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Cloud Workload Placement: Matching Infrastructure to Requirements

7 chapters · 35 lessons

1. 1. Workload Characterization Fundamentals

   5 lessons

   Learn to profile application requirements across compute, memory, I/O, network, and latency dimensions.

2. 2. Cloud Abstraction Layers and Service Models

   5 lessons

   Understand the spectrum from bare metal to managed services and when each abstraction fits.

3. 3. Data Gravity and Network Topology

   5 lessons

   Apply principles of data locality, transfer costs, and network design to minimize latency and expense.

4. 4. Infrastructure for AI and ML Workloads

   5 lessons

   Design infrastructure for training, inference, embeddings, vector search, and RAG pipelines.

5. 5. Compliance, Security, and Governance Constraints

   5 lessons

   Navigate data residency, encryption, identity, and regulatory requirements in infrastructure design.

6. 6. Cost Modeling and Economic Tradeoffs

   5 lessons

   Build total cost of ownership models that account for compute, storage, network, and operational expenses.

7. 7. Real-World Placement Scenarios and Case Studies

   5 lessons

   Apply workload placement reasoning to mobile backends, data pipelines, AI systems, and edge computing.