Definition

What is cloud scalability?

Scalability in cloud computing refers to the ability of a cloud environment to adapt to increasing workloads over time without a noticeable change in performance or reliability. Ideally, a scalable cloud environment should also provide predictable and consistent costs as workloads change.

Scalability is important because an organization's cloud workload is not constant. Ebbs and flows of business, research and development projects, and changes in product offerings can increase or decrease the need for cloud-based processing. A cloud environment needs to keep pace as a business grows.

Some might confuse cloud scalability with cloud elasticity, which refers to short-term, real-time responses to sudden changes in cloud processing demand, such as an unexpected spike in traffic. Cloud scalability addresses long-term, planned growth in demand, whether that's from increased customer traffic, regional expansion or an acquisition. Scalability and elasticity, however, go hand in hand when mapping out a long-term cloud plan.

Why is cloud scalable?

The cloud is inherently scalable because of its infrastructure: data centers with massive numbers of servers controlled by software that manages workload demand, security, data storage and segmentation, and more. A cloud host provider might have thousands of customers, each with constantly varying workload demand. In day-to-day operations, software creates virtual machines for each customer account within the host data center, and each cloud customer's data is distributed across those VMs. If a customer needs more storage, the host system creates more VMs to handle them. This is more of an elasticity function.

To meet long-term, planned growth, a cloud host provider can add nodes, which are discrete compute units within the cloud environment. Nodes work together in a group known as a cluster to serve the needs of a cloud account.

Cloud infrastructure can also scale in other ways. For example, a cloud customer might require more powerful servers or the use of multiple data centers once its processing demands pass a certain threshold. How that happens depends on the type of cloud environment the customer uses. These are the possible types of cloud environments:

  • Public cloud is managed by a third-party cloud service provider (CSP), which delivers its services over the internet. The customer shares computing resources with other CSP customers, so resources added for scaling purposes are virtualized.
  • Private cloud tends to be run by the organization that uses it, which gives it more control over how it adds resources as the business grows.
  • Hybrid cloud is a combination of public and private cloud, and it provides more flexibility in how to allocate and add resources.
  • Multi-cloud uses the services of multiple CSPs. Organizations use a multi-cloud approach to avoid vendor lock-in and to increase cloud reliability should a disruption occur at one CSP. A multi-cloud approach also provides more flexibility for scaling a cloud environment.

What are the types of cloud scalability?

There are three types of cloud scalability to consider for your cloud infrastructure:

  1. Vertical scaling, also known as scaling up, refers to adding more resources to existing physical hardware, typically central processing units, RAM or storage. This optimization of existing cloud servers is the most cost-effective way to scale, assuming that processing demands don't exceed what optimization can provide.
  2. Horizontal scaling, also known as scaling out, refers to adding more instances of a resource, such as servers, to the cloud environment. This method distributes workloads across more resources and is often used to address applications with high processing demands, such as a website with heavy traffic or data analytics on large data sets.
  3. Diagonal scaling, also known as hybrid scaling, employs both vertical and horizontal scaling methods. Its primary advantage is flexibility. For example, horizontal scaling might not be able to bring new instances online fast enough to meet a spike in processing demand, whereas vertical scaling can respond faster. Diagonal scaling is most often used in applications that require a high level of processing power, network bandwidth and storage.

Benefits of cloud scalability

The benefits of cloud scalability are best understood by comparing them to scaling an on-premises network. These are the primary factors to compare:

  • Cost. You want the maximum return on any IT investment, and cost predictability is part of that. Planning long-term expansion of an on-premises network requires consideration of additional staffing, outside consultants, hardware, service contracts, communications bandwidth, software upgrades and so on. Unexpected events could easily drive up the cost of those new resources. In a cloud environment, the CSP already has those resources in place when a customer needs to scale cost-effectively. This makes costs easier to predict and keep within a budget.
  • Business agility. A cloud environment is designed to adapt to unexpected change with resources at the ready waiting to be allocated. Most on-premises environments have limited ability to scale before requiring more investment in resources.
  • Security. The major CSPs are among the best-secured operations. Security of on-premises environments is only as good as their weakest point. Organizations are often blind to their biggest network risks and can't match the security resources of CSPs. That problem is magnified when scaling an in-house network as it typically creates more points of vulnerability.
  • Performance. On-premises networks likely will struggle to maintain optimal performance levels if demands on them grow faster than planned or when a spike in traffic occurs. Cloud environments, again, are designed to accommodate unexpected changes.
  • Reliability. Scaling an on-premises network typically causes business disruption as new resources are brought online, old ones are retired and IT staffs are stretched thin. Cloud environments scale seamlessly because of resource redundancy and their ability to distribute workloads.
  • Geographic expansion. Planned expansion into another global region is often a reason for scaling an IT network. With an on-premises network, expanding into a new region likely requires creating a new local network with hardware, software and staff resources. The major CSPs have a global reach and can easily bring network services to a new region.

