Common Pitfalls and Mistakes to Avoid When Auto-Scaling Cloud Applications

Auto-scaling is one of the most powerful features of cloud infrastructure, allowing businesses to dynamically adjust computing resources in response to changing demands. While this capability can improve application performance, optimise resource utilisation, and reduce costs, it’s not without its challenges. Misconfigurations, incorrect assumptions, and a lack of understanding of auto-scaling mechanisms can lead to inefficiencies, security risks, and unnecessary expenses. This article explores five common pitfalls and mistakes to avoid when auto-scaling cloud applications.

1. Wrong Metrics for Scaling Decisions

Auto-scaling relies on specific metrics to determine when to add or remove resources, but one of the most common mistakes is relying on the wrong metrics. Many cloud providers offer various performance metrics, such as CPU utilisation, memory usage, network traffic, and request rates. However, choosing the incorrect or irrelevant metric can result in poor scaling decisions.

For example, many applications use CPU utilisation as the primary scaling metric. While this can work for compute-heavy applications, it may be less relevant for I/O-bound applications where memory or network utilisation might be a more accurate indicator of resource pressure. Relying on a single metric can lead to a delayed or incorrect response to traffic spikes or drops, affecting performance and potentially causing downtime.

A best practice is to carefully analyse the application’s performance and load characteristics to identify the most suitable metrics. Multi-metric scaling, which combines several key performance indicators (KPIs), can also be a more effective approach, allowing the system to make better-informed decisions.

2. Over-Scaling or Under-Scaling

A common problem with auto-scaling is over-scaling or under-scaling due to poorly defined scaling thresholds and policies. Over-scaling occurs when the system adds too many resources during a traffic spike, leading to wasted resources and higher costs. Under-scaling, on the other hand, results in too few resources being provisioned, causing performance degradation or even service outages.

One of the reasons for this issue is failing to consider the system’s scale-in and scale-out behaviour carefully. For instance, if the scale-out threshold is too low, resources might be added prematurely, and if the scale-in threshold is too aggressive, resources may be removed before they are no longer needed.

Another factor to consider is the scaling cooldown period. The cooldown is the time between successive scaling actions, and configuring this period too short or too long can exacerbate over-scaling or under-scaling. For example, if the cooldown is too short, the system might add and remove resources too frequently, leading to resource thrashing. If it’s too long, the system may fail to respond quickly enough to changing traffic patterns.

Carefully tuning scaling policies, thresholds, and cooldown periods is essential to strike the right balance between scaling responsiveness and resource efficiency.

3. Incompatible Application Architecture

Not all applications are designed to take advantage of auto-scaling, and attempting to scale a poorly architected application can lead to significant challenges. A common mistake is assuming that auto-scaling will work out of the box with any architecture. In reality, an application needs to be designed with scalability in mind for auto-scaling to be effective.

For instance, stateful applications that rely on in-memory sessions or local storage can present difficulties when scaled horizontally (i.e., by adding more instances). If the state cannot be replicated or shared between instances, scaling may lead to inconsistent application behaviour or data loss. Similarly, database scaling can be tricky if the application architecture doesn’t account for database replication, sharding, or connection pooling.

In these cases, refactoring the application to a stateless architecture, where application instances can be easily scaled in and out without affecting the application’s behaviour, is crucial. Leveraging external storage solutions like distributed caches, session stores, or databases that can scale independently of the application is also a common solution.

Here’s an example in Python to demonstrate how a stateless web application can be designed using an external cache (Redis) to manage user sessions across multiple instances:

import redis
from flask import Flask, session

app = Flask(__name__)
app.secret_key = 'supersecretkey'
cache = redis.StrictRedis(host='localhost', port=6379, db=0)

@app.route('/set_session/<name>')
def set_session(name):
    session['user'] = name
    cache.set(session.sid, name)  # Store session in Redis
    return f'Session set for {name}'

@app.route('/get_session')
def get_session():
    user = cache.get(session.sid)  # Retrieve session from Redis
    return f'Session user is {user.decode("utf-8")}'

This code is a simple Flask-based web application that uses Redis to store and retrieve user session data. The web application consists of two routes (or URLs):

/set_session/<name> — sets up a session for the user and stores it in Redis.
/get_session — retrieves the user’s session data from Redis and displays it.
This appendix shows how you can implement storing sessions in a centralised Redis database, which is especially useful in distributed systems, for example when the application is scaling (when multiple servers are running).

4. Overlooking Security Risks

While auto-scaling offers flexibility, it can also introduce security vulnerabilities if not properly managed. One of the most common mistakes is failing to secure new instances that are spun up automatically. Without appropriate security configurations and monitoring, these instances may be vulnerable to attacks, unauthorised access, or misconfigurations.

For example, automatically provisioned instances might not inherit all the necessary security policies, firewall rules, or encryption settings. This is particularly risky in environments with sensitive data or critical workloads. Additionally, scaling might involve different regions or availability zones, where security policies might not be consistent.

It’s essential to automate security as part of the auto-scaling process. This includes ensuring that new instances are automatically configured with the necessary security settings, such as patch management, firewalls, and access controls. Integrating security scanning and monitoring tools to detect and mitigate potential vulnerabilities is also important.

5. Poor Cost Management

One of the primary reasons organisations adopt auto-scaling is to optimise costs by paying only for the resources they use. However, auto-scaling can lead to unexpectedly high costs if not managed properly. Over-scaling, as discussed earlier, can result in more instances being spun up than necessary, leading to higher charges. Additionally, failing to monitor idle resources, orphaned volumes, or forgotten instances can add unnecessary costs.

To avoid this, it’s critical to continuously monitor usage and costs using cloud provider tools or third-party cost management platforms. Setting budget alerts and reviewing resource utilisation regularly helps ensure that scaling policies remain efficient and cost-effective.

In some cases, combining auto-scaling with reserved instances or spot instances can provide a more cost-efficient strategy. For example, predictable workloads can be handled using reserved instances at a lower cost, while unpredictable spikes can be managed using auto-scaling with spot instances, which are typically cheaper than on-demand instances.

Conclusion

Auto-scaling is a powerful tool for managing cloud applications, but it’s not without its challenges. By avoiding common pitfalls such as relying on the wrong metrics, over-scaling or under-scaling, using incompatible application architectures, overlooking security risks, and failing to manage costs effectively, organisations can leverage auto-scaling to optimise their cloud resources. Careful planning, monitoring, and a deep understanding of application performance are key to making the most of auto-scaling capabilities while avoiding costly mistakes.