2023 IEEE.

关键词： Multiobjective optimization

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Cardiac Image Analysis for Accurate Heart Disease Diagnosis Using Deep Learning Techniques: A Comprehensive Review 1

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： David Samuel Azariya, S. Mohanraj, V. Jothi, G. Jeba Emilyn, J. Akilandeswari, J. Department of Information Technology Sona College of Technology Tamil Nadu Salem India Department of Computer Application Sona College of Arts and Science Tamil Nadu Salem India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

Coronary illness is a leading cause of mortality worldwide. Prompt discovery and precise forecasts of coronary disease can help in forestalling and dealing with the sickness. Lately, profound learning strategies, e.g., convolution neural networks (CNNs) have shown promising outcomes in different clinical imaging errands, including coronary illness expectation. This review proposes a CNN-based approach for coronary illness expectation utilizing cardiovascular pictures. The proposed model comprises numerous convolution layers, pooling, and completely associated layers. The model is prepared on an enormous dataset of cardiovascular pictures and related marks for coronary illness presence or non-attendance. The prepared model is assessed on a different test dataset to evaluate its exactness, responsiveness, explicitness, and other execution measurements. Our outcomes show that the proposed CNN-based approach accomplishes high exactness and outflanks customary machine learning strategies for coronary illness expectation. The proposed model can be utilized as a device to help doctors in the early determination and treatment of coronary illness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electrocardiograms

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Developing Campus Digital Twin with Integrated 3D Point Clouds and 3D Modeling Techniques 6

Developing Campus Digital Twin with Integrated 3D Point Clou...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Utama, Ryan Nathan Sutjiatmadja, Jonathan Osmond, Fabrian Stefan, Lucas Gunawan, Alexander A.S. Chowanda, Andry Irwansyah, Edy Pramudya, Fabian Surya Game Application & Technology Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Exploring digital twin technology through the creation of 3D digital model offers a powerful tool for enhancing real-time monitoring, predictive analysis, and optimization of complex systems. The purpose of this study is to develop a 3D digital model of our building campus as the first step towards developing a digital twin of the university. In this study, we develop a digital model with integrated 3D point clouds and 3D modeling techniques. By scanning the physical model of the campus, data in the form of point clouds is extracted and unnecessary noise is deleted using Autodesk Recap Pro. These point clouds are used as a benchmark to ensure the 3D models created using Blender are as accurate as possible to the physical model. After successfully creating the digital model, several features/interactions are implemented using Unity to mimic the physical model, thus developing the digital twin. The digital twin of the campus will be used to develop a virtual tour of the campus. We concluded that the 3D digital model of the campus was successfully developed, despite the time-consuming nature of the process. We hope that this study serves as a reference to optimize and enhance digital twin development in the future. © 2024 IEEE.

关键词： Digital elevation model

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Multiprocessor Task Scheduling Optimization for Cyber-Physical System Using an Improved Salp Swarm Optimization Algorithm

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SN computer Science 2024年第1期5卷 184页

作者： Acharya, Biswaranjan Panda, Sucheta Ray, Niranjan K. Department of Computer Application Veer Surendra Sai University of Technology Burla Odisha Sambalpur 768018 India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar 751024 India

Salp Swarm Algorithm (SSA) is a bio-inspired optimization algorithm used in this paper to optimize the multiprocessor scheduling process in the current cyber-physical system. Although SSA is mainly utilized in terms of local search, in our case, an improved version has been introduced with the use of a Local Search Algorithm (LSA) and binary SSA, namely Improved SSA (ISSA). More to the point, eight optimization algorithms are compared with this proposed ISSA namely SSA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Jaya Algorithm (JAYA), Chaotic Squirrel Search Algorithm (CSSA), Quantum-inspired Binary Chaotic Salp Swarm Algorithm (QBCSSA) and Space Transformation Search (STS) with SSA is termed as STS-SSA. The performance of ISSA along with the other 6 meta-heuristic and 2 improved versions of SSA algorithms are compared with 12 traditional benchmark functions and evaluated for 100 and 300 dimensions. Convergent curves have also been demonstrated and the proposed ISSA has been shown to find a global optimum within the very initial phase of iterations. For calculating the efficiency of the proposed algorithm, the gear train design problem has been employed. The proposed algorithm has demonstrated higher accuracy rates and better convergent values than the other applied algorithms. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Benchmark function Cyber-physical system Gear train strategy Global optima Grid computing Local search algorithm (LSA) Multiprocessor task scheduling Salp swarm algorithm

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Deep learning for deepfake detection: Scientific landscape and future directions 4th

Deep learning for deepfake detection: Scientific landscape a...

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4th International Conference on Computational Methods in Science and technology, ICCMST 2024

作者： Dhiman, Pummy Kaur, Amandeep Sharma, Dilip Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Engineering and Application GLA University Mathura India

ISBN: (纸本)9781032911571

In today’s digital age, technology has advanced to the point where it is difficult to distinguish be-tween genuine and forged media content. It was intended to be used for entertainment, but it is now being used to deceive people and create biasness, which can lead to disastrous situations. The goal of this bibliometric study is to provide scientific mapping on the role of deep learning in deepfake detection published online. Scopus and VOSviewer are used to create a concept map of 128 articles. Additionally, future directions for research are suggested. © 2025 the Author(s).

关键词： Fake detection

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Internet of Everything: A Review on Smart Healthcare Taxonomy, Research Challenges and Methodologies 14

Internet of Everything: A Review on Smart Healthcare Taxonom...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Raj, Anu Prakash, Shiva Srivastava, Jyoti Madan Mohan Malaviya University of Technolgy Information Technology & Computer Application Department Gorakhpur India

ISBN: (纸本)9798350335095

The Internet of Everything (IoE) has the potential to revolutionize many aspects of our daily lives, including healthcare, business and industrial world etc. Smart healthcare systems that utilize IoE technologies could greatly improve patient outcomes, increase efficiency, and reduce costs. However, there are several challenges that must be overcome in order to develop effective and reliable power supply systems for IoE-enabled healthcare devices. IoE healthcare devices may be deployed in a variety of settings, and the power supply system must be able to monitor and manage these devices remotely. The development of high-performance smart healthcare systems for the IoE requires the development of reliable and scalable power supply systems that can overcome the challenges of distribution, connection, charging technologies, and management. This paper mainly emphasizes on the IoE and their components that are used in the smart healthcare system and rising of the research challenges. In this paper, by addressing the taxonomy of healthcare and research challenges, researchers and businesses can develop innovative solutions that will transform healthcare delivery and improve patient outcomes. © 2023 IEEE.

关键词： Internet of Everything

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