By the end of the course, students should be able to:

1. Explain the architectural principles of scalable computing infrastructure.

2. Deploy and administer Linux-based client/server systems.

3. Configure network infrastructure and distributed storage services.

4. Build and manage virtualized and containerized environments.

5. Deploy distributed applications using orchestration frameworks.

6. Monitor, automate, and secure multi-node infrastructures.

7. Understand the infrastructure requirements of AI and HPC workloads.

8. Design integrated scalable computing environments for real-world applications.

1. Introduction to Modern Computing Infrastructure: From Single Machines to Planet-Scale Systems

Topics:

• Evolution of computing infrastructure

• Client/server systems

• Distributed systems overview

• Cloud, HPC, and AI infrastructure

• Infrastructure engineering roles

• Linux ecosystem overview

Hands on:

• Linux server setup

• VM provisioning using VirtualBox/KVM

• Multi-node virtual network setup

2. Linux Systems Administration for Infrastructure Engineers: The Operating System as Infrastructure

Topics:

• Linux system architecture

• systemd services

• User and group management

• Package management

• Process management

• Remote administration with SSH

Hands on:

• SSH key-based authentication

• Service management

• Multi-user server configuration

3. Core Network Infrastructure Services: Naming, Addressing, and Time Synchronization

Topics:

• DNS fundamentals

• DHCP services

• NTP/chrony

• Hostname resolution

• Infrastructure dependency chains

Hands on:

• Configure DNS server

• Configure DHCP server

• Configure NTP synchronization across nodes

4. Web and Application Infrastructure: Building the Modern Service Layer

Topics:

• HTTP architecture

• Web servers (NGINX/Apache)

• Reverse proxies

• TLS/SSL certificates

• Application servers

• Load balancing concepts

Hands on:

• Deploy NGINX reverse proxy

• HTTPS configuration

• Host multiple services on one server

5. Distributed Storage and Data Services: Data Beyond a Single Machine

Topics:

• NFS and distributed file systems

• Object storage concepts

• Database servers

• Storage scalability

• Data consistency and replication

Hands on:

• Configure NFS server

• PostgreSQL deployment

• Shared storage across compute nodes

6. Virtualization and Containerization: Abstracting the Machine

Topics:

• Hypervisors and virtualization

• KVM/QEMU concepts

• Containers vs virtual machines

• Docker/Podman

• Container images and registries

Hands on:

• Build container images

• Deploy multi-container applications

• VM and container networking

7. Container Orchestration and Cluster Management: Managing Infrastructure at Scale

Topics:

• Kubernetes architecture

• Pods, deployments, and services

• Cluster scheduling

• Persistent volumes

• Service discovery

Hands on:

• Deploy local Kubernetes cluster

• Deploy scalable web applications

• Kubernetes networking and storage

8. Infrastructure Automation and DevOps: Infrastructure as Code

Topics:

• Infrastructure automation principles

• Configuration management

• Ansible

• CI/CD fundamentals

• Automated deployment pipelines

Hands on:

• Ansible playbooks

• Automated multi-node configuration

• Continuous deployment exercises

9. Monitoring, Logging, and Observability: Seeing Inside Distributed Systems

Topics:

• Monitoring fundamentals

• Metrics and telemetry

• Prometheus and Grafana

• Centralized logging

• Alerting systems

Hands on:

• Build monitoring dashboard

• Configure node exporters

• Log aggregation setup

10. Security and Reliability Engineering: Keeping Systems Secure and Available

Topics:

• Firewalls and access control

• VPN concepts

• Certificates and PKI

• Backup and recovery

• Fault tolerance and redundancy

Hands on:

• Configure firewall policies

• Secure remote access

• Backup and restoration exercises

11. AI and High-Performance Computing Infrastructure: Infrastructure for Intelligent and Parallel Systems

Topics:

• GPU computing overview

• AI training infrastructure

• Distributed deep learning systems

• HPC clusters

• Slurm and job scheduling

• Distributed communication systems

Hands on:

• GPU workload deployment

• Slurm job submission

• Distributed training demonstration

12. Cloud Platforms and Scalable Deployment: Elastic Infrastructure and Cloud-Native Systems

Topics:

• Cloud computing architecture

• Public vs private cloud

• Infrastructure elasticity

• Cloud storage and networking

• Hybrid infrastructure concepts

• Cost-aware infrastructure engineering

Hands on:

• Cloud VM deployment

• Cloud-native application deployment

• Scalable service configuration

The course naturally prepares students for:

• DevOps Engineer

• Site Reliability Engineer

• Cloud Engineer

• Platform Engineer

• Infrastructure Engineer

• Systems Administrator

• HPC Systems Engineer

• MLOps Engineer

• AI Infrastructure Engineer

• Backend Infrastructure Developer