Infrastructure Scaling, Capacity Planning, and High Availability Questions

Approach summary
I’d design for independent AZ fault domains, automated failover, and fast recovery to meet ~99.999% (≈5.3 min/year) — combine infrastructure redundancy, resilient services, strong monitoring, and tested runbooks.

Compute

• Spread instances across ≥3 AZs using Auto Scaling Groups (ASG) and ALB/NLB with cross-AZ enabled.
• Use immutable deployments (AMI/container images) and blue/green or canary rollouts to avoid in-place failures.
• Maintain warm pools or standby instances to reduce RTO for scale or replacement.
• Health checks + automated instance replacement; use lifecycle hooks to drain connections gracefully.

Storage

• Use region-aware, AZ-redundant services: S3 for objects (built-in durability/availability), EFS/Azure Files/GCP Filestore in multi-AZ mode for shared FS.
• For block/db: use managed Multi‑AZ DB (RDS Multi‑AZ or Aurora with reader/writer endpoints) so primary failover is automatic. Implement automated backups and point-in-time recovery; practice restores regularly.
• Replicate critical state across AZs (not just snapshots) to keep RPO near zero.

Networking

• Deploy subnets per AZ, NACLs and route tables redundant; deploy one NAT Gateway per AZ (avoid single NAT).
• Use ALB/NLB across AZs with health checks; use Route53 health checks and low TTL DNS for service endpoints.
• Ensure redundant Internet/Transit Gateways and multiple VGWs or Direct Connect links where applicable.

Service-level considerations

• Define SLIs/SLOs (availability, latency), set error budgets, and map them to components.
• Invest in observability: synthetic checks, per-AZ metrics, distributed tracing, alerting with escalation playbooks.
• Test failover regularly (chaos engineering, simulated AZ outage) and maintain runbooks/automation for failback.
• Plan maintenance windows, automated patching with canary, and capacity buffers for spike resilience.

Trade-offs

• Cost vs. availability: warm standby and multi-AZ resources increase cost — justify by business impact.
• Aim for elimination of single points of failure, automated recovery, and continuous testing to reliably approach five nines.

Approach summary
Describe traffic, session model (sticky vs external), instance capacity (RPS, concurrent sessions, memory), target SLAs, and choose autoscaling signals (CPU, RPS per instance, queue length, custom metrics like active_sessions).

Capacity estimation formulas

• Estimate required instances by throughput:

instances_by_rps = ceil( peak_rps / instance_max_rps )

(instance_max_rps = measured median RPS an instance serves at target latency)

• Estimate by concurrent sessions (stateful / sticky):

instances_by_sessions = ceil( peak_concurrent_sessions / sessions_per_instance )

(sessions_per_instance = memory_limit_for_sessions / avg_session_size)

• Add headroom:

target_instances = ceil( max(instances_by_rps, instances_by_sessions) * (1 + headroom_fraction) )

(recommended headroom_fraction = 0.2–0.5 depending on burstiness)

Autoscaling thresholds & policies

• For external session store (stateless app servers): scale on RPS or request latency:
  • Scale out when RPS_per_instance > 0.7 * instance_max_rps or p95_latency > SLA
  • Scale in when RPS_per_instance < 0.4 * instance_max_rps for cooldown
• For sticky sessions (stateful): prefer scaling on queue length, new session rate, or manual/controlled scale to avoid rebalancing cost. Use a gentle scale rate and session draining.
• Example threshold formulas:

scale_out_when: (total_rps / current_instances) > 0.7 * instance_max_rps
scale_in_when:  (total_rps / current_instances) < 0.4 * instance_max_rps  AND current_instances > min_instances

Operational considerations

• Cooldown windows: 3–10 minutes; longer for sticky sessions.
• Draining: Evict new traffic, wait for session timeout or migrate sessions gracefully.
• Use health checks and circuit breakers; monitor session store latency (for external store).

Testing & validation

• Baseline profiling: measure instance_max_rps, avg_session_size, memory use, p50/p95 latency.
• Load tests: ramp to peak and 2x peak with realistic session patterns; validate scaling decision times, error rates, and session stickiness.
• Chaos/rolling tests: force instance termination to verify session draining and sticky behavior.
• Soak tests: sustained load for hours to capture memory leaks.
• Metrics to observe: p95 latency, error rate, CPU, memory, RPS_per_instance, session_store_latency, time_to_scale, dropped_sessions.

Trade-offs

• Sticky sessions simplify design but reduce flexibility and require careful drain logic; external session stores add complexity but enable faster autoscaling and better utilization. Choose based on SLA and operational readiness.

Immediate mitigations (fast, actionable)

• Confirm the exact quota using Service Quotas / AWS CLI (aws service-quotas get-service-quota) to know which limit is hit.
• Request an emergency quota increase via Service Quotas console or Support (choose Business/Enterprise for faster SLAs).
• Free capacity now: stop or delete noncritical RDS instances, snapshots, or automated snapshots; scale down oversized dev/test instances to a smaller class; or recycle idle instances.
• Reuse existing databases: restore from an existing snapshot into an available instance class or promote a read-replica instead of creating new instances.
• Create the DB in another Region or another AWS account (if multi-account org available) as a fast temporary workaround.
• Fall back to a lightweight alternative: deploy a single EC2-hosted database or use Aurora Serverless v2 (if compatible) to restore app functionality quickly.
• Communicate: notify stakeholders, push short maintenance windows and feature flags to reduce load.

