Kubernetes architecture provides an API through which teams declare desired state, while controllers continuously reconcile actual state toward that target.
It enables consistent security controls, systematic redundancy and disciplined performance tuning across clusters of any size.
This advanced guide for practitioners explains how the control plane, node agents and core objects interact. Learn concrete measures that harden workloads, eliminate single points of failure and deliver predictable throughput and latency.
New to Kubernetes? Start with our beginner guide: Kubernetes architecture and core components (Beginner Guide)
What is Kubernetes Architecture?
Kubernetes operates as an API-driven control system. You define intent using resource specifications and controllers observe cluster state and act until reality matches that intent.
The control plane consists of the API server, the scheduler and the controller manager. The cloud controller manager integrates nodes, routes and load balancers from the underlying provider.
Cluster state persists in etcd. Each worker node runs kubelet, which communicates with the API server and starts containers through a CRI runtime such as containerd or CRI-O.
Kube-proxy configures stable virtual IPs and load balancing for Services using iptables or IPVS. Pods form the smallest deployable unit, while higher-level controllers manage them through Deployments, StatefulSets, DaemonSets, Jobs and CronJobs.
Consequently, the API server becomes the decisive boundary for security, availability and performance.
How is Kubernetes Architecture Secure?
A secure cluster depends on strong identity, scoped authorization and runtime isolation enforced by policy.
Admission control blocks risky configurations before they enter persistence, while platform defaults should minimize blast radius at runtime.
API Access and Governance
API access is always treated as a privileged interface. Authentication through certificates, tokens or OIDC ensures that callers have verified identities.
Authorization through RBAC enforces least privilege by scoping Roles to Namespaces where possible and binding them only to the users or ServiceAccounts that require them.
Admission control validates and, when appropriate, mutates requests so that unsafe settings are rejected and baseline standards such as labels and resource limits are present.
Moreover, audit logging is enabled and reviewed routinely because it provides an authoritative timeline for incident response and compliance.
Workload Hardening
Safe defaults provide substantial risk reduction without additional service mesh components. Pod Security Admission applies restricted profiles to the Namespaces that host application workloads.
Security Context configurations directs containers to run as non-root, drop Linux capabilities, rely on a read-only filesystem and use a restrictive seccomp profile. Where supported, AppArmor or SELinux adds another policy layer.
Service Accounts supplies identities for Pods and their bindings grant only the precise permissions that workloads require. Consequently, the compromise of a container yields limited capability and reach.
Minimal Deployment with Prudent Security Defaults
apiVersion: apps/v1
kind: Deployment
metadata:
name: web
spec:
replicas: 3
selector:
matchLabels: { app: web }
template:
metadata:
labels: { app: web }
spec:
serviceAccountName: web-sa
securityContext:
runAsNonRoot: true
seccompProfile: { type: RuntimeDefault }
containers:
- name: app
image: ghcr.io/org/web:1.2
ports: [{ containerPort: 8080 }]
securityContext:
allowPrivilegeEscalation: false
readOnlyRootFilesystem: true
capabilities: { drop: ["ALL"] }
resources:
requests: { cpu: "250m", memory: "256Mi" }
limits: { cpu: "500m", memory: "512Mi" } Network Security and Zero Trust
The default model assumes a flat Pod network; therefore, policy must add intent. NetworkPolicy establishes a default-deny stance for both ingress and egress, then allows only the flows the application requires.
Ingress or, preferably, the Gateway API provides L7 routing with a clear separation of responsibilities between application and platform teams.
TLS protects traffic consistently at the edge and, where appropriate, within the cluster using mTLS so that workload identity and policy can be enforced. As a result, lateral movement remains constrained even if an attacker reaches a Pod.
Secrets and Data Protection
Secret handling and store integrity require explicit safeguards.
Kubernetes Secrets holds sensitive values and applications ingest them via environment variables or volumes with defined rotation schedules.
Envelope encryption at rest is enabled through a KMS provider so that etcd never stores secrets in plain text.
Etcd itself is secured with TLS and client authentication, while regular snapshots are shipped to secure storage.
Restore procedures are exercised periodically, since only a successful restore proves that a backup is useful.
Network Policy that Permits Only Required Traffic
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: web-allow-only
namespace: default
spec:
podSelector:
matchLabels: { app: web }
policyTypes: ["Ingress","Egress"]
ingress:
- from:
- namespaceSelector: { matchLabels: { name: ingress } }
ports: [{ protocol: TCP, port: 8080 }]
egress:
- to:
- namespaceSelector: { matchLabels: { name: database } }
ports: [{ protocol: TCP, port: 5432 }] Five Default Stances for Safer Pods
- Ensure that runAsNonRoot is set for every container.
