#Feature Overview

The Immutable Infrastructure support provides comprehensive cluster and cloud infrastructure management capabilities designed for enterprise-grade Kubernetes deployments. This platform leverages advanced automation and infrastructure-as-code principles to deliver reliable, scalable, and maintainable infrastructure solutions.

#Cluster Management

Our platform offers end-to-end Kubernetes cluster lifecycle management with immutable OS principles, ensuring consistent and reproducible deployments across environments.

#Cluster Creation

• Immutable OS Support: Create clusters using immutable OS patterns for enhanced security and consistency
• Automated Compute Provisioning: Automatic provisioning of compute instances with pre-configured specifications
• Bootstrap Automation: Automated cluster bootstrapping with minimal manual intervention

#Cluster Deletion

• Complete Resource Cleanup: Comprehensive deletion process that removes all associated resources
• Provider Resource Release: Proper deallocation of provider resources to prevent orphaned instances

#Cluster Scaling

• Horizontal Scaling: Add or remove worker nodes to meet workload demands
• Automated Compute Management: Automatic creation and release of compute instances during scaling operations
• Zero-Downtime Scaling: Scale operations without service interruption

#Cluster Upgrades

• Kubernetes Version Management: Seamless upgrades to newer Kubernetes versions

#Supported Infrastructure Providers

Our platform follows a pluggable provider model aligned with Cluster API infrastructure providers. It is designed for multiple infrastructure platforms. Today, the DCS infrastructure provider is supported, with additional providers in progress.

• Provider-Agnostic Design: Core workflows are consistent across providers
• Current Support: DCS infrastructure provider
• Roadmap: Additional providers are being added

#Compute Resource Management

Advanced virtual machine lifecycle management with enterprise-grade features for optimal resource utilization and performance.

#Compute Lifecycle Operations

• Create Compute Instances: Provision instances with customizable specifications and configurations
• Delete Compute Instances: Secure deletion with proper resource cleanup

#Compute Configuration Options

• Instance/Flavor Selection: Choose from predefined instance types or flavors optimized for different workloads
• Size Customization: Flexible sizing options from small development instances to large production workloads
• Resource Allocation: Precise control over CPU, memory, and storage allocation
• Network Configuration: Advanced networking options including custom subnets and security groups
• Storage Options: Multiple storage types and classes (for example: SSD, HDD, NVMe) for different performance requirements

#Use Cases

#Use Case 1: Highly Available Control Plane

Scenario: Deploy a production cluster with a highly available control plane to ensure cluster stability.

Implementation:

• Deploy a 3-node control plane with automatic failover
• Control plane nodes are distributed across different availability zones (when supported by the infrastructure)
• Load balancer automatically distributes API server traffic
• Automatic recovery of failed control plane components

Benefits:

• No single point of failure in the control plane
• Cluster remains operational even if one control plane node fails
• Automatic recovery reduces manual intervention

#Use Case 2: Horizontal Scaling for Workload Demands

Scenario: Respond to increased application load by adding worker nodes, then scale down when demand decreases.

Implementation:

• Adjust the replicas field in the MachineDeployment resource
• Cluster API automatically provisions new nodes based on the Machine Template
• New nodes automatically join the cluster and become ready for workloads
• When scaling down, nodes are drained and deleted gracefully

Benefits:

• Respond to workload changes in minutes, not hours
• Automated scaling reduces operational overhead

#Use Case 3: Rolling Upgrades with Zero Downtime

Scenario: Upgrade the Kubernetes version or VM template without disrupting running applications.

Implementation:

• Update the Machine Template or Kubernetes version in the control plane/MachineDeployment
• Cluster API performs a rolling upgrade: creates new nodes, waits for them to be ready, then deletes old nodes
• Configurable maxSurge and maxUnavailable parameters control upgrade behavior
• Pods are automatically drained from old nodes and rescheduled on new nodes

Benefits:

• Zero-downtime upgrades for mission-critical applications
• Gradual rollout allows for early problem detection
• Easy rollback if issues are discovered
• No manual node re-provisioning required

#Use Case 4: Multi-Node Pool Management

Scenario: Run different types of workloads on dedicated node pools with different configurations.

Implementation:

• Create multiple MachineDeployments, each with different Machine Templates
• Configure different resource allocations (CPU, memory, storage) per pool
• Use node labels and taints to control workload placement
• Scale each pool independently based on workload requirements

Example Pools:

• General Purpose Pool: Balanced CPU/memory for typical workloads
• Compute-Optimized Pool: High CPU for batch processing or build workloads
• Memory-Optimized Pool: High memory for databases or caching

Benefits:

• Optimize resource allocation for different workload types
• Isolate workloads for security and performance
• Independent scaling per workload type
• Cost optimization through right-sized resources