Scalability is a system’s ability to swiftly enlarge or reduce the power or size of computing, storage, or networking infrastructure. With the evolution of the requirements and resource demands of applications, scaling storage infrastructure provides a means of adapting to resource demands, optimizing costs, and improving the operations team efficiency.
Scale Up vs Scale Out
Scaling up (vertical scaling) and scaling out (horizontal scaling) are key methods organizations use to add capacity to their infrastructure. To an end user, these two concepts may seem to perform the same function. However, they each handle specific needs and solve specific capacity issues for the system’s infrastructure in different ways.
Scaling up is adding further resources, like hard drives and memory, to increase the computing capacity of physical servers. Whereas scaling out is adding more servers to your architecture to spread the workload across more machines.
Scaling up storage infrastructure aims to add resources supporting an application to improve or maintain ample performance. Both virtual and hardware resources can be scaled up. In the context of hardware, it may be as straightforward as using a larger hard drive to greatly increase storage capacity. Note, though, scaling up does not necessarily require changes to your system architecture.
Scaling up infrastructure is viable until individual components are impossible to scale anymore — making this a rather short-term solution.
When to Scale Up Infrastructure
- When there’s a performance impact: A good indicator of when to scale up is when your workloads start reaching performance limits, resulting in increased latency and performance bottlenecks caused by I/O and CPU capacity.
- When storage optimization does not work: Whenever the effectiveness of optimization solutions for performance and capacity diminishes, it may be time to scale up.
- Relative speed: Replacing a resource, such as a single processor with a dual processor, means that the throughput of the CPU is doubled. The same can be done to resources such as dynamic random access memory (DRAM) to improve dynamic memory performance.
- Simplicity: Increasing the size of an existing system means that network connectivity and software configuration do not change. As a result, the time and effort saved ensure the scaling process is much more straightforward compared to scaling out architecture.
- Cost-effectiveness: A scale-up approach is cheaper compared to scaling out, as networking hardware and licensing cost much less. Additionally, operational costs such as cooling are lower with scale-up architectures.
- Limited energy consumption: As less physical equipment is needed in comparison to scaling out, the overall energy consumption related to scaling up is significantly lessened.
- Latency: Introducing higher capacity machines may not guarantee that a workload runs faster. Latency may be introduced in scale-up architecture for a use case such as video processing, which in turn may lead to worse performance.
- Labor and risks: Upgrading the system can be cumbersome, as you may, for instance, have to copy data to a new server. Switchover to a new server may result in downtime and poses a risk of data loss during the process.
- Aging hardware: The constraints of aging equipment lead to diminishing effectiveness and efficiency with time. Backup and recovery times are examples of functionality that is negatively impacted by diminishing performance and capacity.
Scale-out infrastructure replaces hardware to scale functionality, performance, and capacity. Scaling out addresses some of the limitations of scale-up infrastructure, as it is generally more efficient and effective. Furthermore, scaling out using the cloud ensures you do not have to buy new hardware whenever you want to upgrade your system.
While scaling out allows you to replicate resources or services, one of its key differentiators is fluid resource scaling. This allows you to respond to varying demand quickly and effectively.
When to Scale Out Infrastructure
- When you need a long-term scaling strategy: The incremental nature of scaling out allows you to scale your infrastructure for expected, long-term data growth. Components can be added or removed depending on your goals.
- When upgrades need to be flexible: Scaling out avoids the limitations of depreciating technology, as well as vendor lock-in for specific hardware technologies.
- When storage workloads need to be distributed: Scaling out is perfect for use cases that require workloads to be distributed across several storage nodes.
- Embraces newer server technologies: Because the architecture is not constrained by older hardware, scale-out infrastructure is not affected by capacity and performance issues as much as scale-up infrastructure.
- Adaptability to demand changes: Scale-out architecture makes it easier to adapt to changes in demand since services and hardware can be removed or added to satisfy demand needs. This also makes it easy to carry out resource scaling.
- Cost management: Scaling out follows an incremental model, which makes costs more predictable. Furthermore, such a model allows you to pay for the resources required as you need them.
- Limited rack space: Scale-out infrastructure poses the risk of running out of rack space. Theoretically, rack space can get to a point where it cannot support increasing demand, showing that scaling out is not always the approach to handle greater demand.
- Increased operational costs: The introduction of more server resources introduces additional costs, such as licensing, cooling, and power.
- Higher upfront costs: Setting up a scale-out system requires a sizable investment, as you’re not just upgrading existing infrastructure.
Scale Up or Scale Out?
Notably, both scale-up and scale-out approaches have different purposes in data center infrastructures. However, the right approach for your business depends on factors such as current performance, cost-effectiveness, and your challenges, goals, and use case.