Cloud vs data centers.

When it comes to data, it is important that businesses can scale up – and down, according to the amount of usage that is occurring.

The ability to grow is an important part of most businesses, but this doesn’t apply just to the profit that is being made. When it comes to data, it is also important that businesses can scale up – and down, according to the amount of usage that is occurring.

In addition to a growth in popularity of a business, there are also other times that data size requirements can spike – Black Friday, around Christmas, or during the daytime, for example.

Traditionally, businesses needed to create more data storage in a manual and semi-permanent manner, often leaving swathes of space for when it is not being used – wasting space and money. The term ‘hyperscalability’ refers to how able a computer’s architecture is to respond to growth in the demand for more data.

Deciding between cloud data storage and using a data center is like choosing between a virtual data storage solution and a more rigid one. They each come with different benefits and the decision should look at how suited they are to your business.

The Power of Scalability

For businesses, the scalability of data is an important consideration. It is important that your data storage availability matches your data storage needs as they fluctuate. This allows you to maximise the impact of moments of high demand and economise when demand is lower.

This means that your website is able to function well even at peak times, without your business wasting money when you need lower capacity.

Simplifying Scaling Problems

The problems that are associated with not being able to scale as based around having apps and systems that cannot cope with increased demand, as well as wastage of resources at low times, proving to be costly.

When businesses don’t have sufficient data storage for their needs, users will find that there are problems with performance, leading to error messages, and locking them out of application completely. This will almost certainly result in reduced or loss of service for customers – potentially losing them entirely, and therefore a lower income for the business.

Some companies are able to successfully predict their data storage needs and therefore accommodate paying for the extra space when they need it.

For other businesses, the best solution is a cloud system that can be adjusted following the demand for data storage.

Cloud Scaling Strategies

There are two ways of scaling – either up or down (vertically) or in or out (horizontally). Each scaling strategy is different and should be applied according to what you need from it.

  • Vertical scaling on the cloud is the process of replacing your existing server with a new and more powerful one or adding I/O resources, CPU, or memory to your server. Scaling up or down can be done by changing the sizes of the instance if you are using Amazon Web Service (or AWS) or Microsoft Azure, or if you are using a data center, you can replace an old appliance with a new, more powerful one.

If you are using AWS or Microsoft Azure, they have different instance sizes, making vertical scaling possible for everything that you need.

When you scale up or down, you will probably need an amount of downtime whilst the process is carried out.

  • Horizontal scaling on the cloud system works in a different way. This process involves adding extra servers to the system, allowing data to be shared amongst a larger number of servers, lowering the strain on each one. This means that instead of replacing an old one, you are adding instances to your system. Horizontal scaling can be done without downtime, is easier to manage than vertical scaling, and it is preferable to reduce the number of requests for an instance.

How Data Centers Tackle Hyperscaling

When you are looking at hyperscaling for the cloud, you have three options – to do it manually, to schedule it, or to set it up to occur automatically.

If you want to carry out manual scaling, you will need an engineer to physically do the work at the time when the scaling is needed. This means that you will continually need to monitor the demand for data and is likely to result in excess or deficient levels of data storage. Although it is a simple process, human error is also a possibility.

Scheduling your hyperscaling is a similar process to the manual hyperscaling. However, instead of having a person carrying it out in real-time, you can use your knowledge of the pattern of demand to schedule your in-scaling and out-scaling. This helps to mitigate human error and reduce the need for close monitoring, but it is still unable to react quickly to new and unexpected fluctuations in demand.

Automatic scaling – or autoscaling – works on the premise of having a set of rules that trigger the scaling. This means that if, for example, your memory or vCPU rates go above or below a certain level, you can scale automatically out, in, up, or down. There is no risk of human error and with this option, you can always ensure that you have the right amount of data storage that you need.

Conclusion

Scaling is an important concept that every business that uses technology needs to be aware of. Ensuring that your data storage is scalable means that you can avoid over-spending on your storage as well as cope with any extra demand that you may have.