Capacity Planning and Cost Optimization in System Design - Tutorials

Capacity planning and cost optimization are critical components of modern system design. They ensure that applications can handle expected workloads while keeping operational expenses under control. This tutorial walks you through the fundamentals, practical techniques, and tools you need to design scalable, cost‑effective systems.

Why Capacity Planning Matters

A well‑executed capacity plan helps you answer three core questions:

Will the system meet performance SLAs under peak load?
How much infrastructure is required to sustain growth?
What is the optimal trade‑off between performance and cost?

Neglecting these questions can lead to outages, degraded user experience, and runaway cloud bills.

Key Concepts

1. Workload Characterization

Understanding the nature of your workload is the first step. Typical dimensions include:

Request rate (RPS/QPS): Number of requests per second.
Data volume: Size of input/output per request.
Concurrency: Simultaneous active sessions.
Latency targets: Desired response time percentiles (e.g., 95th‑percentile ≤ 200 ms).

2. Resource Metrics

Measure CPU, memory, network, and storage usage at the granularity of a single request. Tools such as perf, htop, and cloud‑native monitoring (e.g., AWS CloudWatch, Prometheus) are indispensable.

3. Scaling Strategies

Vertical scaling – increase resources of a single node.
Horizontal scaling – add more nodes behind a load balancer.
Auto‑scaling – dynamically adjust node count based on metrics.
Sharding / Partitioning – distribute data across multiple logical buckets.

Step‑by‑Step Capacity Planning Process

Define business and performance objectives.
Collect baseline metrics under representative load.
Model resource consumption per unit of work.
Forecast future demand (growth rate, seasonality).
Select scaling strategy and sizing rules.
Validate with load‑testing and cost simulation.
Implement monitoring, alerts, and cost‑governance policies.

Mathematical Modeling Example

Assume a microservice processes R requests per second, each consuming c_cpu CPU cores and c_mem GB of memory. The required number of instances N can be estimated as:

python

N = ceil( (R * c_cpu) / cpu_per_instance )
# where cpu_per_instance is the CPU allocation per VM/container

Similarly, total monthly cost on a cloud provider can be approximated with:

python

monthly_cost = N * instance_hourly_rate * hours_per_month
# Add storage, data‑transfer, and licensing fees as needed

Cost Optimization Techniques

Right‑Sizing Instances

Choose the smallest instance type that satisfies cpu_per_instance and mem_per_instance. Spot or pre‑emptible instances can reduce costs by 60‑80 % for fault‑tolerant workloads.

Autoscaling Policies

Define thresholds that trigger scaling events. A typical policy uses both CPU utilization and request queue length to avoid oscillations.

json

{
  "AutoScalingGroupName": "web-asg",
  "MinSize": 2,
  "MaxSize": 20,
  "TargetTrackingConfiguration": {
    "PredefinedMetricSpecification": {
      "PredefinedMetricType": "ASGAverageCPUUtilization"
    },
    "TargetValue": 55.0
  }
}

Caching Layers

Introduce in‑memory caches (e.g., Redis, Memcached) to offload read traffic from databases. Cache‑hit ratios above 80 % can cut database cost dramatically.

Data Partitioning & Archival

Move cold data to cheaper storage classes (e.g., AWS S3 Glacier) and partition hot data to reduce scan size.

⚠ Warning: Never disable monitoring when using aggressive cost‑saving measures such as spot instances; lack of visibility can mask performance regressions.

💡 Tip: Leverage cloud provider cost‑explorer APIs to automate daily budget alerts.

📝 Note: Capacity planning is an iterative activity. Re‑evaluate assumptions after each major release or traffic pattern shift.

Monitoring & Alerting Dashboard Example

Sample Load‑Testing Script (k6)

javascript

import http from 'k6/http';
import { sleep, check } from 'k6';

export const options = {
  stages: [
    { duration: '5m', target: 200 }, // ramp‑up to 200 RPS
    { duration: '10m', target: 200 }, // sustain
    { duration: '5m', target: 0 },   // ramp‑down
  ],
};

export default function () {
  let res = http.get('https://api.example.com/v1/resource');
  check(res, { 'status is 200': (r) => r.status === 200 });
  sleep(1);
}

FAQs

Q: How often should I revisit my capacity plan?
A: At a minimum quarterly, after any major product launch, and whenever you observe sustained traffic growth >10 % month‑over‑month.

Q: Is vertical scaling ever preferable to horizontal scaling?
A: Vertical scaling can be simpler for stateful services that cannot be easily partitioned, but it hits hard limits faster and offers less resilience than horizontal scaling.

Q: Can I rely solely on cloud provider pricing calculators?
A: Pricing calculators give a rough estimate. Always validate with real‑world usage data and include hidden costs such as data egress, API calls, and support tiers.

Quick Quiz

Q. What is the primary metric to trigger a scale‑out event in a CPU‑based auto‑scaling policy?

Memory usage
CPU utilization
Disk I/O
Network latency

Answer: CPU utilization
CPU utilization directly reflects processing load; crossing the target threshold signals that more compute capacity is needed.

Q. Which storage class typically offers the lowest cost for archival data on AWS?

S3 Standard
S3 Intelligent‑Tiering
S3 Glacier Deep Archive
EFS

Answer: S3 Glacier Deep Archive
Glacier Deep Archive is designed for data accessed less than once a year and provides the cheapest per‑GB price among AWS storage options.

References & Further Reading

References

📘 Summary: Capacity planning combines workload analysis, resource modeling, and cost estimation to build systems that scale efficiently. By following a disciplined process—defining objectives, measuring baseline metrics, forecasting demand, choosing appropriate scaling strategies, and continuously monitoring—you can meet performance targets while keeping expenses under control.

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