Department of Computer Science and Information Systems

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Author: search.filters.author.Shekhawat, Virendra SinghSubject: search.filters.subject.Cloud Computing

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    A Comprehensive Analysis of Cloud Adoption and Cloud Security Issues
    (IEEE, 2024) Shekhawat, Virendra Singh
    Cloud computing has expanded substantially since 2006. By 2011, cloud computing was the top technical goal for organizations worldwide, and industry studies forecast the market would reach ∃441 billion by 2024. Cloud computing has altered IT delivery and management. IT organizations invest in cloud technologies to enhance IT operations and decrease marketing time. The current cloud service model allows enterprises to test new technology and services, such as IoT and Big Data, with little upfront outlay. Most firms have challenges transferring business services and sensitive data to public cloud infrastructures. Over 100 IT executives, managers, and architects were polled about using Public Cloud services. This poll assesses commercial and technology obstacles and cloud storage and sharing concerns. This study examines the Cloud Adoption Landscape in India, including Trends in Offering and Deployment, Adoption Challenges/Roadblocks, and Expectations for Enhanced Adoption. Cloud Computing, Cloud Services, Adoption Challenges, Trends, Data Security, Data Privacy, Hybrid Cloud, Market forecast.
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    Deep Convolutional Neural Network with a Fuzzy (DCNN-F) technique for energy and time optimized scheduling of cloud computing
    (Springer, 2024-07) Shekhawat, Virendra Singh
    Self-adaptive deep learning techniques provide scalability and flexibility in deploying and administrating deep learning models in the cloud environment. DL is widely used in cloud computing architecture, and these methods seek to optimize performance and resource utilization by automatically adjusting the resources allotted to machine learning tasks in response to workload fluctuations. Adaptive task scheduling algorithms maximise the distribution of DL techniques to available resources based on their features and needs. DL algorithms make intelligent judgements regarding job allocation, guaranteeing effective resource utilization and workload management. They consider variables, including task priority, resource availability, and resource capabilities. This research work deploys the Deep Convolutional Neural Network with a Fuzzy (DCNN-F) technique by differentiating the cloud nodes. The complexity of workflow scheduling in the cloud context is optimized by efficient learning, whereas energy and time consumption are effectively handled. The DCNN-F is trained with the resources in the cloud, and the solution for scheduling issues is rectified by learning data. The network is iteratively refined and optimized based on the feedback mechanism in DCNN-F. By combining the power of DCNN-Fs with efficient resource allocation strategies, research can maximise energy and time scheduling precedence-constrained tasks in cloud computing environments. The simulation outcome of DCNN-F is compared with state-of-art techniques, and DCNN-F outperforms Deep Q-Learning (DQL), Deep Reinforcement Learning based Optimization (DRL-O) and Deep Reinforcement Learning based Scheduling (DRL-S) techniques.