Distributed Systems Course Outline
I. Introduction to Distributed Systems
Overview of Distributed Systems
Definition and characteristics of distributed systems
Challenges and benefits of distributed computing
Evolution and trends in distributed systems
Distributed Systems Architecture
Client-server architecture vs. peer-to-peer architecture
Middleware and communication protocols (RPC, REST, messaging)
Scalability, reliability, and fault tolerance
Distributed System Models
Architectural models (layered architecture, microservices)
Process models (client-server, master-slave, peer-to-peer)
Data models (replication, consistency models)
II. Communication in Distributed Systems
Interprocess Communication
Message passing and remote procedure calls (RPC)
Synchronous vs. asynchronous communication
Event-driven architecture and publish-subscribe model
Network Protocols and Middleware
TCP/IP and UDP protocols
HTTP/HTTPS and RESTful APIs
Middleware technologies (CORBA, Java RMI, gRPC)
Distributed Coordination
Distributed transactions (ACID properties)
Two-phase commit protocol (2PC) and three-phase commit protocol (3PC)
Consensus algorithms (Paxos, Raft)
III. Distributed Systems Design Principles
Designing Distributed Applications
Decentralized vs. centralized design
Partitioning and replication strategies
Data consistency and synchronization techniques
Fault Tolerance and Resilience
Failure models and fault detection
Redundancy and replication strategies
Error handling and recovery mechanisms
Security in Distributed Systems
Authentication and authorization
Secure communication protocols (SSL/TLS)
Distributed denial-of-service (DDoS) protection
IV. Distributed Data Management
Distributed File Systems
Characteristics of distributed file systems (NFS, HDFS)
File replication and fault tolerance
Data locality and caching strategies
Distributed Databases
Replication and partitioning techniques
CAP theorem (Consistency, Availability, Partition tolerance)
NoSQL databases and distributed SQL databases (Cassandra, MongoDB, Spanner)
Big Data Processing Frameworks
MapReduce paradigm and Apache Hadoop
Stream processing frameworks (Apache Kafka, Apache Flink)
Scalable data analytics and machine learning in distributed systems
V. Distributed System Middleware and Services
Service-Oriented Architecture (SOA)
Design principles and service composition
RESTful web services vs. SOAP services
Service discovery and orchestration
Containerization and Microservices
Docker containers and Kubernetes orchestration
Benefits of microservices architecture
Challenges and best practices in microservices deployment
Serverless Computing
Function as a Service (FaaS) platforms (AWS Lambda, Azure Functions)
Event-driven architecture and serverless design patterns
Scalability and cost-efficiency considerations
VI. Cloud Computing and Distributed Systems
Cloud Infrastructure
Public, private, and hybrid cloud models
Virtualization and containerization technologies
Cloud-native development and deployment
Edge Computing
Edge computing architecture and use cases
IoT integration and real-time data processing
Challenges and security considerations in edge computing
VII. Case Studies and Real-World Applications
Industry Use Cases
Distributed systems in finance, healthcare, e-commerce
Scalability challenges and solutions
Performance optimization strategies
VIII. Emerging Trends in Distributed Systems
Blockchain and Distributed Ledgers
Distributed consensus mechanisms (Proof of Work, Proof of Stake)
Smart contracts and decentralized applications (DApps)
Applications beyond cryptocurrencies (supply chain, voting systems)
Artificial Intelligence and Machine Learning
AI-driven optimizations in distributed systems
Distributed training of machine learning models
AI-powered analytics and decision-making
IX. Ethical and Legal Considerations
Ethical Issues in Distributed Systems
Privacy and data protection
Fairness and accountability in AI algorithms
Ethical implications of IoT and edge computing
X. Practical Applications and Projects
Hands-On Labs and Exercises
Implementing distributed algorithms (consensus, replication)
Developing distributed applications using middleware technologies
Performance testing and optimization
Capstone Project
Designing and deploying a scalable distributed system
Integration of various distributed technologies and frameworks
Project presentation and evaluation