- Use a read-only root filesystem for typical applications.
- Drop all Linux capabilities by default and add back only what is necessary.
- Disallow host network, PID and IPC namespaces for application Pods.
- Apply a default-deny NetworkPolicy for each Namespace.
How does Kubernetes Ensure Redundancy and Resilience?
High availability eliminates single points of failure, limits the impact of planned work and accelerates recovery from unplanned incidents.
Control plane design, workload placement and data protection all contribute to this posture.
Control Plane High Availability
Multiple API servers and a healthy etcd quorum form the foundation of a resilient control plane. Multiple API servers sit behind a stable endpoint and, where the platform allows, span zones.
Etcd operates with an odd number of members, commonly three or five, distributed across zones to ensure quorum during a zone loss.
Stacked etcd, which co-locates etcd with API servers, offers operational simplicity for smaller clusters, whereas external etcd improves blast-radius control and upgrade independence for larger or regulated environments.
Additionally, control plane upgrades follow a repeatable, automated procedure because predictable steps reduce risk during maintenance.
Workload Availability
Robust placement avoids co-failure and ensures graceful maintenance.
Deployments and StatefulSets manage replicas so that rolling updates and restarts proceed without downtime. Topology spread constraints keep replicas evenly distributed across zones and nodes.
Pod anti-affinity prevents multiple replicas from landing on the same node. PodDisruptionBudgets bound voluntary disruption during node drains, autoscaler actions and upgrades.
Therefore, platform operations proceed with minimal effect on users.
Networking and Service Continuity
Service abstractions and data plane choices influence continuity under load. Kube-proxy configures forwarding with iptables or IPVS and at scale IPVS often provides superior performance and observability.
External load balancers perform health checks and distribute traffic across nodes. Readiness probes and, where necessary, readiness gates ensure that only ready Pods receive traffic.
NodeLocal DNSCache is enabled in busy clusters because it reduces upstream lookups and cross-node latency. Accordingly, shifting traffic patterns produce fewer error spikes and fewer brownouts.
Data Durability and Recovery
Stateful components require protection and verifiable recovery. CSI drivers and StorageClasses provide dynamic, policy-driven volumes that match access patterns and performance goals.
Access modes such as RWO and RWX are selected to reflect single-writer or multi-writer use and failover behavior should be validated before production. Backups and disaster recovery are treated as first-class duties.
Etcd snapshots are automated and shipped off cluster, while application data is backed up with tools that understand the engine’s consistency model. Restores are tested on clean clusters because only tested recovery can be trusted.
Seven HA Measures to Apply Promptly
- Define PodDisruptionBudgets for every user-facing Deployment.
- Configure topology spread constraints for all multi-replica workloads.
- Apply anti-affinity rules to critical replicas that must not co-locate.
- Prefer IPVS for large Services where the platform supports it.
- Enable NodeLocal DNSCache to stabilize DNS behavior in busy clusters.
- Operate multi-zone node pools and rehearse zone-level drains in staging.
- Automate etcd snapshots and perform periodic restore drills.
How Kubernetes Architecture Deliver Performance?
Performance depends on accurate placement, realistic sizing, efficient networking and appropriate storage, guided by metrics that reflect user experience.
The objective is predictable latency and steady throughput rather than sporadic peaks.
Scheduling and Resource Hygiene
The scheduler relies on resource requests for placement decisions; therefore, the absence of requests undermines predictability. Requests and limits are set from observed utilization rather than conjecture.
Requests cover typical load with headroom and limits remain close to requests so that throttling behaves predictably. QoS classes are considered explicitly, and where possible, Pods target the Guaranteed or Burstable classes to achieve consistent scheduling.
Container images remain lean so that start-up times and rollouts do not incur unnecessary network or storage overhead. Consequently, scheduling accuracy improves and kernel throttling reduces.
Node-Level Tuning
Latency-sensitive services benefit from disciplined node configuration. The CRI runtime runs on kernels that support cgroups v2 to enhance resource isolation.
The CPU Manager static policy can dedicate CPU cores to specific Pods and topology hints can reduce cross-NUMA traffic when very low latency is required.
HugePages and NUMA awareness is applied to specialized workloads such as in-memory databases and packet processing.
GPU scheduling should use dedicated node pools, strict driver and runtime alignment and minimal background daemons on GPU nodes.
Accordingly, jitter decreases and tail latency improves.
