AmrithTech2K6/CSCLM2K6

1 **CLOUD COMPUTING - INTRODUCTION TO CLOUD COMPUTING2 3 4### Part 1: Technologies for Network-Based Systems (30 topics)5 61. Evolution from centralized to distributed systems: A technical timeline72. Client-server vs. peer-to-peer architecture: A comparative study83. Clustered computing: Types (high availability, load balancing, HPC)94. Grid computing fundamentals and its differences from cloud105. Utility computing: Pay-per-use model origins116. Virtualization as the core enabler of network-based systems127. Service-oriented architecture (SOA) in cloud environments138. Web services (REST, SOAP, WSDL, UDDI) for cloud interoperability149. Containerization (Docker) vs. virtual machines: Performance trade-offs1510. Microservices architecture: Decomposing monolithic apps1611. Serverless computing (FaaS): Event-driven network-based systems1712. Software-defined networking (SDN) for cloud data centers1813. Network function virtualization (NFV) in telecom clouds1914. Message queuing systems (RabbitMQ, Kafka) for distributed communication2015. Remote procedure calls (RPC/gRPC) in cloud-native apps2116. Representational state transfer (REST) constraints for scalable APIs2217. Edge computing: Extending network-based systems to the periphery2318. Fog computing: Middle layer between edge and cloud2419. Content delivery networks (CDN) as a distributed system2520. Blockchain as a distributed ledger technology in cloud2621. Distributed databases (Cassandra, CockroachDB) fundamentals2722. In-memory data grids (Redis, Hazelcast) for performance2823. Distributed file systems (Ceph, GlusterFS) basics2924. Consensus algorithms (Paxos, Raft) in cloud systems3025. Leader election and distributed coordination (ZooKeeper)3126. Distributed transactions: Two-phase commit (2PC) vs. Saga pattern3227. CAP theorem and its implications for cloud design3328. Fallacies of distributed computing (L. Peter Deutsch)3429. Case study: Google's Borg and Omega schedulers3530. Case study: Amazon DynamoDB's distributed architecture36 37### Part 2: System Models for Distributed and Cloud Computing (30 topics)38 3931. Physical models vs. architectural models vs. formal models4032. Synchronous vs. asynchronous distributed systems4133. Failure models: Crash, omission, arbitrary (Byzantine) failures4234. Security models: Threats, vulnerabilities, and trust models4335. Scalability models: Vertical vs. horizontal scaling4436. Load balancing models: Static, dynamic, and adaptive algorithms4537. Workload models: CPU-bound, I/O-bound, memory-bound4638. Data placement models in distributed storage4739. Replication models: Active vs. passive replication4840. Consistency models: Strong, eventual, causal, monotonic reads4941. Client-server model: Strengths and limitations5042. Peer-to-peer model: Structured (DHT) vs. unstructured5143. Agent-based system models for autonomous cloud management5244. Mobile code models: Code-on-demand, remote evaluation5345. Cloud bursting model: Hybrid cloud scaling5446. Multi-tenancy model: Isolation and resource sharing5547. Publish-subscribe model for event-driven clouds5648. Master-slave (master-worker) model for parallel processing5749. Leader-follower replication model in databases5850. Sharded (partitioned) system model for horizontal scaling5951. Actor model for concurrent cloud computations (Akka, Orleans)6052. Lambda architecture for big data (batch + speed layers)6153. Kappa architecture: Stream-processing only6254. Control plane vs. data plane separation model6355. Stateful vs. stateless system models in cloud6456. Model checking for distributed system verification6557. Formal specification languages (TLA+, Alloy) for cloud designs6658. Simulation tools (CloudSim, iFogSim) for modeling6759. Performance modeling: Queuing theory for cloud centers6860. Case study: System model of Kubernetes control plane69