Change is the only constant and growth is the only motive that shapes the future of any business. Having said that, provisioning resources or decommissioning non-essential systems from the infrastructure is inevitable, if a business has to cope with demand deflections and optimize cost. Here, scalability takes center stage. According to Gartner, Inc., CIOs must create resilient technical foundations whose scalability will enable free cash flow for digital investments. However, being deft in scaling the infrastructure to meet evolving demands while optimizing cost needs integration with cloud-native services.
Broadly, there are two features to keep in mind when creating a scaling strategy, namely,
- Predictive Scaling, where actions taken on instances are driven through a predictive study of traffic patterns. The objective is to bring the scaling index closer to the target value, howsoever possible.
- Dynamic Scaling, where the instance count is automatically changed to be on par with information generated from monitoring and management service. The objective is to have enough capacity for the infrastructure to maintain usage at the target value.
Why is cloud computing highly touted for scalability? Irrespective of the industry sector an organization is in, there are certain complexities involved when it comes to scaling up or scaling down resources. While doing so, choosing between vertical scaling and horizontal scaling becomes an important decision to make.
What is Vertical Scaling?
Vertical scaling is the up-scaling method where the computational capacity of infrastructure is uplifted to meet urgent or new demands. Also known as scale-up, vertical scaling gives an organization the capability to retain the infrastructure resources in the present logical unit. It expands the infrastructure’s processability, storage power, and network capacity as intended.
Fundamentally, vertical scaling equips with the ability to ramp up the existing software or hardware capacity. However, it is necessary that an organization only expands its capacity to the server’s limits. Up-scaling increases a single system’s capacity and if there arehuge volumes of requests, one serverwill not be able to take the load as the hardware has capacity limitations. Such a scenario necessitates choosing horizontal scaling.
What is Horizontal Scaling?
Horizontal scaling is associated with the infrastructure’s devices and computational power. Also known as scaling out, horizontal scaling brings an additional number of devices and dispenses the current computational power across the additional and existing devices. This means that the number of devices will keep increasing while there is no spike in computational power. Such a distribution differentiates scaling out from scaling up.
Horizontal scaling provides organization scalability and reliability to have moreredundancy. Itis apreferred scaling method consideringdistributed architectures. In the case where dividing into multiple servers is essential, an organization must consider if it has astate. If it hasstateless services, horizontal scaling is the best practice to adopt. Different services can be used along with aload balancerto split the traffic into multiple servers.
Key Differences between Horizontal Scaling and Vertical Scaling
| Differentiating Factors | Horizontal Scaling | Vertical Scaling |
| Cost | Exponentially greater than vertical scaling. Involves numerous physical systems distributed among multiple data centers across different availability zones. | An economical alternative to scaling out in terms of management, maintenance, and operations. |
| Downtime | Downtime rarely occurs, as multiple systems are simultaneously processing the requests. | More prone to downtime, as one system processes the workloads. |
| Data Transmission | Happens using network communication, can be slow, and is susceptible to failure. | Hinges on inter-process communication, is uncomplicated, and occurs at speed. |
| Features | Clustering, distributed file systems, and strong load balancing. | Easy implementation and maintenance, minimal incompatibility risk, and apt for lower data throughput needs. |
| Performance | High performance due to distributed programming that sources power from multiple systems. | Consistent performance as the system has multiple cores, allowing optimization of storage capacity and data processing. |
| Execution | Consecutive blocks of logic are broken down into smaller units, allowing concurrent data execution across systems. | Eliminates repeated modifications in the logic process. Uses advanced specifications to execute on a system with the same code. |
Choosing between Scaling Up and Scaling Out
As the demand soars, the need to maintain capacity, accessibility, and uptime becomes instantaneous against the backdrop of load spikes. In such a case, understanding whether vertical scaling or horizontal scaling is the best option becomes challenging. It involves considering several factors while deciding on the right methodology, as listed below.
- Identify business goals and technical requirements to meet those goals. Then, consult with every stakeholder to gauge business areas that would drive value with cloud scaling.
- Engage with in-house and external technical experts and leverage their knowledge for critical decision-making associated with the business processes and application architecture. Application performance needs and technical characteristics such as response time, system throughput, and availability, can help an organization better understand its scaling requirements.
- Another factor to consider is developing prototypes. This helps understand the gist of each scaling model when in practice. Test the prototypes for their cost-effectiveness, reliability, productivity, and fault tolerance, keeping the findings in sync with the organization’s short, medium, and long-term scaling objectives.
Every organization’s use case varies and needs a bespoke set of questions to get started with choosing the right scaling method.A viable solution is working with stakeholders from different departmentsand coming up with specific questions, and answering those questions accurately. Once the objectives are defined, technology experts can help visualize if the chosen scaling model would actually work for the application architecture.
If none of the scaling methods fit a particular organization, they can use a combination of vertical scaling and horizontal scaling to meet their needs. Such an approach integrates vertical scaling’s speed and reliability with horizontal scaling’s robust business continuity and infinite scalability.
Things to Remember
- The deployment of new components or hardware for the infrastructure is not the most feasible solution.
- A sub-optimal environment with inconsistencies and bottlenecks is the result of integrating more systems without knowing the technical requirements of the application architecture.
- If it is the first time, then vertical scaling is the preferred option to increase the infrastructure capacity or modify its system specifications. When the workloads increase significantly, the time is right to adopt horizontal scaling.
To Sum Up
Tapping new opportunities with a traditional infrastructure is challenging. Whether extending the infrastructure, modernizing the data warehouse, streaming new content, or launching the applications with faster go-to-market, every new business prospect is lucratively driven by the cloud. Cloud computing is here to stay as an essential technology for business transformation, promoting innovation, saving costs, and building resilient business models.
Organizations are increasingly adopting cloud environments to operate their infrastructure at scale and add value to the business. Third-party vendors, cloud consulting partners, and major cloud service providers are all focused to provide cutting-edge scalability solutions, combining the existing infrastructure with custom solutions, or outsourcing the scalability requirements entirely. What seems to be an easy choice, scalability involves tedious decision-making. Organizations must always look for both, vertical scaling and horizontal scaling, to reap the best of both worlds.
Frequently Asked Questions (FAQs)
| What is an example of horizontal scaling? |
| An example of horizontal scaling is adding more servers to distribute the load of a web application, allowing for increased capacity and performance without upgrading individual server hardware. |
| What is an example of vertical scaling? |
| An example of vertical scaling is upgrading a server’s CPU, RAM, or storage capacity to handle increased workload demands for a single instance. |
| Is horizontal or vertical scaling cheaper? |
| Generally, vertical scaling is cheaper upfront. You upgrade one existing machine instead of buying and managing multiple new ones. |
| Is Kubernetes vertical or horizontal scaling? |
| Kubernetes supports both horizontal and vertical scaling. It can scale applications horizontally (adding more pods) or vertically (allocating more resources to existing pods). |
| Which is better vertical or horizontal scaling |
| The choice between vertical and horizontal scaling depends on the specific needs and constraints of the application. Vertical scaling is simpler but limited, while horizontal scaling offers more flexibility but can be more complex to manage. |
