Electronics Guide

Container Runtime Reliability

The container runtime manages the lifecycle of containers and represents a critical component in the reliability chain. Docker Engine operates as a client-server application with a daemon process that manages container operations. Runtime reliability depends on proper daemon configuration, resource isolation, and monitoring.

Container isolation relies on Linux kernel features including namespaces for process isolation, cgroups for resource limiting, and union filesystems for layered storage. Failures in any of these mechanisms can compromise container reliability. Engineers should monitor kernel-level metrics and maintain updated host operating systems to ensure these foundational components function correctly.

The Docker daemon represents a single point of failure on each host. Configuring live restore allows containers to continue running even when the daemon stops for updates or crashes. Implementing daemon health checks and automatic restart policies ensures rapid recovery from daemon failures.

Image Reliability and Security

Container images form the basis for deployed applications, and their reliability affects every container instance. Image reliability encompasses build reproducibility, vulnerability management, and proper layering practices.

Building reliable images requires deterministic builds using pinned base image versions and dependency versions. Avoid using the "latest" tag for base images, as this introduces unpredictability. Instead, reference specific image digests or semantic version tags to ensure consistent builds across environments.

Image scanning identifies vulnerabilities in base images and application dependencies. Integrate scanning into continuous integration pipelines to catch vulnerabilities before deployment. Establish policies that prevent deployment of images with critical vulnerabilities, and maintain a regular cadence of base image updates to incorporate security patches.

Layer optimization affects both reliability and performance. Minimize the number of layers by combining related commands, order layers from least to most frequently changed to maximize cache utilization, and remove unnecessary files within the same layer they were created to reduce image size and attack surface.

Container Networking Reliability

Docker networking enables communication between containers and external systems. Network reliability requires understanding the available network drivers and their failure modes.

Bridge networks provide isolated networks for containers on a single host. Overlay networks extend networking across multiple Docker hosts, enabling swarm services. Network failures can result from IP address exhaustion, DNS resolution problems, or network driver issues.

Implementing health checks that verify network connectivity helps detect network-related failures early. Configure appropriate timeouts and retry logic in applications to handle transient network issues gracefully. Monitor network metrics including connection counts, latency, and error rates to identify degradation before it causes outages.

Storage Driver Reliability

Storage drivers manage how container filesystems operate, affecting both performance and reliability. The overlay2 driver is recommended for most use cases, providing good performance with copy-on-write semantics.

Storage driver failures can manifest as corrupted container filesystems, inability to start containers, or data loss. Monitor disk space utilization carefully, as running out of space in the Docker data directory causes widespread failures. Implement log rotation and image cleanup policies to prevent storage exhaustion.

For containers requiring persistent data, avoid storing important data in the container's writable layer. Instead, use volumes or bind mounts that persist independently of container lifecycle. This separation ensures data survives container recreation and enables proper backup procedures.

Control Plane Reliability

The Kubernetes control plane consists of the API server, controller manager, scheduler, and etcd datastore. These components manage cluster state and must remain available for the cluster to function correctly.

Running multiple control plane replicas provides high availability. The API server is stateless and can scale horizontally. The controller manager and scheduler use leader election, allowing multiple replicas with only one active leader at a time. Etcd requires careful attention to cluster topology, typically running three or five members to maintain quorum while tolerating failures.

Etcd reliability is paramount because it stores all cluster state. Implement regular etcd backups using snapshot functionality. Monitor etcd metrics including leader changes, proposal failures, and disk latency. Ensure etcd runs on fast storage, as disk performance directly impacts cluster responsiveness.

Control plane networking requires reliable connectivity between components and to worker nodes. Network partitions can prevent the control plane from managing workloads, even when worker nodes themselves remain healthy. Implement network monitoring and redundant network paths where possible.

Worker Node Reliability

Worker nodes run containerized workloads and must remain healthy for applications to function. Node reliability encompasses hardware health, operating system stability, and kubelet operation.

