Overview
ABOUT THE COURSE: This course we will cover fundamentals of Edge computing and its applications in low latency and critical real-time computing scenarios. The course brings in theory of Edge computing, focusing on it as a complementary approach that addresses some of the limitations of cloud computing. The course will cover applications where edge computing is a necessity, such as real-time applications that require low latency and high bandwidth. For example, autonomous vehicles require real-time processing of data from sensors, which cannot be done in a centralized data center due to latency issues. This course covers various innovations of waves from cloud computing to edge computing. This course provides an in-depth understanding of edge computing principles with different use case of edge computing. In this course we will explore different frameworks for computing over edge devices and cloud. We will cover different techniques for distributed data analytics over edge devices like edge data center. Different edge computing fundamentals will be covered, such as RTT, Docker containers, Kubernetes, MQTT, Kafka, time and clock synchronization and key-value stores at edge. We will also cover various cloud platforms that provide edge services.This course also covers recent advances of machine learning, deep learning and artificial intelligence with appropriate use cases in edge computing such as predictive maintenance, self driving cars and deep reinforcement learning for edge applications.INTENDED AUDIENCE: CSE, ECE, EEPREREQUISITES: NetworkingINDUSTRY SUPPORT: IT industries
Syllabus
1. “Fog and Edge Computing: Principles and Paradigms”, Rajkumar Buyya (Editor), Satish Narayana Srirama (Editor), Wiley, 2019
2. Cloud Computing: Principles and Paradigms”, Editors: Rajkumar Buyya, James Broberg,Andrzej M. Goscinski, Wiley, 2011
3. “Cloud and Distributed Computing: Algorithms and Systems”, Rajiv Misra, Yashwant Patel, Wiley 2020.
Beside these books, we will provide Journal papers as references.