Learning Objectives
5 objectives- Understand fundamental concepts, characteristics, and challenges of distributed systems.
- Analyze various communication models, architectures, and computing paradigms in distributed environments.
- Examine design principles of distributed file systems and distributed data management strategies.
- Evaluate fault tolerance, scalability, security, and privacy mechanisms in distributed systems.
- Apply knowledge through case studies of real-world distributed system implementations.
Content Outline
PreviewUnit 920 - Distributed Systems
1. Introduction to Distributed Systems
- Definition and scope of distributed systems
- Key characteristics: concurrency, lack of a global clock, independent failures
- Advantages: resource sharing, scalability, fault tolerance
- Challenges: synchronization, fault handling, consistency, security
2. Communication in Distributed Systems
- Communication models: message passing, remote procedure calls (RPC), remote method invocation (RMI)
- Communication protocols: TCP/IP, UDP, HTTP, gRPC
- Communication architectures: client-server, peer-to-peer
- Serialization and marshaling techniques
3. Distributed System Architectures
- Client-server architecture: roles, interaction patterns
- Peer-to-peer architecture: decentralized control, scalability
- Microservices architecture: modularity, service discovery, orchestration
- Cloud computing architecture: service models (IaaS, PaaS, SaaS), deployment models
4. Distributed Computing Paradigms
- Parallel computing: concepts, shared vs distributed memory
- Cluster computing: tightly coupled nodes, high availability
- Grid computing: resource sharing across administrative domains
- Cloud computing: on-demand resource provisioning, elasticity
- Applications, scalability, and fault tolerance considerations in each paradigm
5. Distributed File Systems
- Design goals: transparency, fault tolerance, scalability
- Hadoop Distributed File System (HDFS): architecture, data blocks, replication
- Network File System (NFS): protocol overview, stateless vs stateful design
- Ceph: object storage, CRUSH algorithm, scalability and fault tolerance
- Data replication, consistency models, access protocols
6. Distributed Data Management
- Distributed database systems: architecture, query processing, transaction management
- NoSQL databases: types (key-value, document, column-family, graph), use cases
- Data replication strategies: synchronous vs asynchronous replication
- Consistency models: strong, eventual, causal consistency
- CAP theorem and trade-offs
7. Fault Tolerance and Replication
- Fault types: crash, omission, timing, Byzantine
- Fault tolerance mechanisms: redundancy, checkpointing, rollback recovery
- Replication strategies: primary-backup, multi-primary
- Consistency models in replication
- Recovery techniques and consensus algorithms (e.g., Paxos, Raft)
8. Scalability and Load Balancing
- Scalability challenges: bottlenecks, resource contention
- Load balancing algorithms: round-robin, least connections, weighted distribution
- Horizontal scaling vs vertical scaling
- Auto-scaling and elasticity in cloud systems
- Performance metrics and monitoring
9. Security and Privacy in Distributed Systems
- Security threats: eavesdropping, spoofing, denial of service, insider threats
- Authentication mechanisms: passwords, certificates, multi-factor authentication
- Encryption techniques: symmetric, asymmetric, TLS/SSL
- Access control models: discretionary, mandatory, role-based access control
- Privacy concerns and data protection regulations
10. Case Studies in Distributed Systems
- Google's Bigtable: architecture, data model, scalability
- Amazon DynamoDB: design principles, eventual consistency, partitioning
- Apache Hadoop: ecosystem overview, MapReduce, HDFS
- Microsoft Azure: cloud services architecture, scalability, security features
- Performance characteristics and lessons learned
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