Configuring infrastructure for containerized applications means building an environment that supports orchestration, networking, storage, and security at scale. Here’s how to approach it.
1. Assess Requirements
Workload analysis: determine whether your applications are stateful or stateless, CPU-bound, memory-heavy, or GPU-dependent.
Scalability: define horizontal and vertical scaling targets.
Performance: quantify CPU, memory, I/O, and GPU needs.
Security: specify isolation levels, access control requirements, and compliance constraints.
High availability: decide on redundancy and failover targets (zones, regions, clusters).
2. Choose an Orchestration Platform
Kubernetes (K8s) is the default choice for most teams.
Alternatives include Docker Swarm, Red Hat OpenShift, Amazon ECS, and HashiCorp Nomad.
Match the platform to your operational maturity and feature requirements.
3. Infrastructure Design
Bare metal or virtualized: Bare metal eliminates hypervisor overhead for large-scale, steady-state workloads. Virtualization (VMware vSphere, Hyper-V, Proxmox) adds flexibility for mixed workloads and easier lifecycle management.
Cloud, on-prem, or hybrid: Managed Kubernetes services (EKS, AKS, GKE) reduce control-plane ops. On-premises gives you full hardware control and data locality. Hybrid setups let you burst to cloud or keep sensitive data local.
4. Compute Resources
Size nodes for your workload profile: high-core-count CPUs (Intel Xeon, AMD EPYC) for throughput, high-frequency for latency-sensitive apps.
GPU workloads: provision NVIDIA A100, H100, T4, or V100 cards and install the NVIDIA GPU Operator (or vendor equivalent) for Kubernetes device plugin and scheduling support.
Set resource requests and limits per container; use node pools or taints/tolerations to isolate specialized hardware.
5. Networking
Pick a CNI plugin: Calico for policy enforcement and BGP peering, Cilium for eBPF-based visibility and L7 policy, Flannel for simplicity, Weave Net for small clusters.
Load balancing: MetalLB for on-prem L2/BGP; cloud load balancers (ALB, NLB, Cloud Load Balancing) for managed services.
Ingress: deploy NGINX Ingress Controller, Traefik, or HAProxy for HTTP/HTTPS routing, TLS termination, and rate limiting.
Service mesh: Istio or Linkerd if you need mTLS, fine-grained traffic splitting, or distributed tracing without code changes.
Enable dynamic provisioning via StorageClasses so PVCs bind automatically.
Back up PVs with Velero (supports CSI snapshots) or Kasten K10 for application-consistent backups.
7. Security
Image supply chain: pull from private registries, sign images (cosign, Notary), scan with Trivy, Grype, or Syft in CI.
RBAC: least-privilege roles for users and service accounts; avoid cluster-admin except for break-glass.
Network policies: default-deny ingress/egress; allow only required pod-to-pod and external paths.
Secrets: externalize with HashiCorp Vault (via CSI driver or Vault Agent Injector), AWS Secrets Manager, or Azure Key Vault. Avoid plain Kubernetes Secrets for sensitive data.
Admission control: use Kyverno or OPA Gatekeeper for policy enforcement (e.g., require non-root, drop capabilities, forbid latest tags). PodSecurityPolicies are deprecated; use Pod Security Standards instead.
8. Monitoring and Logging
Metrics: Prometheus (or VictoriaMetrics/Thanos for scale) with Grafana dashboards. Monitor control plane, node exporters, kube-state-metrics, and application endpoints.
Logs: Loki (cost-effective) or EFK/ELK stack. Tail containers via Fluent Bit or Vector; structure logs as JSON.
Tracing: Jaeger or Tempo for distributed traces; instrument services with OpenTelemetry SDKs.
9. Automation and CI/CD
Infrastructure as Code: Terraform for cloud resources and cluster bootstrap; Ansible or Talos for node configuration; Flux or Argo CD for GitOps cluster sync.
Pipelines: build images in CI (GitLab CI, GitHub Actions, Jenkins), push to registry, run security scans, then promote via GitOps or Argo Rollouts for progressive delivery.
10. Backup and Disaster Recovery
Control plane: automated etcd snapshots (cronjob or operator) stored off-cluster.
Workloads and data: Velero with CSI snapshot support for full cluster or namespace-scoped restores.
Test restores quarterly. Document RTO/RPO per application tier.
11. Load Testing
Run k6, JMeter, or Locust against staging to validate autoscaling, resource limits, and latency budgets before production traffic.
12. Compliance and Governance
Map controls to frameworks (CIS Kubernetes Benchmark, PCI-DSS, HIPAA, GDPR).
Enforce policies with Kyverno or OPA: required labels, resource quotas, approved registries, disallowed hostPath volumes.
Audit with kube-audit or Falco for runtime anomalies.
Reference Architecture Example
Compute: AMD EPYC 7763 nodes, 256 GB RAM, NVIDIA A100 80GB for ML pool.
Storage: Ceph cluster (3+ monitors, 6+ OSDs) with RBD CSI for block, CephFS CSI for shared files.
Network: Calico with BGP to top-of-rack switches; MetalLB L2 mode for ingress VIPs.
Backup: Velero daily schedules to S3-compatible object store; etcd snapshots every 6 hours.
Observability: Prometheus + Grafana + Loki + Tempo; alerting via Alertmanager to PagerDuty.
Ali YAZICI is a Senior IT Infrastructure Manager with 15+ years of enterprise experience. While a recognized expert in datacenter architecture, multi-cloud environments, storage, and advanced data protection and Commvault automation , his current focus is on next-generation datacenter technologies, including NVIDIA GPU architecture, high-performance server virtualization, and implementing AI-driven tools. He shares his practical, hands-on experience and combination of his personal field notes and “Expert-Driven AI.” he use AI tools as an assistant to structure drafts, which he then heavily edit, fact-check, and infuse with my own practical experience, original screenshots , and “in-the-trenches” insights that only a human expert can provide.
