Problem Statement

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Container Orchestration System - Problem Statement

Overview

Design a container orchestration platform like Kubernetes that manages containerized applications across thousands of nodes with auto-scaling, service discovery, load balancing, self-healing, and zero-downtime deployments.

Functional Requirements

Container Management

  • Container Lifecycle: Create, start, stop, restart, delete containers
  • Image Management: Pull images from registries, cache locally
  • Resource Allocation: CPU, memory, disk, network limits per container
  • Health Checks: Liveness and readiness probes
  • Restart Policies: Always, on-failure, never
  • Container Logs: Collect and aggregate logs from all containers

Cluster Management

  • Node Management: Add/remove nodes dynamically
  • Node Health: Monitor node health and capacity
  • Node Labeling: Label nodes for workload placement
  • Node Taints: Prevent certain workloads on nodes
  • Cluster Autoscaling: Add/remove nodes based on demand

Workload Scheduling

  • Pod Scheduling: Place containers on appropriate nodes
  • Resource Requests: Guarantee minimum resources
  • Resource Limits: Enforce maximum resources
  • Affinity Rules: Co-locate or separate workloads
  • Priority Classes: Prioritize critical workloads
  • Preemption: Evict low-priority pods for high-priority

Service Discovery and Networking

  • Service Discovery: DNS-based service discovery
  • Load Balancing: Distribute traffic across pods
  • Network Policies: Control traffic between pods
  • Ingress: External access to services
  • Service Mesh: Advanced traffic management

Storage Management

  • Persistent Volumes: Attach storage to containers
  • Volume Types: Local, NFS, cloud storage (EBS, GCS)
  • Dynamic Provisioning: Auto-create volumes on demand
  • Volume Snapshots: Backup and restore volumes
  • Storage Classes: Different performance tiers

Configuration and Secrets

  • ConfigMaps: Store configuration data
  • Secrets: Store sensitive data (passwords, keys)
  • Environment Variables: Inject config into containers
  • Volume Mounts: Mount config as files

Auto-Scaling

  • Horizontal Pod Autoscaling: Scale pods based on metrics
  • Vertical Pod Autoscaling: Adjust resource requests
  • Cluster Autoscaling: Add/remove nodes
  • Custom Metrics: Scale on application-specific metrics

Rolling Updates and Rollbacks

  • Rolling Updates: Update without downtime
  • Blue-Green Deployments: Switch between versions
  • Canary Deployments: Gradual rollout to subset
  • Rollback: Revert to previous version

Non-Functional Requirements

Performance

  • Scheduling Latency: <1 second to schedule pod
  • API Response Time: <100ms for API calls
  • Service Discovery: <10ms to resolve service
  • Health Check: <5 seconds to detect failure
  • Scaling Time: <30 seconds to scale up/down

Scalability

  • Cluster Size: Support 5,000 nodes per cluster
  • Pod Count: 150,000 pods per cluster
  • Container Count: 300,000 containers per cluster
  • API Throughput: 10,000 API requests/second
  • Multi-Cluster: Manage 100+ clusters

Reliability

  • Control Plane Uptime: 99.99% availability
  • Data Plane Uptime: 99.95% availability
  • Self-Healing: Auto-restart failed containers
  • Disaster Recovery: <5 minutes RTO, <1 minute RPO
  • Zero-Downtime Updates: Rolling updates without service interruption

Consistency

  • Desired State: Eventually consistent with actual state
  • API Consistency: Strong consistency for critical operations
  • Service Discovery: Eventually consistent (5-second lag)
  • Configuration: Strongly consistent

Key Challenges

1. Distributed State Management

  • Maintain cluster state across multiple control plane nodes
  • Ensure consistency using etcd (Raft consensus)
  • Handle network partitions
  • Recover from failures

2. Efficient Scheduling

  • Place 150K pods on 5K nodes optimally
  • Consider resource constraints, affinity rules
  • Schedule in <1 second
  • Handle node failures and rescheduling

3. Service Discovery at Scale

  • Resolve 100K service lookups/second
  • Update DNS records in real-time
  • Handle pod IP changes
  • Maintain consistency

4. Network Performance

  • Route traffic between 300K containers
  • Implement network policies
  • Minimize latency overhead
  • Scale to 10 Gbps per node

Success Metrics

Operational Metrics

  • Scheduling Success Rate: >99% pods scheduled successfully
  • Pod Startup Time: p95 <30 seconds
  • Self-Healing Time: <1 minute to restart failed pod
  • API Availability: 99.99% uptime
  • Resource Utilization: 70%+ cluster utilization

Performance Metrics

  • API Latency: p95 <100ms
  • Scheduling Latency: p95 <1 second
  • Service Discovery: p95 <10ms
  • Network Latency: <1ms pod-to-pod

Business Metrics

  • Cluster Count: 100+ clusters managed
  • Total Nodes: 500K+ nodes globally
  • Total Pods: 15M+ pods running
  • Cost Savings: 40%+ vs manual management

This problem requires building a highly available, scalable, and efficient system for managing containerized applications across massive clusters while maintaining consistency and reliability.