The kubelet is the primary node agent responsible for managing pods on each node. Kubelet failures prevent new pods from starting and existing pods from being monitored. Configure the kubelet with appropriate resource reservations to prevent system components from being starved of resources by application workloads.

Node problem detector identifies hardware and kernel issues that affect reliability. Common problems include disk pressure, memory pressure, network unavailability, and kernel deadlocks. Configure node problem detector to create conditions that trigger pod eviction when serious problems occur.

Container runtime reliability on worker nodes affects all pods on that node. Monitor container runtime metrics and implement automatic node remediation for persistent runtime issues. Consider using node auto-repair features provided by cloud platforms to automatically replace unhealthy nodes.

Cluster Autoscaling

Cluster autoscaling adjusts the number of worker nodes based on workload demand. Proper autoscaling configuration ensures sufficient capacity for reliability while optimizing resource utilization.

The cluster autoscaler adds nodes when pods cannot be scheduled due to insufficient resources and removes underutilized nodes. Configure appropriate scale-down delays to prevent thrashing during variable workloads. Set minimum node counts to ensure baseline capacity remains available even during low demand periods.

Node pools or node groups allow different node configurations for different workload types. Separate system components from application workloads using node selectors or taints and tolerations. This separation prevents resource contention between critical system components and application pods.

Liveness and Readiness Probes

Probes enable Kubernetes to monitor application health and take appropriate action when problems occur. Proper probe configuration is essential for reliable pod management.

Liveness probes determine whether a container is running correctly. When a liveness probe fails, Kubernetes restarts the container. Configure liveness probes to detect conditions where the application is running but unable to make progress, such as deadlocks or corrupted state.

Readiness probes determine whether a container is ready to accept traffic. When a readiness probe fails, Kubernetes removes the pod from service endpoints but does not restart it. Use readiness probes to prevent traffic from reaching pods during startup, when dependencies are unavailable, or during graceful shutdown.

Startup probes handle applications with long initialization times. The startup probe runs first and must succeed before liveness and readiness probes begin. This prevents liveness probe failures during slow startups from causing restart loops.

Probe configuration requires careful tuning. Set initialDelaySeconds appropriately for application startup time. Configure periodSeconds, timeoutSeconds, and failureThreshold to balance between detecting failures quickly and avoiding false positives during transient issues. Test probe behavior under various failure scenarios to ensure they detect real problems without causing unnecessary restarts.

Restart Policies and Backoff

Kubernetes restart policies control how failed containers are handled. The Always policy restarts containers regardless of exit code, suitable for long-running services. The OnFailure policy restarts only on non-zero exit codes, appropriate for jobs that should not restart on successful completion. The Never policy prevents any restarts.

Kubernetes implements exponential backoff for container restarts to prevent rapid restart loops from consuming excessive resources. The backoff starts at ten seconds and doubles with each restart, capping at five minutes. Understanding this behavior helps in diagnosing startup issues and setting appropriate probe timings.

CrashLoopBackOff status indicates a container is repeatedly failing and being restarted. Investigate the root cause by examining container logs, events, and resource constraints. Common causes include misconfiguration, missing dependencies, resource exhaustion, and application bugs exposed by the containerized environment.

Pod Disruption Budgets

Pod Disruption Budgets (PDBs) protect applications from excessive voluntary disruptions during cluster maintenance operations. PDBs specify the minimum number or percentage of pods that must remain available during disruptions.

Voluntary disruptions include node drains, cluster upgrades, and autoscaler scale-down operations. PDBs do not protect against involuntary disruptions such as hardware failures or application crashes. Configure PDBs based on application availability requirements and the minimum replicas needed for continued operation.

Overly restrictive PDBs can block cluster maintenance operations indefinitely. Balance availability requirements against operational needs by ensuring PDBs allow at least one pod to be unavailable or setting maxUnavailable to a reasonable value. Monitor PDB status to identify configurations that are blocking necessary maintenance.

Pod Priority and Preemption

Priority classes assign relative importance to pods, determining scheduling order and preemption behavior. Higher priority pods can preempt lower priority pods when resources are scarce.