GPU Operators, Metrics and Version Alignment
To boost performance, the NVIDIA device plugin should be deployed to expose GPUs to the kubelet in a supported way. The DCGM exporter should be installed to publish GPU, PCIe, memory, and ECC metrics to Prometheus.
Driver, CUDA, and cuDNN versions should be pinned and documented because drift often breaks training images and NCCL collectives. Where MIG is available, GPU slices should be used to bin-pack inference jobs and protect memory QoS for mixed tenants.
Here’s an example of GPU device request with MIG resource:
apiVersion: v1
kind: Pod
metadata:
name: infer-mig
spec:
nodeSelector:
accelerator: nvidia
containers:
- name: server
image: ghcr.io/org/infer:1.0
resources:
requests:
nvidia.com/mig-1g.10gb: "1"
cpu: "2"
memory: "8Gi"
limits:
nvidia.com/mig-1g.10gb: "1" Network Throughput and Latency
CNI selection and data plane capabilities shape scaling behavior.
IPVS often outperforms iptables for large Services because it manages a native load-balancing table with counters and efficient lookups.
eBPF-based data planes can remove complex iptables chains, provide fast-path processing and expose rich observability; however, they should be evaluated for maturity and upgrade cadence.
NodeLocal DNSCache reduces DNS amplification effects, while zone-aware routing keeps traffic local when safe. The Gateway API offers modern L7 traffic management with a clear separation of responsibilities.
Therefore, p99 latency remains controlled as concurrency increases.
Storage IOPS and Tail Latency
Storage decisions frequently define service ceilings. StorageClasses map to performance tiers with clear expectations, for example standard, fast and local-ssd.
Local Persistent Volumes and NVMe are used for workloads that demand high IOPS and low latency, while application-level replication should compensate for local failure domains.
Warm starts are engineered deliberately: new nodes should pre-pull frequently used images, initContainers should prime caches or download models and image layers should remain compact.
As a result, batch durations shorten and interactive services avoid I/O stalls.
Autoscaling that Tracks Real Signals
Autoscaling improves reliability only when it follows metrics that represent actual demand. The Horizontal Pod Autoscaler can scale on CPU, memory or external metrics such as requests per second or queue depth.
The Vertical Pod Autoscaler can recommend or set requests from observed usage and it runs in recommend mode for dynamic services while write-back suits stable tiers.
The Cluster Autoscaler respects PodDisruptionBudgets and topology spread and node pools should be sized so that one new node admits several pending Pods. Consequently, scaling actions align with user pressure and maintain predictable capacity.
Signals to Scale on and How They Mislead
- CPU is simple to collect, yet it can lag true work on I/O-bound services.
- Memory protects against eviction, yet it does not describe throughput.
- Requests per second and queue depth reflect user pressure, yet they require careful SLO mapping.
- Custom SLIs such as p95 latency align with user experience, yet they depend on reliable telemetry.
Leverage Kubernetes with AceCloud!
Kubernetes separates intent, orchestration and execution in a way that supports secure, available and performant systems.
When resources are right-sized, placement is deliberate and autoscaling follows meaningful signals, performance becomes consistent and explainable.
Strengthen your Kubernetes foundation with AceCloud. We deliver managed clusters with hardened security, multi-zone high availability, performance-tuned data paths and GPU-ready node pools.
Request a no-cost cluster assessment and a tailored blueprint for security, redundancy and performance!
Frequently Asked Questions:
Kubernetes consists of a control plane (API server, scheduler, controller manager, cloud-controller manager) and a data plane of worker nodes that run kubelet, a container runtime (for example, containerd or CRI-O), and kube-proxy. Cluster state is stored in etcd.
The control plane makes decisions and exposes the Kubernetes API; the data plane executes those decisions by running Pods on nodes. In practice, you interact with the API server (control plane), while kubelet and the runtime on each node (data plane) keep workloads running.
For applications, you run multiple replicas and set PodDisruptionBudgets so voluntary operations (for example, drains or upgrades) do not take down too many Pods at once. For the control plane, you run multiple API servers and an odd-sized etcd quorum (typically three or five members), using either stacked or external etcd topologies.
A Service provides a stable virtual IP and DNS name over a changing set of Pods. kube-proxy programs node-level rules (iptables or IPVS) so connections to the Service are load-balanced to healthy backends.
etcd is the consistent key-value store that backs all cluster data. Because every resource is persisted there, you must back it up and, for sensitive data such as Secrets, enable API-server envelope encryption with a KMS provider.
