The Era of Standalone Servers
Dedicated machines running one OS and application stack.
Limitations:
- Expensive hardware.
- Underutilized resources (CPU, memory).
- Manual provisioning and scaling.
Advantages:
- Simplicity: Easy to set up and manage.
- Full Resource Access: No overhead from virtualization.
- Predictable Performance: No "noisy neighbor" issues.
- Strong Isolation: No shared hardware risks (security/stability).
Disadvantages:
- Low Resource Utilization: CPU/RAM often underused.
- Scalability Issues: Vertical scaling (upgrading hardware) is costly.
- No High Availability: Single point of failure.
- Maintenance Downtime: Hardware/OS updates require reboots.
Architecture Diagram:
The Rise of Virtualization Technology
Multiple virtual machines (VMs) run on a single physical server, each with its own OS.
How Virtualization Works:
- Hardware Abstraction: Hypervisor partitions physical resources (CPU, RAM, storage).
- VM Isolation: Each VM operates independently.
- Virtual Hardware Emulation: Presents virtual devices like vCPU and vNIC.
- Dynamic Resource Allocation: Resources can be adjusted without restarting the VM.
Advantages:
- Higher Hardware Utilization (multiple VMs on one server).
- Isolation (one VM crash doesn’t affect others).
- Snapshots & Cloning (for easy backups/testing).
- Live Migration (move VMs between hosts without downtime).
- Cost Savings (less physical hardware needed).
Disadvantages:
- Performance Overhead due to running multiple OS instances. (hypervisor translation).
- Resource contention due to shared usage,
- Complex Management (requires tools like vCenter).
- Licensing Costs (for enterprise tools like VMware).
Types of Hypervisors:
Hypervisors (VMM): (Virtual Machine Monitors) to create and manage virtual machines (VMs)
- Type 1 (Bare-Metal) = Best for production, performance, and scalability.
- Type 2 (Hosted) = Best for flexibility, development, and personal use.
Architecture Diagram:
Embracing Cloud Computing
Virtualized resources delivered as scalable, on-demand services over the internet.
Service Models:
- IaaS – AWS, Azure
- PaaS – Heroku, Google App Engine
- SaaS – Salesforce, Dropbox
- FaaS – AWS Lambda, Azure Functions
How Cloud Computing Works:
- User Request: Client accesses services via the internet.
- Load Balancing: Traffic distributed across servers.
- Virtualization: Dynamic allocation of VMs/containers.
- Storage & Compute: Data stored and processed in distributed systems.
- APIs & Middleware: Services communicate via REST/gRPC APIs.
- Security & Compliance: IAM-based access control and encryption.
Cloud Deployment Models:
Advantages:
- Scalability: Auto-scaling handles traffic spikes.
- Cost Efficiency: Pay-as-you-go pricing (no upfront hardware costs).
- High Availability: Built-in redundancy (multi-region deployments).
- Disaster Recovery: Automated backups & failover.
- Global Reach: CDNs ensure low-latency access worldwide.
Disadvantages:
- Vendor Lock-in: Difficulty migrating between cloud providers.
- Security Concerns: Shared responsibility model (user vs. provider).
- Latency Issues: Distance from data centers can affect performance.
- Cost Management Challenges: Unoptimized resources lead to "bill shock."
Architecture Diagram:
Essential DevOps Tools:
- Docker: Creates containers.
- Kubernetes: Manages and scales those containers.
- Terraform: Sets up the cloud servers where Kubernetes runs.
- Ansible: configures the servers before Kubernetes takes over.
- Jenkins: Automation tool for CI/CD (automate building, testing, and deploying software)
The Age of Containerization
Lightweight, portable units (containers) share the host OS kernel but isolate applications.
Key Components:
- Host Machine (Physical/VM): Runs Docker Engine/containerd.
- Container Engine (Runtime): Manages container lifecycle.
- Container Images: Immutable templates built from Dockerfiles.
- Orchestration Tools: Like Kubernetes or Docker Swarm.
Key Tools in the Ecosystem:
- Docker – Build and run containers
- Kubernetes – Orchestrate at scale
- OpenShift – Enterprise-grade Kubernetes
- Podman – Daemonless alternative to Docker
- AWS ECS/Fargate – Serverless containers
- Service Meshes (e.g., Istio) – Secure communication between microservices
- Serverless Frameworks – Run code without managing servers
Use Cases:
- Microservices Architecture: Decompose apps into small, scalable containers.
- CI/CD Pipelines: Test and deploy apps consistently.
- Cloud-Native Applications: Built for AWS ECS, Google Cloud Run, etc.
- Hybrid Cloud Deployments: Run the same container on-premises and in the cloud.
Containers vs. VMs:
Advantages:
- Lightweight: Less CPU/memory usage than VMs.
- Lower overhead (no guest OS).
- Portable: Runs consistently on laptops, clouds, and servers.
- Fast deployment: Starts in seconds vs. minutes for VMs.
- DevOps-friendly: Works with CI/CD pipelines (GitHub Actions, Jenkins).
Disadvantages:
- Security Risks: Shared kernel risks (breakout attacks).
- OS Dependency: Linux containers can’t run natively on Windows (without a VM).
- Orchestration Complexity: Requires tools like Kubernetes at scale.
Sample Container Architecture:
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