Scaling policies (target tracking, step, simple) in AWS - Deep Dive

Overview - Scaling policies (target tracking, step, simple)

What is it?

Scaling policies are rules that automatically adjust the number of resources, like servers, in a cloud system based on demand. They help keep the system running smoothly by adding or removing resources as needed. There are three main types: target tracking, step, and simple scaling policies. Each type decides when and how to change resources differently.

Why it matters

Without scaling policies, cloud systems would either waste money by running too many resources or fail to handle traffic spikes, causing slow or broken services. Scaling policies solve this by balancing cost and performance automatically. This means users get fast, reliable service without manual work or delays.

Where it fits

Before learning scaling policies, you should understand cloud resources like servers and how they can be added or removed. After this, you can learn about advanced auto-scaling features, cost optimization, and monitoring cloud performance.

Mental Model

Core Idea

Scaling policies are automatic rules that adjust cloud resources up or down to keep performance steady and costs low.

Think of it like...

Imagine a restaurant that adds or removes tables based on how many customers arrive. If many people come, the restaurant adds tables quickly; if few come, it removes tables to save space. Scaling policies do the same for cloud servers.

┌───────────────────────────────┐
│          Cloud System          │
│ ┌───────────────┐             │
│ │ Scaling Policy│             │
│ └──────┬────────┘             │
│        │                      │
│        ▼                      │
│ ┌───────────────┐             │
│ │ Resource Pool │◄────────────┤
│ └───────────────┘             │
│        ▲                      │
│        │                      │
│ ┌───────────────┐             │
│ │ Metrics &     │             │
│ │ Monitoring    │             │
│ └───────────────┘             │
└───────────────────────────────┘

Build-Up - 7 Steps

1

Foundation

What is Auto Scaling in Cloud

Concept: Auto scaling means automatically changing the number of servers based on demand.

Cloud systems run applications on servers. Sometimes many users use the app, needing more servers. Sometimes fewer users mean fewer servers are needed. Auto scaling watches usage and adds or removes servers without human help.

Result

The system keeps running well during busy times and saves money during quiet times.

Understanding auto scaling is key because it solves the problem of matching resources to demand without manual work.

2

Foundation

Types of Scaling Policies Overview

Concept: There are three main ways to decide when and how to scale: simple, step, and target tracking.

Simple scaling adds or removes a fixed number of servers after a condition is met. Step scaling changes servers in steps depending on how big the demand change is. Target tracking adjusts servers to keep a metric, like CPU use, near a target value.

Result

You know the basic options to control scaling behavior.

Knowing the types helps choose the right policy for different needs and complexity.

3

Intermediate

How Simple Scaling Works

🤔Before reading on: do you think simple scaling reacts immediately or waits before scaling? Commit to your answer.

Concept: Simple scaling triggers a fixed change after a metric crosses a threshold and a cooldown period.

Simple scaling watches a metric like CPU usage. If CPU goes above 70%, it adds 1 server. Then it waits (cooldown) before scaling again to avoid too many changes. If CPU drops below 30%, it removes 1 server similarly.

Result

Resources increase or decrease in fixed steps after conditions, with pauses to prevent rapid changes.

Understanding cooldowns prevents mistakes like scaling too fast and wasting resources.

4

Intermediate

Step Scaling Adds Flexibility

🤔Before reading on: do you think step scaling changes resources by the same amount every time or varies it? Commit to your answer.

Concept: Step scaling changes the number of servers based on how far a metric is from thresholds, using steps.

If CPU is slightly above 70%, add 1 server. If CPU is much higher, say above 90%, add 3 servers. This way, bigger demand changes cause bigger scaling steps. It also uses cooldowns to avoid rapid changes.

Result

Scaling reacts more smoothly and proportionally to demand changes.

Knowing step scaling helps handle sudden big traffic spikes efficiently.

5

Intermediate

Target Tracking Keeps Metrics Stable

🤔Before reading on: do you think target tracking sets fixed scaling steps or adjusts continuously? Commit to your answer.

Concept: Target tracking automatically adjusts resources to keep a metric near a target value.

You set a target, like 50% CPU usage. The system adds or removes servers to keep CPU close to 50%. It continuously monitors and adjusts, without fixed steps or cooldowns. This is like a thermostat keeping room temperature steady.

Result

The system maintains stable performance with smooth scaling.

Understanding target tracking shows how automation can maintain balance without manual tuning.

6

Advanced

Choosing the Right Scaling Policy

🤔Before reading on: do you think one scaling policy fits all cases or different cases need different policies? Commit to your answer.

Concept: Different workloads and goals require different scaling policies for best results.

Simple scaling is easy but can be slow or cause oscillations. Step scaling handles spikes better but needs careful step setup. Target tracking is best for steady control but may react to noisy metrics. Choosing depends on workload patterns and cost-performance balance.

Result

You can pick the best policy for your cloud application needs.

Knowing policy strengths and weaknesses prevents poor scaling choices that hurt performance or cost.

7

Expert

Advanced Behavior and Pitfalls in Scaling

🤔Before reading on: do you think scaling policies always work perfectly or can cause unexpected issues? Commit to your answer.

Concept: Scaling policies can interact with each other and cloud limits, causing surprises like scaling loops or delays.

For example, multiple policies can trigger conflicting actions. Cooldowns may delay needed scaling. Metrics can be noisy, causing unnecessary scaling. Also, cloud provider limits on max servers affect scaling. Experts monitor and tune policies continuously to avoid these issues.

