In the Data Age 2025 report, worldwide data is expected to grow 61% to 175 zettabytes by 2025. The enterprise sector, in particular, generates more than 30% each year. To be ready for a digital future, consider the scaling strategy of data infrastructure beforehand.
Scale-up and scale-out are the main ways to add capacity to your infrastructure. While both solutions perform the same function and the end-user perspective, they solve different capacity issues and needs of the system’s infrastructure.
Let’s take a closer look at scale-up and scale-out architecture, how they differ from one another, and factors to consider when choosing a scaling model.
When running massive data centers, you may face the need to increase your machine’s capacity to run larger workloads. In this case, apply vertical scaling or scale-up.
Scale-up is a simple method of increasing your computing capacity by adding additional resources such as a central processing unit (CPU) and Dynamic random-access memory (DRAM) to on-premises servers or improving the performance of your disk by changing it to a faster one. To implement this strategy, you do not need to make any changes to your system’s architecture. Cloud computing providers, such as Microsoft Azure and Google Cloud, allow you to scale-up your virtual machine with a few clicks.
Scale-out is another way to add capacity to your architecture. Instead of buying one powerful machine, horizontal scaling means adding simple servers that run a distributed computing model.
This approach is popular among companies such as Amazon, Uber, and Netflix, that want to provide customers all over the world with the same user experience.
Before applying one strategy to your data center, consider the following differences.
While scale-up allows you to increase the performance of existing hardware, as well as extending its lifecycle, scale-out enables you to take advantage of newer server technologies in running fault tolerance, system monitoring, and minimize downtime.
With scale-up, pay less for licensing and network equipment. Scale-out means higher costs for power, licensing, and networking equipment.
When applying scale-up, consider future upgrades and software support that could be limited by vendor lock-in. Scale-out allows you to take advantage of the latest memory, storage, and processor technology.
Scale-up does not suit a long-term strategy since the capacity of the servers will be upgraded to the threshold of their performance. The scale-out approach allows you to scale the architecture in the long-term.
With scale-up, you receive a single storage system management, while scale-out includes aggregated management capability.
Scale-up storage is more straightforward, compared to a scale-out system with numerous elements to manage. Thus, to run a scale-up model, you will need server monitoring tools. Stackify’s Application Performance Management tool, Retrace, helps maintain a healthy application. In addition to basic server metrics, performance metrics allow you to monitor your entire stack and provide code level insights into errors and performance issues.
Both scale-up and scale-out solve different issues of data centers and should be used for different cases.
In this article, we explained what scale-out and scale-up means and compared these approaches and use cases.
To manage data growth, have a solid strategy for infrastructure scalability. To provide the capacity and performance that workloads require, apply either a scale-up or scale-down model. Your choice should be based on the type of workload and your expansion needs.
However, it is a well-known practice to use both approaches to allocate your network resources. For instance, you can use scale-up to handle massive traffic while running the main data center on scale-up machines.
Stackify’s APM tool, Retrace, supports monitoring for both scale-up and scale-out environments with packages to scale either up or out as needed.