Introduction and types of distributed systems – Introduction to P2P systems, Edge Networks, CPS, etc, architecture of DS – overview of processes – A Taxonomy of Distributed Systems, scalable performance, load balancing, and availability. Models of computation – shared memory and message passing system— synchronous and asynchronous systems. Communication in Distributed Systems – Remote Procedure Calls and Message Oriented Communications and implementation, High-level communication and publish-subscribe in Mapreduce

Logical time and event ordering, Global state and snapshot algorithms, clock synchronization, distributed mutual exclusion, leader election, deadlock detection, termination detection, Distributed Databases, implementations over a simple distributed system and case studies of distributed databases and systems – Distributed file systems: Examples from Dropbox, Google FS (GFS)/ Hadoop Distributed FS (HDFS), Bigtable/HBase MapReduce, RDD

Consistency control: Data Centric Consistency, Client Centric Consistency, Replica Management, Fault tolerance and recovery: basic concepts, fault models. Case Studies from Apache Spark, Edge Networks, Cyber-Physical Systems, Google Spanner, Amazon Aurora, BlockChain Systems etc.

Tools: MPI, OpenMP

Pre-Requisite(s): None
Course Type: Lab

Course Objectives

• Understand the design of distributed systems and algorithms that support distributed computing.
• Gain practical exposure to the design and functioning of existing distributed systems and algorithms.

CO-PO Mapping

Evaluation Pattern: 70/30

Note: Continuous assessments can include quizzes, tutorials, lab assessments, case study and project reviews. Midterm and End semester exams can be a theory exam or lab integrated exam for two hours

1. Andrew S. Tannenbaum and Maarten van Steen, Distributed Systems: Principles and Paradigms, Third Edition, Prentice Hall, 2017.
2. Ajay D. Kshemkalyani and Mukesh Singhal, Distributed Computing: Principles, Algorithms, and Systems, Cambridge University Press, 2012.
3. Garg VK. Elements of distributed computing. John Wiley & Sons; 2002.
4. George Coulouris, Jean Dollimore, Tim Kindberg and Gordon Blair, Distributed Systems: Concepts and Design, Fifth Edition, Pearson Education, 2017.
5. Fokkink W. Distributed algorithms: an intuitive approach. Second Edition, MIT Press; 2018.