Challenges of cloud scalability

These are the four main challenges of cloud scalability:

  1. Internal cloud support. Some organizations have experienced problems that could have been avoided if they had the right expertise in-house to understand the technical complexities of their cloud environment and the applications it supports. The CSP should not be expected to have deep knowledge of an organization's infrastructure, networks and applications.
  2. Security. Similarly, security is an issue when an organization assumes that the CSP has all the responsibility for it. Many breaches have been the result of improper configurations that were the responsibility of the cloud customer, not the cloud host. Organizations need to understand which security responsibilities they own, which ones the CSP owns and which ones they share. They also need to understand how those responsibilities change as they scale up their cloud environments.
  3. Cost control. A long-term cloud contract is based on assumptions of resource demand growth with some accommodations to account for occasional spikes in demand. Getting those assumptions wrong could mean a lower return on investment for cloud expenditures. Companies sometimes fail to factor in related costs when considering a scalable cloud environment. For example, staff training and software or hardware upgrades might be required.
  4. Vendor lock-in. Beware of high egress fees for moving off one CSP to another. Pay attention to the terms of your cloud contract to ensure that you have flexibility to move some or all of your cloud environment to another vendor.

How to achieve cloud scalability

The first task to attain cloud scalability is understanding how an organization's cloud demands will grow over time. This requires a careful assessment of business growth over the term of CSP contracts, including regional expansion, product or service sales, staffing and technology upgrades. This assessment informs those responsible for purchasing long-term cloud services.

Cloud resiliency and reliability is another consideration. Many organizations use two or more CSPs, for example, to ensure the ability to switch should a disruption occur in one of them.

You also need to understand workload demands and how they might affect resource management. Which workloads are consistent, and which are less predictable? The answers inform decisions on resource management so you can ensure that workloads that require reliable high performance have the proper resources, as do workloads that create occasional spikes in resource demand.

Application compatibility is another area to understand before implementing a cloud scaling plan. For example, do all your critical applications support horizontal scaling? Older applications might not and work better in a vertical scaling environment.

Tools and technologies for cloud scalability

CSPs that have the global infrastructure to scale even the largest of businesses, sometimes referred to as hyperscaler clouds, are few. Hyperscaler cloud providers have the geographically distributed data centers and other resources to enable autoscaling -- the ability to adjust resource allocation in real time -- for even their largest customers.

These are the leading hyperscaler cloud vendors with some of the tools and features they offer:

  • Amazon Web Services. AWS' Elastic Compute Cloud provides VM management capabilities to enable on-demand workload scaling. Its Redshift data warehouse provides large-scale data analytics. If you have critical event-driven applications, AWS Lambda is a consideration as it enables code to execute without managing servers.
  • Google Cloud. Google Kubernetes Engine is a service for deploying and managing containerized applications at scale. Spanner is a high availability relational database service. If you rely on artificial intelligence (AI)-based applications, Google Cloud has the Vertex AI platform for creating and training machine learning models.
  • IBM Cloud. IBM Cloud includes IBM Watson to provide AI services at scale. If added security is a need, IBM Cloud's Key Protect hardware security module is a service for managing and protecting cryptographic keys. IBM Cloud Private helps build and manage private clouds for those organizations that require them.
  • Microsoft Azure. Microsoft Entra ID is a cloud-based identity and access management service that can scale. Cloud data warehouse services are provided through Azure Synapse Analytics, and AI services are available through Azure AI Services.

Other vendors in the cloud scalability market include the following:

  • DigitalOcean. Targeted to developers, DigitalOcean offers a suite of tools and services to create cloud applications. Droplets and DigitalOcean Kubernetes, for example, aid the development of scalable VMs and workloads. Cloudways is DigitalOcean's managed cloud hosting service.
  • Linode. Linode is a specialized cloud provider for creating Linux virtual servers. Though smaller than the hyperscale vendors, Linode does have a global network of data centers. Other features include the ability to set up private networks and virtual local area networks for organizations that need to set up isolated network environments. Pricing is considered among the most transparent and is based on resources used.
This was last updated in August 2024

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