Differences Between Elasticity & Scalability in Cloud Computing

Cloud computing is a kind of infinite pool of possibilities. In one way or another - anything is possible with cloud computing in the mix. Cloud makes everything more  convenient and much less troublesome:

  • Need to store a lot of data - check;
  • Need to train machine learning algorithms - check;
  • Need to construct a practical business framework - check;
  • Need to automate and orchestrate the routines - check;
  • Need to handle a large workload - check;

Cloud elasticity and cloud scalability are amongst the integral elements of cloud computing. Despite its widespread use, there is a lot of confusion regarding what is doing what and how exactly. 

In this article, we will explain what cloud scalability is and how it compares to cloud elasticity.

What is the difference between Cloud Elasticity and Cloud Scalability?

In the grand scheme of things, cloud elasticity and cloud scalability are two parts of the whole. Both of them are related to handling the system’s workload.  

The fundamental concept of the two is adaptability. It refers to the system environment’s ability to use as many resources as required.

The difference is in the following: 

  1. Cloud Elasticity is a tactical resource allocation operation. It provides the necessary resources required for the current task and handles varying loads for short periods. For example, running a sentiment analysis algorithm, doing database backups or just taking on user traffic surges on a website. 
  2. Cloud Scalability is a strategic resource allocation operation. Scalability handles the increase and decrease of resources according to the system's workload demands. 

Scalability and elasticity occur behind the scenes and make the system workflow smooth and seamless. As you can see, it is similar to the “think global - act locally” approach of social activists. The main benefits of both scalability and elasticity are the following:

  • Cost-effectiveness. Cloud scalability and cloud elasticity features constitute an effective resource management strategy:
    • The pay-per-use model makes cloud elasticity the proper answer for sudden surges of workload demand (vital for streaming services and marketplaces);
    • The pay-as-you-expand model allows to plan out gradual growth of the infrastructure in sync with  growing requirements (especially handy for ad tech systems);
  • Consistent performance - scalability and elasticity features operate resources in a way that keeps the system’s performance smooth, both for operators and customers.
  • Service availability. Scalability enables stable growth of the system, while elasticity tackles immediate resource demands.

Now let’s explain what each of these things means.

What is Cloud Elasticity?

 Сloud elasticity is a system’s ability to manage available resources according to the current workload requirements dynamically. 

Elasticity is a vital feature of cloud infrastructure. It comes in handy when the system is expected to experience sudden spikes of user activity and, as a result, a drastic increase in workload demand. 

Because of the pay-per-use pricing model of modern cloud platforms, cloud elasticity is a cost-effective solution for a business with a dynamic workload. 

This is the case for businesses with dynamic resource demands like streaming services or e-commerce marketplaces. Various seasonal events (like Christmas, Black Friday) and other engagement triggers (like when HBO’s Chernobyl spiked an interest in nuclear-related products) cause spikes of customer activity. These volatile ebbs and flows of workload require flexible resource management to handle the operation consistently. 

For example:

  • Streaming Services. Netflix is dropping a new season of Mindhunter. The notification triggers a significant number of users to get on the service and watch or upload the episodes. Resource-wise, it is an activity spike that requires swift resource allocation. 
  • E-commerce. Amazon has a Prime Day event with many special offers, sell-offs, promotions, and discounts. It attracts an immense amount of customers on the service who are doing different activities. Actions include searching for products, bidding, buying stuff, writing reviews, rating products. This diverse activity requires a very flexible system that can allocate resources to one sector without dragging down others. 

What is Cloud Scalability?

 Cloud scalability is the ability of the system’s infrastructure to handle growing workload requirements while retaining a consistent performance adequately. 

Unlike cloud elasticity, which is more of makeshift resource allocation - scalability is a part of infrastructure design. 

Scalability is one of the preeminent features of cloud computing. In the past, a system’s scalability relied on the company’s hardware, and thus, was severely limited in resources. With the adoption of cloud computing, scalability has become much more available and more effective.  

Automatic scaling opened up numerous possibilities for the implementation of big data machine learning models and data analytics to the fold. 

Overall, Cloud Scalability covers expected and predictable workload demands and also handles rapid and unpredictable changes in the scale of operation. The pay-as-you-expand pricing model makes possible the preparation of the infrastructure and its spending budget in the long term without too much strain.

There are several types of cloud scalability:

  • Vertical, aka Scale-Up - the ability to handle an increasing workload by adding resources to the existing infrastructure. It is a short term solution to cover immediate needs.
  • Horizontal, aka Scale-Out - the expansion of the existing infrastructure with new elements to tackle more significant workload requirements. It is a long term solution aimed to cover present and future resource demands with room for expansion.
  • Diagonal scalability is a more flexible solution that combines adding and removal of resources according to the current workload requirements. It is the most cost-effective scalability solution by far.  

Scalability is an important factor for the business whose resource demands are increasing slowly and predictably. 

Let’s take a call center, for example. The typical call center is continuously growing. New employees come in to handle an increasing number of customer requests gradually, and new features are introduced to the system (like sentiment analysis, embedded analytics, etc.). In this case, cloud scalability is used to keep the system’s performance as consistent and efficient as possible over an extended time and growth. 

Another example of scalability in action is Natural language processing model training and optimization for chat-bots. The system starts off on a certain scale and requires room for gradual improvement as it is being used. The database expands and the operating inventory becomes much more intricate.

Consequently, cloud scalability is integral for  cloud-based services such as:

  • Infrastructure-as-a-Service (IaaS) - like Amazon EC2 or Google Compute Engine;
  • Platform-as-a-Service (PaaS) - like Magento Commerce Cloud or AWS Elastic Beanstalk;
  • Storage-as-a-Service (STaaS) - Google Drive, Microsoft OneDrive, and the likes.
  • Data-as-a-Service (DaaS) - customer relationship platforms like Salesforce and Hubspot, ERP applications;
  • Database-as-a-Service (DBaaS) - AWS SimpleDB, Rackspace, Oracle, MongoDB;

In conclusion

Modern business operations live on consistent performance and instant service availability. 

Cloud scalability and cloud elasticity handle these two business aspects in equal measure. 

  • Cloud scalability is an effective solution for businesses whose workload requirements are increasing slowly and predictably. 
  • Cloud elasticity is a cost-effective solution for the business with dynamic and unpredictable resource demands. 

These features make both scalability and elasticity a viable instrument for the company to hold its ground,  grow steadily, and gain a competitive advantage.

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Volodymyr Bilyk

The App Solutions resident AI advocate