P
Parminder Singh
Researcher at Lovely Professional University
Publications - 63
Citations - 826
Parminder Singh is an academic researcher from Lovely Professional University. The author has contributed to research in topics: Cloud computing & Computer science. The author has an hindex of 8, co-authored 58 publications receiving 276 citations.
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Blockchain and Fog Based Architecture for Internet of Everything in Smart Cities
TL;DR: A secured architecture Blockchain and Fog-based Architecture Network (BFAN) for IoE applications in the smart cities that secures sensitive data with encryption, authentication, and Blockchain and ensures improved security features through Blockchain technology is presented.
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Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions
TL;DR: A literature survey for auto-scaling techniques of web applications in cloud computing is presented and a taxonomy of reviewed articles with parameters such as auto- scaling techniques, approach, resources, monitoring tool, experiment, workload, and metric is presented.
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Fog computing: A taxonomy, systematic review, current trends and research challenges
TL;DR: This review article aims to classify recently published studies and investigate the current status in the area of fog computing, and proposed taxonomy for fog computing frameworks based on the existing literature and compared the different research work based on taxonomy.
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Secure and energy-efficient smart building architecture with emerging technology IoT
TL;DR: This paper proposes a smart construction architecture that, through IoT, manages the performance of all technological systems and aims to observe how to integrate the DTLS protocol with the Secure Hash Algorithm (SHA-256) using optimizations from the Certificate Authority (CA) to improve security.
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TASM: technocrat ARIMA and SVR model for workload prediction of web applications in cloud
TL;DR: An adaptive prediction model using linear regression, ARIMA, and support vector regression for web applications, and workload classifier has been proposed to select the model as per workload features to improve the quality of service of web applications in a cloud environment.