Scale up vs. scale out in cloud computing
Scalability is the system's capacity to adjust the size of the storage and computing infrastructure based on the changing demands of the application. Scaling helps to optimize costs by adjusting to the resource demands and boosting the overall system efficiency.
Scale up approach
Scaling up refers to adding more resources that can help the application improve its performance without changing the system architecture. It can be done until it is impossible to add any more components. Therefore, it is better as a short-term architecture.
When to scale up?
There are different scenarios when there is a need to scale up the infrastructure to achieve better results. Let's look at a few reasons leading up to the need to scale up the system.
Let's take an example of a company that works in machine learning and has developed large models requiring
Strengths
Scaling up is a cost-effective solution that helps to improve the overall system speed and performance by using limited energy to upgrade.
Cost-effective | It is comparatively cheaper as the hardware, licensing, and operational costs are less in the scale-up infrastructure. |
Improved speed | It allows replacing resources with more efficient ones, e.g., updating a single processor with a dual processor to double the throughput of the CPU. |
Limited energy consumption | It requires less physical equipment than scaling out; hence the overall energy consumption is less. |
Weaknesses
Scaling up does, however, has a few limitations that require the stakeholders to compare them with their requirements before implementing scale-up. It can be more prone to data loss and can not bring much of a positive impact on the efficiency in case of aging hardware and latency issues.
Prone to data loss | It is more vulnerable to data loss, as switching to a new server may result in downtime and data risks. |
Aging hardware | The equipment ages with time, and even if we scale up, it can diminish effectiveness and efficiency with time. |
Latency | Introducing higher-capacity machines may result in higher latency, e.g., in video processing, leading to worse performance. |
Scale-out approach
Scaling out refers to extending the overall infrastructure by adding more resources to the system than increasing the capacity of the existing resources. It grows the infrastructure with time and has enough capacity for scaling up, so can be considered as a long-term solution.
When to scale out
There are different scenarios when there is a need to scale out the infrastructure to achieve better results. Let's look at a few reasons leading up to the need to scale out the system.
Let's take an example of a streaming service like Netflix that provides services globally to millions of users. Therefore, providing services from one server can be difficult because of the continuous traffic and workloads. To cope, it can install servers in various geographical locations to divide the traffic and provide efficient services to the users.
Strengths
By scaling out facilitates, the on-demand resource payment helps manage the cost better and is more adaptable to changes and new technology servers integrated into the infrastructure.
Better cost management | It follows an incremental model, which allows resource payment as per need, which makes costs more predictable. |
Adjustable with new technologies | It can add new hardware to the infrastructures, therefore, is not affected by capacity and performance issues. |
Adaptability to change | It is easier to remove or add the services and hardware according to the demand, which makes it easy to carry out resource scaling. |
Weaknesses
Scaling out does, however, has a few limitations, mostly in the finances since it is very cost-intensive to set up the infrastructure and consequently increases the operational cost too. Moreover, it can also face rack space limitations that can hinder the process.
Higher upfront costs | It requires a new setup, not just existing infrastructure upgradation; therefore, a significant investment is required. |
Limited rack space | Though it supports infrastructure expansion consequently leads to insufficient rack space to support increasing demand. |
Increased operational costs | The increase in servers and resources consequently adds up to additional costs, like licensing, cooling, and power. |
Summary
Scaling up and scaling out are important approaches that help improve the system's performance and maximize the infrastructure's storage capacity. Scaling up is preferred when a single system is sufficient to meet the requirements, whereas scaling out is preferred when the servers are installed at different locations. Scaling out is mostly focused on handling the traffic and expanding the infrastructure. Another optimal solution is implementing a hybrid approach that can expand the capacity to serve a larger area and enhance the server's strength to cope with the hardware-intensive requirements.
Test your understanding
Match the correct and incorrect statements to true and false, respectively, and evaluate how better you can understand the difference between the two approaches.
Scaling up is more cost-effective than scaling out.
False
Scaling up is more cost-intensive than scaling out.
True
Scaling out is less adaptable to on-demand changes.
Scaling up can be less effective due to the aging hardware.