Longer-term architectural changes (prevent recurrence)

• Implement quota monitoring and automation: export Service Quotas to CloudWatch, alert before thresholds, and automate quota increase requests via API where possible.
• Adopt multi-account pattern and regional distribution to spread resource usage; enforce quotas per environment via SCPs.
• Move to more flexible DB options: Aurora Serverless v2 or Aurora clusters with autoscaling for connections/compute; use RDS Proxy to reduce instance count by multiplexing connections.
• Enforce lifecycle policies: automated cleanup of snapshots, unused instances, and test environments via Infrastructure-as-Code (Terraform) with destroy policies.
• Capacity planning & runbooks: maintain capacity forecasts, playbooks for emergency scale, and regular Trusted Advisor/Cost Explorer audits.
• Governance: tagging, cost/usage chargebacks, Service Quotas owner, and periodic quota review with AWS support.

These steps restore service quickly and reduce future risk by combining monitoring, governance, flexible DB choices, and multi-account/regional distribution.

Approach summary (what I’d deliver)
Design an ASG-backed web tier in AWS (ALB → Auto Scaling Group) using a mix of metric-driven target-tracking, step policies, scheduled + predictive scaling, and warm pools to meet predictable diurnal load while handling flash spikes quickly and cost-effectively.

Metric selection

• Primary: ALB RequestCountPerTarget (RPS per instance) as a target-tracking metric — directly maps load to capacity.
• Secondary: P95 latency (CloudWatch SLO) to trigger step-scaling when latency rises despite RPS.
• Safety: EC2 CPU and host memory (custom CloudWatch) to catch background saturation.
• Business signal: queue depth / checkout rate for flash-sale triggers.

Scaling policy mix

• Target tracking (preferred baseline): keep RPS per instance at target (e.g., 50 RPS/instance) for steady, predictable scaling.
• Step scaling: when latency or CPU crosses thresholds or sudden large RPS jumps occur, use step policies to add N instances depending on breach magnitude for rapid ramp-up.
• Cooldowns: short cooldown (30–60s) for step-scale-ups; longer cooldown (3–5m) for scale-down to avoid oscillation. Use ASG cooldown + metric-specific stabilization.

Scheduled & Predictive scaling

• Scheduled scaling for diurnal patterns (business hours) using known schedules.
• AWS Predictive Scaling (trained on historical traffic) to pre-scale before expected peaks (combines forecast with scheduled).
• Use scheduled/predictive to bring instances into warm pool before anticipated spikes.

Warm pools / pre-warming

• Enable ASG Warm Pools with pre-initialized AMIs or instance snapshots to reduce boot time.
• Use lifecycle hooks to finish app init (pull container image, JIT caches) before adding to ALB.
• For serverless components, use Lambda provisioned concurrency or pre-warmed containers for ECS/Fargate.

Instance sizing & trade-offs

• Fewer larger instances: simpler networking, fewer targets, possibly cheaper per-core, but larger blast radius and slower scale granularity.
• More smaller instances: finer-grained scaling, better fault isolation, faster aggregate concurrency, but higher management/ENI overhead.
• Choose balanced families (e.g., c-type for compute-bound, r-type for memory) and enable mixed ASG (on-demand + spot) with capacity-optimized allocation for cost.

Operational notes

• Add autoscaling alarms with hysteresis and anomaly detection to avoid false triggers.
• Run load tests to validate RPS-per-instance target and warm-pool timing.
• Monitor costs, use rightsizing recommendations, and iterate thresholds based on real SLOs.

Overview (ASG / MIG concepts)
Desired/min/max capacity: desired = target number of instances; min = floor to guarantee availability; max = ceiling to control cost/scale. Scaling policies control how desired changes.

Scaling policy types

• Target-tracking: keep a metric at a target (e.g., maintain avg CPU = 60%). Automatic and simple.
• Step (or simple): scale by fixed increments based on threshold ranges (e.g., +2 if CPU > 70%).
• Scheduled: scale at known times (e.g., weekday mornings).

Health checks & warm-up

• Health checks: load balancer and instance-level checks remove unhealthy instances from ASG and trigger replacements.
• Warm-up (grace period): time to let an instance finish boot/config and register before counting toward metrics; prevents premature scale-ins.

Termination policies
Control which instance to remove (oldest, newest, availability-zone balancing, or custom tags) to preserve stability and cost.

Simple policies (examples)

1. CPU target-tracking (web tier)

• Policy: Target-tracking on ASG keeping average CPU = 55%.
• Config: Target 55%, cooldown/warm-up 300s, min 2, max 20.
• Rationale: Smooth autoscaling, minimal rules.

2. Queue-depth step-scaling (background workers)

• Metric: approximate queue depth (or SQS VisibleMessages) per instance.
• Rules: if queue_depth_per_instance > 50 for 2m -> +2 instances; if < 10 for 5m -> -1 instance. Warm-up 180s; cooldown 300s.
• Rationale: reacts to backlog spikes, avoids oscillation by per-instance normalization and cooldown.

These choices balance responsiveness, stability, and cost; tune thresholds, warm-up, and cooldown with load tests.