A Distributed Operating Systems course for a B.Tech program typically covers the fundamental concepts, architectures, and challenges of distributed systems. Key topics include process synchronization, distributed mutual exclusion, distributed file systems, distributed shared memory, and fault tolerance. Specific textbooks and their syllabi can vary by institution, but the general content remains consistent with the core principles of distributed computing. 

Here's a more detailed breakdown of the typical syllabus and related topics:

Core Concepts and Architectures:

• Introduction to Distributed Systems:
  Definition, goals, and types of distributed systems (e.g., client-server, peer-to-peer, cloud computing). 
• Architectural Models:
  Understanding different architectural patterns like layered, client-server, and peer-to-peer. 
• Inter-process Communication (IPC):
  Mechanisms for processes to communicate and synchronize in a distributed environment (e.g., message passing, remote procedure calls). 
• Clock Synchronization:
  Importance of synchronized clocks in distributed systems and algorithms for achieving it (e.g., Lamport's logical clocks, vector clocks). 
• Distributed Mutual Exclusion:
  Algorithms for ensuring that only one process accesses a shared resource at a time (e.g., token-based algorithms, Ricart-Agrawala algorithm). 
• Distributed Deadlock Detection and Resolution:
  Understanding deadlock situations in distributed systems and techniques for detecting and resolving them. 
• Distributed File Systems:
  Design and implementation of file systems that can be accessed across multiple machines (e.g., NFS, AFS). 
• Distributed Shared Memory:
  Techniques for creating a shared memory abstraction across multiple machines (e.g., page-based DSM, object-based DSM). 
• Fault Tolerance and Replication:
  Strategies for handling failures in distributed systems and ensuring data availability and consistency (e.g., replication, consensus protocols). 
• Load Balancing and Scheduling:
  Algorithms for distributing workload across multiple machines to optimize performance (e.g., static and dynamic load balancing). 
• Security in Distributed Systems:
  Addressing security challenges like authentication, authorization, and data protection in distributed environments.