Distributed Systems
Distributed Systems. Instructor: Dr. Rajiv Misra, Department of Computer Science and Engineering, IIT Patna. A distributed system is a software system in which components located on networked computers communicate and coordinate their actions by passing messages. The components interact with each other in order to achieve a common goal. Distributed applications (distributed apps) are applications or software that runs on multiple computers within a network at the same time and can be stored on servers or with cloud computing. This course provides an in-depth understanding of fundamental principles and models underlying the theory, algorithms, and systems aspects of distributed computing. Few Emerging topics such as Peer-to-Peer computing, Distributed Hash Table, Google File System, HDFS, Spark, Sensor Networks and Security in Distributed Systems will also be covered for significant impact. Upon completing this course, students will have intimate knowledge about how things work in a distributed environment.
(from nptel.ac.in)
This lecture covers the following topics: Motivation; Difference between Hadoop MapReduce vs. Apache Spark; Programming Model; Resilient Distributed Datasets (RDDs); RDD Operations; Built-in Libraries; Examples.
Go to the Course Home or watch other lectures:
| Basic Instruction to Distributed Systems |
| Lecture 01 - Instruction to Distributed Systems |
| Lecture 02 - Basic Algorithms in Message Passing System |
| Lecture 03 - Leader Election in Rings |
| Lecture 04 - Models of Distributed Computation, Causality and Logical Time |
| Logical Time, Global State and Snapshot and Distributed Mutual Exclusion |
| Lecture 05 - Size of Vector Clock, Matrix Clocks, Virtual Time and Physical Clock Synchronization |
| Lecture 06 - Global State and Snapshot Recording Algorithms |
| Lecture 07 - Distributed Mutual Exclusion Algorithms and Non-Token Based Approaches |
| Lecture 08 - Quorum Based Distributed Mutual Exclusion Algorithms |
| Distributed Mutual Exclusion, Consensus and Agreement, Checkpointing and Rollback Recovery |
| Lecture 09 - Token Based Distributed Mutual Exclusion Algorithms |
| Lecture 10 - Consensus and Agreement Algorithms |
| Lecture 11 - Checkpointing and Rollback Recovery |
| Deadlock Detection, DSM and Distributed MST |
| Lecture 12 - Deadlock Detection in Distributed Systems |
| Lecture 13 - Distributed Shared Memory |
| Lecture 14 - Distributed Minimum Spanning Tree |
| Termination Detection, Message Ordering and Group Communication, Fault Tolerance |
| Lecture 15 - Termination Detection in Distributed System |
| Lecture 16 - Message Ordering and Group Communication |
| Lecture 17 - Self-Stabilization |
| Case Studies: Distributed Randomized Algorithms, DHT and P2P Computing |
| Lecture 18 - Distributed Randomized Algorithms |
| Lecture 19 - Peer-to-Peer Computing and Structured Overlay Network |
| Lecture 20 - Google File System (GFS) |
| Case Studies: MapReduce, HDFS and Spark, Distributed Algorithms for Sensor Networks |
| Lecture 21 - MapReduce |
| Lecture 22 - The Hadoop Distributed File System (HDFS) |
| Lecture 23 - Spark |
| Lecture 24 - Distributed Algorithms for Sensor Networks |
| Case Studies: Authentication in Distributed Systems, Bitcoin and BlockChain |
| Lecture 25 - Authentication in Distributed Systems |
| Lecture 26 - Bitcoin: A Peer-to-Peer Electronic Cash System |
| Lecture 27 - BlockChain Technology |