Distributed Computing detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the AI&ML students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Artificial Intelligence & Machine Learning 5th Sem scheme and its subjects, do visit AI&ML 5th Sem 2021 regulation scheme. The detailed syllabus of distributed computing is as follows.
Course Objectives:
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Unit I
INTRODUCTION
Introduction: Definition-Relation to Computer System Components – Motivation – Message -Passing Systems versus Shared Memory Systems – Primitives for Distributed Communication -Synchronous versus Asynchronous Executions – Design Issues and Challenges; A Model of Distributed Computations: A Distributed Program – A Model of Distributed Executions – Models of Communication Networks – Global State of a Distributed System.
Unit II
LOGICAL TIME AND GLOBAL STATE
Logical Time: Physical Clock Synchronization: NTP – A Framework for a System of Logical Clocks – Scalar Time – Vector Time; Message Ordering and Group Communication: Message Ordering Paradigms – Asynchronous Execution with Synchronous Communication – Synchronous Program Order on Asynchronous System – Group Communication – Causal Order – Total Order; Global State and Snapshot Recording Algorithms: Introduction – System Model and Definitions -Snapshot Algorithms for FIFO Channels.
Unit III
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Unit IV
CONSENSUS AND RECOVERY
Consensus and Agreement Algorithms: Problem Definition – Overview of Results – Agreement in a Failure-Free System(Synchronous and Asynchronous) – Agreement in Synchronous Systems with Failures; Checkpointing and Rollback Recovery: Introduction – Background and Definitions -Issues in Failure Recovery – Checkpoint-based Recovery – Coordinated Checkpointing Algorithm — Algorithm for Asynchronous Checkpointing and Recovery
Unit V
CLOUD COMPUTING
Definition of Cloud Computing – Characteristics of Cloud – Cloud Deployment Models – Cloud Service Models – Driving Factors and Challenges of Cloud – Virtualization – Load Balancing -Scalability and Elasticity – Replication – Monitoring – Cloud Services and Platforms: Compute Services – Storage Services – Application Services
Course Outcomes:
Upon the completion of this course, the student will be able to
- Explain the foundations of distributed systems (K2)
- Solve synchronization and state consistency problems (K3)
- Use resource sharing techniques in distributed systems (K3)
- Apply working model of consensus and reliability of distributed systems (K3)
- Explain the fundamentals of cloud computing (K2)
Text Books:
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Reference Books:
- George Coulouris, Jean Dollimore, Time Kindberg, “Distributed Systems Concepts and Design”, Fifth Edition, Pearson Education, 2012.
- Pradeep L Sinha, “Distributed Operating Systems: Concepts and Design”, Prentice Hall of India, 2007.
- Tanenbaum A S, Van Steen M, “Distributed Systems: Principles and Paradigms”, Pearson Education, 2007.
- Liu M L, “Distributed Computing: Principles and Applications”, Pearson Education, 2004.
- Nancy A Lynch, “Distributed Algorithms”, Morgan Kaufman Publishers, 2003.
- Arshdeep Bagga, Vijay Madisetti, “ Cloud Computing: A Hands-On Approach”, Universities Press, 2014.
For detailed syllabus of all other subjects of Artificial Intelligence & Machine Learning, 2021 regulation curriculum do visit AI&ML 5th Sem subject syllabuses for 2021 regulation.
For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results direct link.