Define priority classes that reflect business criticality. System-critical components should have the highest priority to ensure they remain running. Configure preemption policies to control whether pods can preempt others of lower priority.

Avoid assigning high priority to all workloads, as this defeats the purpose of prioritization. Regularly review priority class assignments to ensure they accurately reflect current business requirements. Monitor preemption events to identify resource constraints that may require capacity adjustments.

Node Health Monitoring

The node controller monitors node health by watching for regular heartbeats from the kubelet. When heartbeats stop, the node is marked as NotReady after a configurable timeout, typically forty seconds.

Node conditions provide detailed health information including Ready, MemoryPressure, DiskPressure, PIDPressure, and NetworkUnavailable. Monitor these conditions to identify nodes experiencing problems before complete failure occurs.

Extended node unavailability triggers pod eviction after the pod eviction timeout, typically five minutes. During this period, pods on the failed node continue to consume resource quotas and may prevent scheduling of replacement pods. For faster recovery, consider reducing eviction timeouts or implementing custom controllers that respond more quickly to failures.

Workload Distribution

Distributing workloads across multiple nodes prevents single node failures from causing complete application outages. Pod anti-affinity rules ensure replicas of the same application run on different nodes.

Topology spread constraints provide fine-grained control over workload distribution across failure domains such as nodes, zones, or regions. Configure maximum skew values to ensure relatively even distribution while allowing some flexibility for scheduling.

Node selectors and affinity rules can unintentionally concentrate workloads on a subset of nodes. Regularly review scheduling constraints to ensure they do not create single points of failure. Use pod topology spread constraints in combination with affinity rules to balance specific requirements against fault tolerance.

Zone and Region Awareness

Cloud environments organize infrastructure into zones within regions. Zone failures can affect all nodes in that zone simultaneously. Deploying across multiple zones provides resilience against zone-level failures.

Configure workloads to spread across zones using topology spread constraints or pod anti-affinity with topology keys for zones. Ensure sufficient replicas exist to maintain availability when an entire zone becomes unavailable.

Cross-zone traffic incurs additional latency and cost. Balance availability requirements against performance and cost by carefully selecting the number of zones and replica distribution. For latency-sensitive applications, consider zone-aware routing that prefers same-zone communication when possible.

Persistent Volume Architecture

Kubernetes abstracts storage through Persistent Volumes (PVs) and Persistent Volume Claims (PVCs). This abstraction separates storage provisioning from consumption, enabling portable storage configurations.

Storage classes define different tiers of storage with varying performance and reliability characteristics. Configure storage classes for different use cases such as high-performance SSD storage for databases and cost-effective standard storage for logs. Include appropriate reclaim policies to prevent accidental data loss when claims are deleted.

Dynamic provisioning automatically creates storage when claims are submitted. Ensure provisioners are highly available and monitor their health. Failed provisioners prevent new storage from being created, potentially blocking deployments.

Volume Replication and Snapshots

Storage replication protects against data loss from storage system failures. Many cloud storage systems provide synchronous replication within a zone and options for cross-zone or cross-region replication.

Volume snapshots enable point-in-time backups of persistent volumes. Implement regular snapshot schedules for critical data. Test restore procedures regularly to ensure snapshots are actually recoverable.

Application-consistent snapshots require coordination with the application to ensure data on disk is in a consistent state. For databases, this typically means flushing buffers and pausing writes during the snapshot. Consider using backup tools designed for specific applications rather than relying solely on volume snapshots.

Storage Failure Modes

Storage failures can manifest as performance degradation, data corruption, or complete unavailability. Understanding common failure modes enables better preparation and faster diagnosis.

Performance degradation often precedes complete failure. Monitor storage latency, IOPS, and throughput. Implement alerts for storage performance metrics that deviate from baselines. Some storage systems provide health indicators that can predict impending failures.

Network storage failures may result from network issues rather than storage system problems. Ensure network connectivity between nodes and storage systems is monitored and redundant. Configure appropriate timeouts and retry logic for storage operations.

