B.E Artificial Intelligence and Machine Learning Distributed Computing Syllabus - Mumbai University
This is the Third Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2022-23. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.
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Syllabus for Distributed Computing
Module 1: Introduction to Distributed Systems
- 1.1 Characterization of Distributed Systems: Issues, Goals, and Types of distributed systems, Distributed System Models, Hardware concepts, Software Concept.
- 1.2 Middleware: Models of Middleware, Services offered by middleware, Client Server model.
Module 2: Communication
- 2.1 Layered Protocols, Interprocess communication (IPC): MPI, Remote Procedure Call (RPC), Remote Object Invocation, Remote Method Invocation (RMI)
- 2.2 Message Oriented Communication, Stream Oriented Communication, Group Communication
Module 3: Synchronization
- 3.1 Clock Synchronization, Physical Clock, Logical Clocks, Election Algorithms, Mutual Exclusion, Distributed Mutual Exclusion-Classification of Mutual Exclusion Algorithm, Requirements of Mutual Exclusion Algorithms, Performance measure.
- 3.2 Non Token based Algorithms: Lamport Algorithm, Ricart–Agrawala‘s Algorithm, Maekawa‘s Algorithm
- 3.3 Token Based Algorithms: Suzuki-Kasami‘s Broadcast Algorithms, Singhal‘s Heuristic Algorithm, Raymond‘s Tree.based Algorithm, Comparative Performance Analysis.
Module 4: Resource and Process Management
- 4.1 Desirable Features of global Scheduling algorithm, Task assignment approach, Load balancing approach, load sharing approach
- 4.2 Introduction to process management, process migration, Threads, Virtualization, Clients, Servers, Code Migration
Module 5: Consistency, Replication and Fault Tolerance
- Introduction to replication and consistency, Data-Centric and Client-Centric Consistency Models, Replica Management Fault Tolerance: Introduction, Process resilience, Reliable client-server and group communication, Recovery
Module 6: Distributed File Systems and Name Services
- Introduction and features of DFS, File models, File Accessing models, File-Caching Schemes, File Replication, Case Study: Distributed File Systems (DSF), Network File System (NFS), Andrew File System (AFS), HDFS
Useful Links
- 1 Andrew S. Tanenbaum and Maarten Van Steen, ―Distributed Systems: Principles and Paradigms, 2nd edition, Pearson Education.
- 2 George Coulouris, Jean Dollimore, Tim Kindberg, , "Distributed Systems: Concepts and Design", 4th Edition, Pearson Education, 2005.
- 1 A. S. Tanenbaum and M. V. Steen, "Distributed Systems: Principles and Paradigms", Second Edition, Prentice Hall, 2006.
- 2 M. L. Liu, ―Distributed Computing Principles and Applications‖, Pearson Addison Wesley, 2004.
- 3 Learn to Master Distributed Computing by ScriptDemics, StarEdu Solutions
- 1 https://onlinecourses.nptel.ac.in/noc21_cs87/
- 2 https://nptel.ac.in/courses/106106168 * Suggestion: Laboratory work based on the above syllabus can be incorporated as a mini project in CSM601: Mini-Project.
Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2022-23. Wording is as printed in that syllabus. Module numbering is as printed there too.
The complete syllabus
This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.