Result

You understand real-world challenges and how to manage them.

Knowing these pitfalls helps build reliable, cost-effective auto scaling in production.

Under the Hood

Scaling policies work by monitoring cloud resource metrics through a service like CloudWatch. When a metric crosses a threshold, the policy triggers scaling actions via API calls to add or remove instances. Cooldown periods prevent rapid repeated actions. Target tracking uses a control loop that compares current metric values to a target and adjusts capacity smoothly.

Why designed this way?

These policies were designed to automate resource management, reducing manual work and errors. Simple scaling was first for basic needs. Step scaling added finer control for variable demand. Target tracking was introduced to maintain stable performance automatically. Tradeoffs include complexity versus control and responsiveness versus cost.

┌───────────────┐      ┌───────────────┐      ┌───────────────┐
│   Metrics    │─────▶│ Scaling Policy │─────▶│ Scaling Action │
│ (CPU, etc.)  │      │ (Simple/Step/ │      │ (Add/Remove   │
└───────────────┘      │ Target Track) │      │  Instances)   │
                       └───────────────┘      └───────────────┘
                             ▲
                             │
                      ┌───────────────┐
                      │ Cooldown Timer│
                      └───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does simple scaling react instantly to every metric change? Commit to yes or no.

Common Belief: Simple scaling immediately adds or removes servers as soon as a metric crosses a threshold.

Reality: Simple scaling waits for a cooldown period after scaling before reacting again to avoid rapid changes.

Why it matters: Without cooldowns, systems can scale up and down too fast, causing instability and wasted costs.

Quick: Does target tracking always keep the metric exactly at the target? Commit to yes or no.

Common Belief: Target tracking perfectly maintains the metric at the target value all the time.

Reality: Target tracking aims to keep the metric near the target but small fluctuations and delays mean it cannot be exact.

Why it matters: Expecting perfect control can lead to over-tuning and unnecessary complexity.

Quick: Can multiple scaling policies run together without conflicts? Commit to yes or no.

Common Belief: You can safely run multiple scaling policies on the same resource without issues.

Reality: Multiple policies can conflict, causing unpredictable scaling behavior if not carefully coordinated.

Why it matters: Ignoring this can cause scaling loops, resource thrashing, or failure to scale properly.

Quick: Does step scaling always add or remove the same number of servers? Commit to yes or no.

Common Belief: Step scaling changes capacity by a fixed amount regardless of how big the metric change is.

Reality: Step scaling adjusts capacity in different steps depending on how far the metric is from thresholds.

Why it matters: Misunderstanding this can cause wrong step configurations and poor scaling responsiveness.

Expert Zone

1

Step scaling policies can be combined with target tracking to handle both steady-state control and sudden spikes effectively.

2

Cooldown periods are not just delays but essential to prevent oscillations and ensure system stability.

3

Metric selection and aggregation period greatly affect scaling behavior; noisy or delayed metrics can cause poor scaling decisions.

When NOT to use

Avoid simple scaling for highly variable workloads; use step or target tracking instead. Do not rely solely on target tracking if metrics are noisy or delayed; consider combining with step scaling. For very predictable workloads, scheduled scaling might be better than reactive policies.

Production Patterns

In production, teams often use target tracking for baseline scaling and step scaling for handling spikes. They monitor scaling events and tune cooldowns and thresholds continuously. Policies are tested under load to avoid surprises. Multiple policies are coordinated using tags and priority settings.

Connections

Feedback Control Systems

Scaling policies, especially target tracking, work like feedback control loops that adjust outputs based on measured inputs.

Understanding feedback control theory helps grasp how target tracking maintains stable system performance automatically.

Thermostats in HVAC Systems

Target tracking scaling policies are similar to thermostats that keep room temperature near a set point by turning heating or cooling on or off.

This connection shows how cloud scaling uses everyday control principles to balance resource use and performance.

Inventory Management in Retail

Step scaling resembles inventory restocking strategies where order amounts depend on how low stock is, balancing supply and demand.

Recognizing this helps understand why scaling steps vary with demand changes to optimize resource use.

Common Pitfalls

#1 Scaling too quickly without cooldowns causes resource thrashing.

Wrong approach:

Set simple scaling policy to add 1 server whenever CPU > 70% with no cooldown.

Correct approach:

Set simple scaling policy to add 1 server when CPU > 70% with a cooldown period of 300 seconds.

Root cause:

Ignoring cooldowns leads to repeated scaling actions before the system stabilizes.

#2 Using noisy metrics causes unnecessary scaling actions.

Wrong approach:

Configure target tracking on a metric with high short-term fluctuations without smoothing.

Correct approach:

Use averaged or smoothed metrics for target tracking to avoid reacting to noise.

Root cause:

Not accounting for metric noise causes the policy to react to temporary spikes.

#3 Running multiple conflicting scaling policies on the same resource.

Wrong approach:

Attach both step scaling and simple scaling policies with overlapping triggers to the same group without coordination.

Correct approach:

Coordinate policies by using different metrics or priorities, or combine logic into one policy.

Root cause:

Lack of policy coordination causes conflicting scaling commands.

Key Takeaways

Scaling policies automate resource adjustments to balance performance and cost in cloud systems.

Simple, step, and target tracking policies offer different levels of control and responsiveness.

Cooldown periods and metric quality are critical to stable and effective scaling.

Choosing and tuning the right policy depends on workload patterns and business goals.

Expert use involves combining policies, monitoring behavior, and avoiding common pitfalls like conflicts and noisy metrics.