Data corruption can result from software bugs, hardware failures, or improper shutdown. Implement integrity checking where possible. For critical applications, consider storage systems with built-in checksumming and automatic repair capabilities.

Volume Access Modes

Access modes control how volumes can be mounted. ReadWriteOnce (RWO) allows mounting by a single node. ReadOnlyMany (ROX) allows read-only mounting by multiple nodes. ReadWriteMany (RWX) allows read-write mounting by multiple nodes.

Access mode selection affects availability. RWO volumes cannot be moved between nodes without first unmounting, which requires terminating pods using the volume. For high availability stateful applications, consider whether RWX storage or application-level replication is more appropriate.

Not all storage systems support all access modes. Verify storage class capabilities match application requirements before deployment. Attempting to use unsupported access modes results in mount failures.

Network Policy Design

Network policies use labels to select pods and define allowed traffic. A well-designed label strategy simplifies policy management and reduces the risk of misconfiguration.

Default deny policies block all traffic except what is explicitly allowed. While this approach maximizes security, it requires comprehensive policies for all legitimate traffic patterns. Implement default deny carefully in stages, monitoring for blocked traffic.

Policy complexity increases operational risk. Keep policies simple and well-documented. Use policy visualization tools to understand the effective traffic rules. Test policies in non-production environments before applying to production.

Network Policy Implementation

Network policies require a compatible network plugin (CNI) that implements the NetworkPolicy API. Not all network plugins support network policies, and capabilities vary between implementations.

Monitor network policy enforcement to detect issues. Blocked traffic should generate observable events or metrics. Implement network policy logging where available to troubleshoot connectivity problems.

Network policy changes take effect immediately upon application. Plan policy changes carefully and consider implementing them during maintenance windows for critical systems. Have rollback procedures ready in case new policies cause unexpected connectivity issues.

DNS Reliability for Network Policies

Many network policies allow traffic based on IP addresses, but service discovery typically uses DNS. Ensure network policies allow DNS traffic to cluster DNS services.

External traffic policies based on IP addresses can be fragile if external service IPs change. Consider using egress gateways or external service abstractions that centralize external connectivity management.

DNS caching affects policy enforcement timing. Pods may continue reaching services at old IP addresses until DNS caches expire. Consider DNS TTL settings when planning network policy changes that affect service connectivity.

Service Mesh Architecture

Service meshes typically deploy sidecar proxies alongside application containers. These proxies intercept all network traffic, enabling advanced traffic management without application changes.

The control plane manages proxy configuration and policy distribution. Control plane availability directly affects the ability to update traffic policies, though data plane traffic typically continues flowing during control plane outages.

Sidecar injection can be automatic or manual. Automatic injection simplifies deployment but may interfere with some workloads. Provide options to exclude specific namespaces or pods from injection when necessary.

Traffic Management for Reliability

Service meshes enable sophisticated traffic management including load balancing, circuit breaking, and retry policies. These capabilities enhance application reliability when properly configured.

Circuit breakers prevent cascading failures by stopping traffic to unhealthy services. Configure circuit breaker thresholds based on service characteristics. Overly aggressive circuit breakers can cause unnecessary service isolation during transient issues.

Retry policies automatically retry failed requests. Configure retry budgets to prevent retry storms that overwhelm recovering services. Use exponential backoff with jitter to spread retry load over time.

Timeout configuration prevents requests from waiting indefinitely. Set timeouts based on expected service latency with appropriate margin. Implement deadline propagation to ensure end-to-end timeout enforcement across service chains.

Mutual TLS and Security

Service meshes can automatically encrypt service-to-service traffic using mutual TLS (mTLS). This encryption provides both confidentiality and authentication.

Certificate management is handled by the service mesh control plane. Monitor certificate expiration and renewal. Ensure the certificate authority infrastructure is highly available and properly secured.

Transitioning to mTLS requires careful planning. Use permissive mode initially to identify services that may have compatibility issues. Monitor for failed connections that may indicate certificate problems or misconfigured services.

Observability Features

Service meshes provide detailed observability into service communication including metrics, distributed tracing, and access logging. These capabilities are essential for understanding and troubleshooting reliability issues.

