Overview

Serverless computing has emerged as a transformative technology, gaining prominence over traditional cloud computing. It is characterized by reduced costs, lower latency, and the elimination of server-side management overhead, and is driven by the increasing adoption of containerization and microservices architectures. However, there is a significant lack of comprehensive resources for academic research purposes in this field. Serverless Computing Concepts, Technology, and Architecture addresses this gap and provides a comprehensive exploration of the fundamental concepts, characteristics, challenges, applications, and futuristic approaches of serverless computing. This book serves as a valuable reference for doctorate and post-doctorate research scholars, undergraduates, and postgraduates in fields such as computer science, information technology, electronics engineering, and other related disciplines. Serverless Computing Concepts, Technology, and Architecture is poised to be a one-stop reference point for those seeking to understand and harness the potential of serverless computing. It will serve as a prominent guide for researchers in this field for years to come, enriching their knowledge and advancing the study of serverless computing. Serverless Computing Concepts, Technology, and Architecture delves into the intricate details of this cutting-edge technology, and equips readers with the knowledge to harness its full potential in their research and real-world applications. Its comprehensive coverage includes AWS Lambda, benefits and challenges of containerization, micro service architecture, visual studio code, Function as a Service (FaaS), and much more. This extensive reference book ensures that readers, from doctoral candidates to seasoned professionals, have a reliable source to turn to in the coming years. By addressing the needs of those in pursuit of advanced research, this book stands as an essential cornerstone in the study and advancement of serverless computing.

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Rajanikanth Aluvalu (Senior Member, IEEE) received the Ph.D. degree in cloud computing as specialization. He is currently working as Professor and Head, Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Formerly, he held positions, including Professor and Head, Department of CSE, Vardhaman College of Engineering, Hyderabad, Volunteered IEEE as Vice-Chair of the Entrepreneurship and Startup Committee, Treasurer and Secretary of the IEEE Computer Society, Hyderabad Section. He is having more than 20 years of teaching experience. He organized various international conferences and delivered keynote addresses. He published more than 100 research papers in various peer-reviewed journals and conferences. He is a Life Member of ISTE and a member of ACM and MIR Labs. He was a recipient of the Best Advisor Award from the IEEE Hyderabad Section as well as the IUCEE Faculty Fellow Award (2018). He an Editorial board member of IJDMMM journal published by Inderscience. He guest edited various books with springer, CRC Press, IGI Global and De Gruyter Publishers. He is reviewer for several SCI, Scopus indexed journals. Dr. Uma Maheswari V (Senior Member, IEEE) received the Ph.D. degree in image analytics and data science from Visveswaraya Technological University, Belgaum. She is currently working as an Associate Professor with the Department of CSE, Chaitanya Bharathi Institute of Technology, Hyderabad. She has published more than 50 research papers in SCI, ESCI, WoS, DBLP, and SCOPUS indexed journals and conferences. She has also published four Indian patents on facial expression analysis in the fields of medical, e-commerce, education, and security. She has done an enormous study and given contributions in facial expression analysis and applications. She constructed feature vector for a given image based on the directions and introduced dynamic threshold values while comparing the images, which helps to analyse any image. She has researched the similarity of images in a given database to retrieve the relevant images. She also worked with convolutional neural networks by giving the pre-processed input image to improve the accuracy. It has been proved that the maximum edge intensity values are enough to retrieve the required feature from the image instead of working on total image data. She has organized various technical programs and served as a technical committee member and a reviewer for various conferences. She has delivered sessions in various capacities. She received the Best Faculty Award under the innovation category from the CSI Mumbai Chapter for the year 2019.

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