Subject Overview

This subject develops professional competence in monitoring cloud services, engineering observability, controlling expenditure and applying FinOps practices across public, private, hybrid and multi-cloud environments. It combines technical implementation, operational analytics, financial governance, automation and managerial decision-making.

Learning Goal

Students will be able to design cloud monitoring and observability solutions, define service objectives, diagnose performance and reliability issues, analyze and forecast cloud costs, optimize resource consumption, implement FinOps governance and make evidence-based operational and investment decisions.

Syllabus

1. Foundations of Cloud Monitoring and Cost Management

Learning Objective: Explain cloud service and deployment models, shared responsibility, elasticity, consumption-based billing, monitoring, observability, telemetry, optimization and the relationship among reliability, performance, cost and business value.

Category: foundation

2. Cloud Telemetry and Observability Architecture

Learning Objective: Design telemetry pipelines for metrics, logs, traces, events and profiles; apply instrumentation, tagging, sampling, retention and correlation using cloud-native services, OpenTelemetry and observability platforms.

Category: core

3. Metrics, Dashboards and Visualization

Learning Objective: Select infrastructure, application, network, database, container and business metrics; build actionable dashboards; apply aggregation, percentiles, baselines, dimensional analysis and visualization principles.

Category: core

4. Alerting, Event Management and Noise Reduction

Learning Objective: Configure static, dynamic and anomaly-based alerts; define severity, routing, escalation and notification policies; reduce alert fatigue through deduplication, suppression, correlation and actionable runbooks.

Category: core

5. Service Reliability Engineering

Learning Objective: Apply SLIs, SLOs, SLAs, error budgets, availability models, latency objectives, capacity planning and reliability–cost trade-offs to manage cloud services systematically.

Category: core

6. Application Performance Monitoring and Diagnostics

Learning Objective: Use distributed tracing, dependency maps, log analytics, profiling and synthetic and real-user monitoring to identify bottlenecks, root causes, regressions and user-experience issues.

Category: application

7. Monitoring Cloud-Native and Distributed Workloads

Learning Objective: Monitor virtual machines, containers, Kubernetes, serverless functions, managed databases, storage, networks, queues and microservices while addressing ephemeral resources and high-cardinality data.

Category: application

8. Incident Response and Operational Improvement

Learning Objective: Implement incident detection, triage, escalation, remediation, communication and recovery; conduct blameless post-incident reviews and use MTTR, MTTD and related indicators for continual improvement.

Category: application

9. Cloud Economics, Pricing and Billing

Learning Objective: Interpret cloud bills and pricing models, including on-demand, reserved, committed-use, spot and tiered pricing; evaluate total cost of ownership, OpEx, CapEx, licensing, egress and shared-service costs.

Category: foundation

10. Cost Visibility, Allocation and Unit Economics

Learning Objective: Establish tagging and account structures; allocate and amortize costs; implement showback and chargeback; analyze cost per customer, transaction or workload using FinOps Framework and FOCUS-aligned data concepts.

Category: core

11. Cost Analytics, Budgeting and Forecasting

Learning Objective: Analyze expenditure trends and variance, create budgets and forecasts, detect cost anomalies, assess scenarios and communicate financial insights through dashboards and executive reports.

Category: core

12. Cloud Resource and Architecture Optimization

Learning Objective: Optimize compute, storage, databases, networking, Kubernetes and serverless workloads through rightsizing, scheduling, autoscaling, lifecycle policies, architecture changes and commitment planning without compromising service objectives.

Category: application

13. FinOps Operating Model and Governance

Learning Objective: Apply the Inform–Optimize–Operate lifecycle, define engineering, finance, procurement and leadership responsibilities, establish policies, KPIs, accountability, guardrails and maturity assessments for enterprise cloud governance.

Category: strategic

14. Automation, AIOps and Intelligent Optimization

Learning Objective: Automate monitoring, remediation, budget controls and optimization using infrastructure as code, policy as code and APIs; evaluate machine learning, AIOps and generative AI for anomaly detection, forecasting and operational assistance.

Category: contemporary

15. Integrated Strategy, Ethics and Future Practice

Learning Objective: Develop a monitoring and cost-optimization strategy for a realistic hybrid or multi-cloud case; address security, privacy, data retention, vendor lock-in, sustainable cloud use, AI governance and emerging autonomous operations and FinOps trends.

Category: advanced