Mesh metrics provide insight into request rates, latency distributions, and error rates for all service-to-service communication. Integrate mesh metrics with existing monitoring systems. Create dashboards and alerts based on service-level indicators derived from mesh telemetry.

Distributed tracing tracks requests across service boundaries. Configure sampling rates to balance observability needs against storage and performance costs. Ensure trace context propagates correctly through all services.

Registry Architecture

Registries can be operated as managed services or self-hosted. Managed registries from cloud providers typically offer high availability with built-in replication. Self-hosted registries require explicit high availability configuration.

Registry storage backend reliability affects image availability. Use replicated storage backends and implement regular backups of registry data. Monitor storage capacity to prevent space exhaustion from blocking image pushes.

Registry authentication and authorization systems must remain available for pull and push operations. Ensure authentication backend high availability and implement caching where possible to reduce authentication system load.

Image Distribution

Image pulls can create significant network traffic, especially during cluster-wide deployments or scaling events. Implement registry mirrors or pull-through caches closer to clusters to reduce pull latency and external bandwidth usage.

Image pull policies affect both reliability and resource usage. Always pulling ensures latest images but increases registry dependency and network usage. Using IfNotPresent relies on cached images, which can cause issues when images are updated in place without changing tags. Use immutable tags or image digests for production workloads.

Image pull failures can prevent pods from starting. Configure imagePullSecrets correctly in service accounts. Monitor for ImagePullBackOff status and investigate registry connectivity, authentication, and image availability.

Registry Redundancy

Multi-region registry replication provides resilience against regional failures. Configure automatic replication between registries in different regions. Implement failover mechanisms that redirect pulls to healthy registries.

Maintaining synchronized registry content requires careful image lifecycle management. Implement consistent tagging and retention policies across replicas. Monitor replication lag to ensure all regions have current images.

Consider maintaining critical images in multiple registries, including potentially different providers, for maximum resilience. Document and test procedures for redirecting image pulls during registry outages.

Chart Development Best Practices

Well-structured charts include appropriate defaults with override capabilities. Document all configurable values and their effects. Include sensible resource requests and limits as defaults.

Chart testing validates templates render correctly and produce valid Kubernetes manifests. Implement chart tests that verify deployed applications function correctly. Use linting tools to catch common chart errors before deployment.

Version charts semantically to communicate the impact of changes. Major versions indicate breaking changes, minor versions add functionality, and patch versions fix bugs. Maintain a changelog documenting changes between versions.

Release Management

Helm releases track deployed chart instances. Maintain release history to enable rollbacks when deployments cause issues. Configure appropriate history limits to balance rollback capability against storage usage.

Atomic deployments ensure releases either succeed completely or roll back automatically. Use the atomic flag for critical deployments to prevent partially deployed releases.

Release naming conventions help identify deployments and their purposes. Include environment indicators in release names. Implement naming standards across teams to ensure consistency.

Chart Repository Reliability

Chart repositories store and distribute Helm charts. Repository availability affects deployment capability. Mirror external chart repositories to maintain deployment capability during external outages.

Sign charts cryptographically to verify authenticity and integrity. Verify signatures during installation to prevent deployment of tampered charts. Maintain secure key management practices for chart signing.

Repository organization affects maintainability. Separate internal charts from external dependencies. Implement access controls appropriate for chart sensitivity. Regularly audit repository contents and remove unused charts.

Operator Architecture

Operators use custom resources to define desired state and controllers to reconcile actual state with desired state. This declarative approach enables self-healing and reduces manual intervention.

Controller reconciliation loops continuously check and correct drift from desired state. Design controllers to be idempotent, producing the same result regardless of how many times reconciliation runs. Handle partial failures gracefully by allowing reconciliation to retry.

Leader election ensures only one operator instance is active when running multiple replicas. Configure leader election timeouts and lease durations appropriately for your availability requirements.

Operator Reliability Patterns

Status reporting provides visibility into operator actions and managed resource health. Update status subresources with meaningful conditions that help users understand current state and any issues.

