In 2025, nearly 85% of businesses will operate on the cloud-first approach, which is a more efficient method of hosting data than on-premises. The move to cloud computing enhanced by COVID-19 and remote work has brought many advantages for companies, including reduced IT costs, improved efficiency, and security.
With this growth trend increasing, the possibility of disruptions to services and outages is growing. Cloud providers are highly reliable. However, they’re “not immune to failure.” In December 2021, Amazon reported seeing multiple Amazon Web Services (AWS) APIs affected. Then within minutes, many popular websites were down.
How can businesses minimize cloud risks, prepare in case of any AWS shortage, and be prepared for sudden surges in demand?
The answer lies in scalability and elasticity — the two crucial elements in cloud computing which significantly help companies. Let’s discuss the differences between elasticity and scalability and look at what can be developed at the cloud infrastructure and application and database levels.
Know the difference between elasticity and scalability.
Both elasticity and scalability are tied to the number of concurrent requests handled simultaneously in a cloud system. They aren’t mutually exclusive. Each of them may need to be supported in their way.
Scalability is the capacity of a system to be flexible to the increasing amount of users, and traffic grows as time passes. This is why it’s the long-term expansion that is designed. A majority of B2B or B2C applications gaining popularity will require this to ensure reliability, high performance, and uptime.
With just a few adjustments to the configuration and a few buttons clicked within minutes, businesses can increase the capacity of their cloud or down without difficulty. In many instances, it is possible to automate this through cloud platforms using scaling factors applied at the cluster, server, or network layers, which can reduce costs for engineering and labor.
Elasticity refers to the capacity of the system to remain flexible to short-term surges of the activity or immediate waves in load. A few examples of systems that frequently face elasticity issues are NFL ticketing systems, auction systems, and insurance companies in natural catastrophes. In 2020 the NFL could rely on AWS to stream live its virtual draft when it needed a significant increase in cloud capacity.
An organization that has trouble with unpredictability in workloads but doesn’t require an established scaling plan could look for a flexible solution using the cloud that has fewer maintenance costs. The management of this cloud will be handled by a third-party service provider and shared among multiple organizations that use the internet for public access.
Does your company have predictable work schedules, high-risk ones, or both?
Find out the best scaling options for cloud infrastructure.
In terms of scaling, companies must look out for under- or over-provisioning. This can happen when IT teams cannot provide quantitative data on the application’s requirements for resources, or the concept of backend scaling isn’t aligned with the business objectives. To determine the best solution, constant testing of performance is crucial.
The business leaders who read this article should talk to their IT teams to discover how to discover the cloud provisioning blueprints. IT teams must constantly monitor response times, the number of requests, load on CPU, and memory utilization to monitor how much cost (COG) is related to cloud costs.
There are many scaling methods for organizations based on their business requirements and technical limitations. Decide if you want to scale upwards or out?
Vertical scaling refers to scaling down or up and is helpful for monolithic applications, usually built before 2017. It can also be challenging to modify. It requires adding additional resources, such as memory (or processing power (CPU) on your current server in case of increased workload. However, this also means that the scaling will have limitations depending on the capabilities of your server. It doesn’t require any application design changes when transferring the same application, file, and database onto a new machine.
Horizontal scaling is scaling out or in and adding servers to the cloud infrastructure so that it functions as one system. Each server should be separate so that servers can be added or taken away separately. This requires various design and architectural considerations about load-balancing sessions, the management of sessions, caches, and communications. Migration of older (or obsolete) applications that aren’t explicitly designed for distributed computing should be carefully refactored. Horizontal scaling is crucial for companies that offer high-availability services that need to be able to operate with minimal downtime and the highest performance in memory and storage.
If you’re not sure what scaling method is best suited to your business, you may be able to use an external cloud engineering automation platform that can assist you in managing your scaling requirements and goals, and implementation.
Consider how architectures for applications impact elasticity and scalability.
