Cloud Computing

Objectives

Cloud computing has become the dominant delivery model for computing infrastructure, platforms, and services as scalable, on-demand utilities. This course introduces concepts, architectures, virtualization technologies, and distributed systems principles for designing secure, resilient, cost-effective cloud solutions. Students learn foundational cloud theory (service/deployment models), modern infrastructure (data centres, networks, storage), key cloud platforms (AWS, Azure, GCP), and emerging applications (serverless, AI/ML, edge computing).

Contents

Cloud computing fundamentals; motivation (economics, scalability, agility); service models (IaaS, PaaS, SaaS, FaaS) and deployment models (public, private, hybrid, community); virtualization as cloud enabler (hypervisors, Type-1 vs. Type-2, live migration); containerization and orchestration (Docker, Kubernetes); distributed systems principles (CAP theorem, fault tolerance, replication, consistency models); cloud infrastructure and data centers (regions, availability zones, warehouse-scale computing, networking, CDNs); cloud storage (object, block, file; distributed file systems); cloud databases (relational vs. NoSQL); cloud networking (VPCs, load balancing, auto-scaling); identity and security (IAM, encryption, zero-trust, compliance); major cloud platforms comparative analysis (AWS, Azure, GCP services and differentiation); serverless and cloud-native architecture (microservices, event-driven, FaaS); data-intensive computing and AI/ML services (MapReduce, Hadoop, managed ML, generative AI, responsible AI); reliability, SLAs, and disaster recovery (availability targets); cloud economics and FinOps (pricing models, cost optimization); emerging trends (edge computing, agentic AI, quantum, sustainable cloud computing).