The cloud is well-known by now – and not just the ones floating up in the sky. If something isn’t saved on your mobile or MacBook, chances are it’s saved in the cloud. In fact, according to edgedelta.com, more than 90% of companies worldwide use cloud computing in their operations. From this, it’s clear that the ability to store and access data anywhere is a must nowadays.
But just because you rely on the cloud doesn’t mean it cannot be improved. A lot of this will depend on the individual, of course, but there are key steps you should take to ensure it offers the best space. Thankfully, the elasticity of the cloud is one such area that should be enhanced – and that’s exactly what this post aims to uncover.
So, to learn how to improve the cloud’s elasticity, and enable your business to reap the benefits, keep reading below.
Leverage Modern Architectures
Despite sounding a bit boxy, modern architectures include containerization, microservices, serverless computing, and more. These aspects are crucial to cloud elasticity. Traditional architectures would not be fast enough or efficient enough to meet modern demand. Instead, modern architectures enable automatic and rapid scaling up or down – the very definition of elastic architectures.
One of the best ways to leverage modern architectures is by implementing serverless architectures. Serverless computing typically offers the highest level of elasticity. Applications are allowed to scale instantly, from zero to thousands of instances and then back down. The need for manual capacity planning is removed here, with the cloud provider handling everything.
Implement Automated Scaling Policies
Auto scaling – otherwise known as automatic scaling – sees computational resources automatically allocated based on system demand. This means there will be plenty of resources available to handle peak demand. Your business would see more wasted spend otherwise.
However, this task does require some thought. It isn’t as simple as just deciding this is the tactic for you. Different providers and platforms will have different features and so on, but most will follow the same general patterns. Some of the steps will include:
- Ensuring your application is designed to be elastic
- Choosing the policy type based on your workload’s predictability
- Identifying metrics that accurately represent load
There will be common pitfalls you need to avoid, such as misconfigured thresholds. Such an issue – the scale-in threshold being too close to the scale-out – would cause the system to struggle.
Optimize Monitoring and Load Balancing
Monitoring and loud balancing act as the “brain” and “muscle” of automated infrastructure. Together, they enable systems to scale resources as demand changes. Not only does this – obviously – improve elasticity, but it also prevents downtime and reduces costs. These are benefits your business should not ignore.
To optimize monitoring, your aim is to provide real-time, actionable data. This data will trigger scaling actions. You could use AI-driven, predictive analytics to forecast future load based on historical data. This would allow for provisioning capacity before traffic spikes occur. You should aim to do this, instead of reactively struggling to play catch-up.
For optimizing load balancing, algorithms need to be fine-tuned. The use of Least Connections is encouraged, specifically if workloads have unpredictable demand. This directs traffic to the lightest-loaded servers. On the other hand, if workloads are varied, then Weighted Round Robin is best – as it accounts for different node capacities.
To conclude, cloud elasticity is critical. You mustn’t allow this to deteriorate if you depend on cloud computing in business. Instead, utilize the advice outlined in this post to ensure your cloud remains elastic and performs as needed.
