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Community Blog Scheduled Scaling vs. Dynamic Scaling: What Is the Best Choice?

Scheduled Scaling vs. Dynamic Scaling: What Is the Best Choice?

This article explains the differences between scheduled scaling and dynamic scaling.

By Alain Francois

Cloud scaling provides an approach to increase or decrease the compute, memory, or networking resources that have been allocated as traffic spikes and patterns demand. You are limited by your hardware resources in the traditional dedicated hosting environment. Before cloud computing, it was very difficult to scale a website, let alone figure out a way to scale a server setup automatically. Cloud computing revolutionizes the way computer resources are allocated, making it possible to build a fully scalable server setup on the cloud.

Auto Scaling: Scaling Up and Scaling Out

Scaling generally refers to adding or reducing the number of active instances being leveraged against your workload's resource demands.

  • Scaling up is the process of resizing a server to give it supplemental or fewer CPUs, memory, or network capacity. It is also called vertical scaling because it keeps your existing infrastructure and adds computing power. It allows data to live on a single node, and scaling spreads the load through CPU and RAM resources for your machines.
  • Scaling out splits the workload across multiple servers that work in parallel. This means you can add more machines to the resource pool. It is also called horizontal scaling since you scale back to the existing infrastructure. It requires breaking a sequential piece of logic into smaller pieces so they can be executed in parallel across multiple machines.
  • Auto Scaling can be seen as the automation of the scaling up and scaling out processes. It is a management service that adjusts the number of elastic computing resources automatically based on your business demands and policies. When business loads increase, Auto Scaling adds ECS instances automatically to ensure sufficient computing capabilities. When business loads decrease, Auto Scaling removes ECS instances automatically to save costs. It is suitable for applications with fluctuating or stable business loads.

Auto Scaling is a critical aspect of modern cloud computing deployments. The goal is to allow users to only pay for what they need. Auto Scaling works in a variety of ways depending on the platform and resources a business uses. There are also different types of Auto Scaling.

Scheduled Scaling

With a scheduled scaling, a user can define a time horizon specifying when more resources will be added. This means you can create a scheduled task to execute a specific scaling rule at a specified time. It is like creating scheduled actions based on predictable load changes. This way, your scheduled actions are performed automatically as a function of date and time.

Dynamic Scaling

With dynamic scaling, resources scale up and down as traffic spikes occur. This means you can create an event-triggered task based on a Cloud Monitor performance metric, such as the CPU utilization. When the metric data of a scaling group meets the specified alert conditions, an alert is triggered to execute your specified scaling rule. This is like real-time monitoring of resources.

What Is the Best Choice?

You should analyze your needs and workloads to determine whether dynamic or scheduled scaling suits your situation. Dynamic scaling removes the burden of launching instances manually by yourself and removing them when they are no longer needed. Scheduled scaling allows resources to scale up as demand ramps up since they can be pre-provisioned in anticipation.

Conclusion

Alibaba Cloud Auto Scaling creates and releases ECS instances automatically based on predefined rules to scale services to match demand. Sometimes, the change in load is unpredictable, and other times, we can predict it in advance. In any of these cases, we have a simple problem that we need to solve; we need to scale our system to meet the demand regardless of whether we can plan for it in advance or not. Alibaba Cloud offers additional scaling modes that can also suit your needs.

Computing Auto Scaling Scheduled Scaling Dynamic Scaling Cloud Scaling Scaling Mode
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