Event generation creates audit trails of operator actions. Generate events for significant state changes, errors, and warnings. Events help users and operators understand what happened and when.

Finalizers prevent resource deletion until cleanup completes. Use finalizers when operators create external resources that must be deleted before the custom resource can be removed. Implement finalizer handling carefully to avoid blocking resource deletion indefinitely.

Operator Upgrades

Operator upgrades must maintain compatibility with existing custom resources. Use custom resource versioning to manage schema evolution. Implement conversion webhooks when breaking changes are necessary.

Rolling upgrades of operators should not disrupt managed workloads. Test upgrades thoroughly, including scenarios where the operator is unavailable during upgrade. Design controllers to handle existing resources gracefully after restart.

Document upgrade procedures and any manual steps required. Provide migration tools when data transformations are necessary. Test rollback procedures to ensure recovery options exist if upgrades cause problems.

Identity and Ordering

StatefulSets provide stable, predictable pod names and network identities. Pods are named with an ordinal index starting from zero. This predictability enables applications to implement features like leader election based on pod name.

Ordered deployment and scaling ensure pods are created and deleted in sequence. This ordering is important for applications like databases that require specific initialization sequences. Parallel pod management can be enabled when ordering is not required.

Headless services provide DNS entries for individual pods, enabling direct pod addressing. Ensure DNS is reliable, as StatefulSet applications often depend on DNS for peer discovery.

Update Strategies

Rolling updates update pods one at a time in reverse ordinal order. This approach maintains availability but extends update duration. Configure partition values to control which pods are updated, enabling canary deployments.

OnDelete strategy requires manual pod deletion to trigger updates. This approach provides maximum control but requires operational intervention. Use OnDelete for applications where automatic updates could cause data loss or corruption.

Update strategies interact with pod management policies. Understand how your chosen update strategy behaves with parallel versus ordered pod management. Test update procedures in non-production environments.

Volume Claim Templates

Volume claim templates automatically provision storage for each pod. Each pod receives its own persistent volume that persists across pod recreation. This automation simplifies stateful application deployment.

Volume claim retention policies control what happens to volumes when pods or StatefulSets are deleted. The default behavior retains volumes, preventing accidental data loss. Configure retention policies based on data persistence requirements.

Storage class selection in volume claim templates affects performance and reliability. Ensure selected storage classes meet application requirements for IOPS, throughput, and availability. Consider zone-specific storage classes for zone-aware StatefulSets.

Job Configuration

Completions specify how many pods must complete successfully. Parallelism controls how many pods run simultaneously. Configure these values based on workload characteristics and resource availability.

Backoff limits control how many times a job retries failed pods before marking the job as failed. Set appropriate limits to allow transient failure recovery without indefinite retries. Monitor job failures to identify persistent issues requiring investigation.

Active deadline seconds limit total job execution time. This prevents runaway jobs from consuming resources indefinitely. Set deadlines based on expected execution times with appropriate margin.

CronJob Scheduling

CronJobs create jobs on schedule. Schedule syntax follows cron format, enabling flexible scheduling from minutely to yearly. Test schedule expressions to verify they produce expected run times.

Concurrency policies control behavior when a new scheduled time arrives while a previous job is still running. Allow creates new jobs regardless of running jobs. Forbid skips new jobs if previous jobs are running. Replace terminates running jobs and creates new ones.

Starting deadline seconds define how late a missed job can start. If the cluster is unable to create a job within this deadline, the run is skipped. Configure deadlines based on how critical schedule adherence is for your workloads.

Job Completion and Cleanup

Completed jobs remain in the cluster until explicitly deleted or automatically cleaned up. TTL controllers can automatically delete completed jobs after a configurable duration. Balance cleanup speed against the need to investigate completed jobs.

Failed jobs may leave pods in various states. Implement monitoring for failed jobs and establish procedures for investigation and remediation. Consider implementing alerting for critical job failures.

Job history limits control how many completed and failed jobs CronJobs retain. Configure limits to provide sufficient history for troubleshooting while preventing resource accumulation.

