Textbooks an References

Main References

• Maarten Van Steen and Andrew S. Tanenbaum. Distributed Systems, 4th Edition. Maarten Van Steen, 2023.
• Roberto Vitillo. Understanding Distributed Systems: What every developer should know about large distributed applications. Independently published, 2021.

References: Distributed Systems

• Fourre Sigs. Distributed Algorithms: A Verbose Tour. Independently published, 2019.
• George Coulouris, Jean Dollimore and Tim Kindberg. Distributed Systems: Concepts and Design, 5th Edition. Addison-Wesley, 2012.
• Brendan Burns. Designing Distributed Systems: Patterns and Paradigms for Scalable, Reliable Services. CRC Press, 2014.
• Sukumar Ghosh. Distributed Systems: An Algorithmic Approach, 2nd Edition. CRC Press, 2014.
• Ajay D. Kshemkalyani and Mukesh Singhal. Distributed Computing: Principles, Algorithms, and Systems. Cambridge University Press, 2008, 2012(online).

References: Distributed Cloud

• Hong Lin and Weiqi Tian. Distributed Cloud: Reference Architecture Design. Independently published, 2023.
• Theo Lynn, John G. Mooney, Brian Lee and Patricia Takako Endo(Eds.). The Cloud-to-Thing Continuum: Opportunities and Challenges in Cloud, Fog and Edge Computing. Palgrave Macmillan, 2020.
• Theo Lynn, John G. Mooney, Jorg Domaschka and Keith A. Ellis(Eds.). Managing Distributed Cloud Applications and Infrastructure: A Self-Optimising Approach. Palgrave Macmillan, 2020.
• Kai Hwang, Geoffrey C. Fox and Jack J. Dongarra. Distributed and Cloud Computing: From Parallel Processing to the Internet of Things. Morgan Kaufmann, 2012.

References: Edge Computing

• K. Anitha Kumari, G. Sudha Sadasivam and D. Dharani, M. Niranjanamurthy. Edge Computing: Fundamentals, Advances and Applications. CRC Press, 2021.
• Javid Taheri and Shuiguang Deng. Edge Computing: Models, Technologies and Applications. Institution of Engineering and Technology, 2020.
• Taheri, Javid, et al. Edge Intelligence: From Theory to Practice. Springer International Publishing, 2023.
• Wang, Xiaofei, et al. Edge AI: Convergence of Edge Computing and Artificial Intelligence. Springer Nature Singapore, 2020.
• Wang, Dong. And Zhang and Daniel Yue. Social Edge Computing: Empowering Human-Centric Edge Computing, Learning and Intelligence. Springer International Publishing, 2023.

References: Internet of Things (IoT)

• Rajiv Ranjan, Karan Mitra, Prem Prakash Jayaraman,  Albert Y. Zomaya(Eds.). Managing Internet of Things Applications Across Edge and Cloud Data Centres - Computing and Networks. The Institution of Engineering and Technology, 2024.
• Brojo Kishore Mishra and Amit Vishwasrao Salunkhe (Eds). Internet of Things - Technological Advances and New Applications. Apple Academic Press, 2023.
• F. John Dian. Fundamentals of Internet of Things: For Students and Professionals. Wiley-IEEE Press, 2022.
• Ammar Rayes and Samer Salam.. Internet of Things from Hype to Reality: The Road to Digitization, 3rd ed. Springer, 2022.
• Sandeep Saxena and Ashok Kumar Pradhan (Eds.). Internet of Things: Security and Privacy in Cyberspace. Springer, 2022.
• Sachi Nandan Mohanty, Jyotir Moy Chatterjee and Suneeta Satpathy (Eds) Internet of Things and Its Applications. Springer, 2021.
• Farshad Firouzi, Krishnendu Chakrabarty and Sani Nassif (Eds.). Intelligent Internet of Things: From Device to Fog and Cloud. Springer International Publishing, 2020.

References: Spark

• Jules S. Damji, Brooke Wenig, Tathagata Das, and Denny Leey. Learning Spark: Lightning-Fast Data Analytics, 2nd Edition. O'Reilly Media, 2020.
• Mahmoud Parsian. Data Algorithms with Spark: Recipes and Design Patterns for Scaling Up using PySpark. O'Reilly Media, 2022.
• Adi Polak. Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch. O'Reilly Media, 2023.
• Wenqiang Feng. Learning Apache Spark with Python. 2021. (free online and pdf)(https://runawayhorse001.github.io/LearningApacheSpark/)
• Jacek Laskowsk. The Internals of Spark Core. 2024. (online book)(https://books.japila.pl/apache-spark-internals/)
• Cybellium Ltd and Kris Hermans. Mastering Apache Spark: A Comprehensive Guide to Learn Apache Spark. Independently published, 2023.
• Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen and Josh Wills. Advanced Analytics with PySpark: Patterns for Learning from Data at Scale Using Python and Spark. O'Reilly Media, 2022.

References: Python

• Eric Matthes. Python Crash Course: A Hands-On, Project-Based Introduction to Programming, 3rd Edition. No Starch Press, 2023.
• Steve Holden, Anna Ravenscroft and Alex Martelli. Python in a Nutshell: A Desktop Quick Reference, 4th Edition. O'Reilly Media, 2023.
• Johannes Ernesti and Peter Kaiser. Python 3: The Comprehensive Guide to Hands-On Python Programming. Rheinwerk Computing, 2022.
• Brett Slatkin. Effective Python: 135 Specific Ways to Write Better Python, 3rd Edition. Addison-Wesley Professional, 2024.
• Luciano Ramalho. Fluent Python: Clear, Concise, and Effective Programming, 2nd Edition. O'Reilly Media, 2022.
• Wes McKinney. Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, 3rd edition. O'Reilly Media, 2022.
• A Byte of Python(free online book)(https://python.swaroopch.com/)

References: Parallel/Distributed Data Processing with Python

• Fabio Nelli. Parallel and High Performance Programming with Python: Unlock parallel and concurrent programming in Python using multithreading, CUDA, Pytorch and Dask. AVA, 2023.
• Yuli Vasiliev. Python for Data Science: A Hands-On Introduction. No Starch Press, 2022.
• Jake VanderPlas. Python Data Science Handbook: Essential Tools for Working with Data, 2nd Edition. O’Reilly Media, 2023.
• Max Pumperla, Edward Oakes and Richard Liaw. Learning Ray: Flexible Distributed Python for Machine Learning. O’Reilly Media, 2023.
• Tim Peters. Parallel Python with Dask: Perform distributed computing, concurrent programming and manage large dataset. GitforGits, 2023.