Cloud Computing And Big Data Technology (second Edition)

This book comprehensively introduces the basic knowledge and main technologies of cloud computing and big data. It covers cloud computing, big data technology, virtualization technology, data centers, parallel computing and clustering technology, cloud storage technology, OpenStack, Hadoop, Spark, Storm, and cloud computing simulation. With a focus on practicality, this volume is enriched with experiments and closely integrates theory with practice, enabling readers to systematically and comprehensively understand cloud computing and big data technologies. This useful reference textbook benefits professionals, academics, researchers, graduate and undergraduate students in databases/information sciences.

An Junxiu, Professor at Chengdu University of Information Technology, is a Senior Visiting Scholar and Master's Supervisor. She serves as the Academic Leader of the Sichuan Provincial Key Laboratory of Software Automatic Generation and Intelligent Service (Domain Ontology and Big Data) and is the Head of the Parallel Computing and Big Data Research Institute. With extensive experience in research and teaching in the fields of data science and big data technology, she has published over 50 papers in related areas and authored nearly 20 monographs or textbooks on cloud computing, big data, and artificial intelligence. Additionally, she is an evaluation expert for the National Natural Science Foundation of China, the Sichuan Science and Technology Project, and the Chengdu Science and Technology Project.

Sian Jin (靳思安) is an Assistant Professor at Temple University's Department of Computer and Information Sciences. He received his PhD in Computer Engineering from Indiana University in 2023. He received his bachelor degree in physics from Beijing Normal University in 2018. His research interest falls in High-performance computing (HPC) data reduction & lossy compression for improving the performance for scientific data analytics & management, as well as for large-scale machine learning & deep learning. Within the past five years, he has published over 20 papers in top conferences and journals, including SC, PPoPP, VLDB, ICDE, EuroSys, HPDC, and ICS.

Wan Lilang , a Master's student at Chengdu University of Information Technology, focuses on the non-stationarity and distribution shifts in time series data. He has contributed to the initial editions of four textbooks published by national-level publishing houses and has published four papers in related fields.