Resource Requests and Limits

Resource requests guarantee minimum resources for scheduling. Requests affect which nodes pods can be scheduled on. Setting accurate requests ensures pods land on nodes with sufficient capacity.

Resource limits cap maximum resource usage. CPU limits throttle container CPU usage. Memory limits trigger container termination when exceeded. Configure limits to prevent individual containers from affecting other workloads.

The relationship between requests and limits affects quality of service. Guaranteed QoS requires equal requests and limits. Burstable QoS has limits higher than requests. BestEffort QoS has no requests or limits. Higher QoS classes receive better protection during resource pressure.

Resource Quotas

Resource quotas limit total resource consumption within namespaces. Quotas prevent individual namespaces from monopolizing cluster resources. Configure quotas based on team allocations and cluster capacity.

Quota enforcement affects pod scheduling. Pods exceeding quota cannot be created. Monitor quota usage to identify namespaces approaching limits. Implement processes for quota increase requests.

Limit ranges set default and maximum values for container resources. Use limit ranges to ensure all containers have resource specifications and to prevent excessive resource requests.

Vertical Pod Autoscaling

Vertical Pod Autoscaler (VPA) automatically adjusts resource requests based on observed usage. VPA recommendations help right-size workloads and identify resource specification issues.

VPA can operate in recommendation-only mode for visibility without automatic changes. Auto mode updates pod specifications, requiring pod recreation. Initial mode only sets resources for new pods.

VPA interacts with Horizontal Pod Autoscaler and should generally not be used simultaneously on the same resource type. Design scaling strategies that use appropriate autoscaling approaches for each workload.

Horizontal Pod Autoscaling

Horizontal Pod Autoscaler (HPA) adjusts replica counts based on metrics. Configure target metrics and thresholds based on workload characteristics. Common metrics include CPU utilization, memory utilization, and custom application metrics.

Scaling behavior configuration controls scale-up and scale-down speed. Configure stabilization windows to prevent thrashing during variable load. Set appropriate scale-down policies to avoid removing capacity too quickly.

HPA requires metrics availability. Ensure metrics server or custom metrics adapters are reliable. HPA stops scaling when metrics are unavailable, potentially leaving workloads under or over provisioned.

Federation Architecture

Federation typically involves a control plane that manages workload distribution across member clusters. The control plane maintains a unified view while individual clusters retain autonomy for local operations.

Workload placement policies determine which clusters host specific workloads. Consider factors including geographic proximity to users, regulatory requirements for data locality, resource availability, and cluster health.

Control plane availability affects federation-level operations. Individual clusters continue operating independently during control plane outages. Design for control plane resilience appropriate to your federation management requirements.

Cross-Cluster Networking

Services spanning multiple clusters require cross-cluster networking solutions. Options include service mesh federation, multi-cluster service discovery, and global load balancers.

Network latency between clusters affects cross-cluster communication. Design applications to tolerate inter-cluster latency. Consider data locality and prefer same-cluster communication when possible.

Security considerations for cross-cluster networking include authentication between clusters, encryption of inter-cluster traffic, and network policy enforcement across cluster boundaries.

Disaster Recovery

Federation enables disaster recovery by maintaining workloads across geographically distributed clusters. Configure workload distribution to ensure sufficient capacity remains available if entire clusters fail.

State synchronization between clusters affects recovery point objectives. Replicate critical state synchronously where feasible. Asynchronous replication reduces latency impact but may result in data loss during failures.

Failover procedures should be automated where possible and well-documented where manual intervention is required. Test disaster recovery procedures regularly to verify they work as expected and teams are familiar with executing them.

Configuration Management

Consistent configuration across federated clusters reduces operational complexity and configuration-related failures. Use GitOps or similar approaches to maintain configuration as code.

Cluster-specific configuration overrides enable customization while maintaining baseline consistency. Manage overrides explicitly and document the reasons for differences.

Configuration drift detection identifies clusters that have diverged from desired state. Implement automated drift detection and remediation to maintain consistency across the federation.