Lesson 1: Transactions are an important component of many cloud systems today. This module presents building blocks to ensure transactions work as intended, from Remote Procedure Calls (RPCs), to serial equivalence for transactions, to optimistic and pessimistic approaches to concurrency control, to deadlock avoidance/prevention. Lesson 2: This module covers how replication – maintaining copies of the same data at different locations – is used to provide many nines of availability in distributed systems, as well as different techniques for replication and for ensuring transactions commit correctly in spite of replication.

Lesson 1: We study the emerging area of stream processing, touching on key design aspects of Apache Storm. Lesson 2: We study how enormous graphs can be processed in clouds. Lesson 3: We study various types of networks/graphs that are both natural and artificial, and their surprising commonalities. Lesson 4: This module presents classical scheduling algorithms that have been used in operating systems since the inception of computers. We then cover two popular scheduling algorithms for Hadoop.

Lesson 1: When files and directories are stored/accessed over the network, it is called a distributed file system. This module covers the working of distributed file systems like NFS and AFS. Lesson 2: This module covers Distributed Shared Memory systems, their techniques, and pros/cons. Lesson 3: This module looks at the area of sensor networks, starting from what’s inside a sensor mote and how networks of them work.

Lesson 1: This module is a primer on basic security concepts, not just applied to distributed systems, but also more generally. We study various policies and mechanisms, including encryption, authentication, and authorization. Lesson 2: This module presents case studies of real datacenter outages, and attempts to draw lessons on how to prevent them and how to better prepare for them.