Let’s consider a straightforward healthcare app – which can be applied to other industries – to examine how it is built on different architectures and how it affects scalability and flexibility. Healthcare services were under severe pressure and needed to drastically expand during the COVID-19 pandemic and could have benefited from cloud-based services.
At the highest level, there are two kinds of architecture: distributed and monolithic. Monolithic (or modular, layered monolith pipeline, microkernel, and) architectures aren’t designed for scalability or elasticity. All modules are contained in the core of the application, and in the end, the entire application can be executed as a complete system. There are three kinds of distributed systems: event-driven microservices and space-based.
The primary application for healthcare has an:
- Patient portal – a place for patients to sign-up and make appointments.
- Physician portal – allows medical personnel to look over medical records, conduct examinations, and prescribe medications.
- Office portal for the accounting department and support staff to receive payments and answer questions.
Hospital services are highly sought-after, and, to accommodate the growing demand, they have to expand the registration of patients and scheduling module. This means that they need to increase the size of the patient portal and not the office or physician portals. Let’s examine how this application could be built on the various architectures.
Monolithic architecture
Tech-enabled startups, such as healthcare, typically stick to this standard uniform software development model due to the speed-to-market advantage. It isn’t the best solution for companies needing scalability or elasticity. It is because there is only one instance integrated into the program and a single, central database.
To scale the application by adding more instances of the app that use load-balancing can result in expanding the other two portals, as well as the portal for patients, even though the business does not require it.
Most monolithic apps use a uni-dimensional database — one of the most costly cloud resources. Cloud prices increase exponentially with increasing scale. This arrangement is expensive, particularly maintenance times for operations and development engineers.
Another factor that renders monolithic architectures incompatible with flexibility and scalability is means-time-to-startup (MTTS) -the time that an entirely new instance of the application starts. It typically takes some time due to the vast area of the application and database: Engineers have to design the necessary functions, such as dependencies, objects as well as connection pools, to ensure connectivity and security to other services.
Event-driven architecture
An event-driven architecture is more suitable than monolithic architectures for scaling and elastic. It, for instance, announces an event whenever something notable occurs. This could mean shopping in an online store during peak times and placing an order and then receive an email stating that it’s not in availability. Asynchronous queues and messaging provide back-pressure when the frontend is increased without scaling the backend through queueing requests.
In this scenario, the distributed design means that each module has the sole event processor, and it is possible to share or distribute data over several modules. There’s some flexibility at the application and a database level concerning the scale of services since they are no longer tied.
Microservices architecture
This approach views every service as a singular-purpose service, giving companies the capability to scale each service independently and avoid using precious resources in excess. The persistence layer should be created and configured specifically for each service to allow for specific scaling to scale databases.
In addition to event-driven architectures, these architectures are more expensive for cloud resource usage than monolithic structures with low use. However, with the increase in load multitenant deployments and instances of frequent traffic interruptions, they are more cost-effective. The MTTS is highly effective and can be measured within seconds due to its fine-grained features.
However, given the sheer quantity of services and their distribution, understanding might be more complex, and there could be more maintenance costs if the services aren’t entirely automated.
Space-based architecture
This design is based on a tuple-spaced computing principle — multiple parallel processors sharing memory. This type of architecture increases flexibility and scalability at the application level and also at a database level.
Every interaction with the application is made using the in-memory data grid. Grid calls are synchronous, and event processors may scale independently when mounted databases have a background data writer who reads and updates the database. Each insert, edit or delete operation is transmitted to the data writer via the service that handles them and is queued for pickup.
The MTTS process is rapid and typically takes only less than a millisecond because all data interactions use memory-based data. However, all applications must join the broker, as well as the first cache load needs to be made using the help of a data reader.
In this age of technology, companies are seeking to increase or reduce IT resources when required to meet the demands of changing times. One of the first steps is shifting from large monolithic systems to distributed systems to increase competitiveness. This is precisely what Netflix, Lyft, Uber, and Google have done. But, the decision of which architecture to use is not a given. It should be made according to the abilities of the developers and their the mean load and peak load budgetary constraints, and business growth objectives.
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