If you’ve explored the advantages of your cloud computing platforms, you’ve likely come across the autoscaling features. Although these differ for each provider, the idea is that they will detect if you need more memory, CPUs, or storage and automatically allocate it for you ahead of need. This allows your applications to perform at a specific level, no matter if workloads increase or decrease.
Usually, there are a couple of options, including driving scaling by the application, which is not fully automated since you have to define and invoke from the application. Or you can turn on a system that allocates different resources through entirely automated mechanisms.
The public cloud providers offer this service, or you can use other on-premises systems (using your hardware) or a managed services provider so that you’re all-inclusive. What are the trade-offs of these types of mechanisms? Let’s go through a few.
Complexity and ops challenges
Implementing and managing an autoscaling system with careful configuration and monitoring to ensure the system responds appropriately to changing demands first requires organizations to define proper scaling policies, including thresholds and rules. It’s not as easy as it sounds.
You’ll learn quickly that this can be complex and time-consuming and require specialized expertise. Most enterprises don’t have this level of knowledge and often get autoscaling configurations wrong. This means systems use too many resources and cost more than they should. Also, continuous monitoring and constant tuning are essential to avoid underprovisioning or overprovisioning.
Cost implications
Autoscaling can reduce costs by optimizing resource utilization, but savings are not guaranteed. I have seen autoscaling systems lead to unexpected cost increases. For example, rapid and frequent scaling operations can generate additional charges that are often unexpected. This will undoubtedly happen if resources are not managed efficiently.
I’ve seen unpredictable workload patterns or sudden spikes in demand trigger autoscaling processes. This results in more instances or resources provisioned, but also a potentially enormous cloud bill. The only way to work around this is to carefully analyze and forecast workload patterns to balance scalability and cost-effectiveness.
Performance variability
Autoscaling introduces performance variability due to the dynamic nature of resource allocation that provides the automation and responsiveness that admins seek. Although the cloud provider’s infrastructure is designed to minimize these effects, there are potential latency or disruption behaviors during scaling operations.
Follow me here. Scaling up or down involves provisioning or deprovisioning instances. This is not free. It also takes time and may impact overall system performance. This variability has been known to affect application responsiveness and user experience. What hurts the most is that you’re paying more for this.
Application complexity
Certain applications don’t work well with autoscaling systems. Legacy or monolithic applications that rely on static configurations or have complex interdependencies may not perform very well with autoscaling systems. Of course, there is a fix, normally rewriting a portion of the entire application to leverage autoscaling more efficiently.
Changing such applications to scale effectively may require extensive refactoring or a complete redesign. This adds development time, costs, and risk. Also, applications requiring shared states or maintaining session affinity can encounter difficulties when scaling across multiple instances.
More good than bad?
Most things in the cloud have trade-offs, and autoscaling is no different. Consider what I’ve explained when choosing an autoscaling system in the cloud. There will always be a benefit and a cost, and you must strike the right balance.
I keep seeing too many IT shops just accept that autoscaling systems are right for them and overlook some of the downsides. They are usually the ones who also complain about a massive cloud bill they did not see coming. This type of mistake is easily avoidable.
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