Technically Speaking

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Beating I/O bottlenecks

There are few things as frustrating in enterprise computing as delays caused by I/O bottlenecks. Your organisation has the right infrastructure and the right networking equipment yet speed and performance are let down by the storage system. What can be done to reduce I/O bottlenecks in the enterprise and what benefits can be seen from doing so?

There are few things as frustrating in enterprise computing as delays caused by I/O bottlenecks. Your organisation has the right infrastructure and the right networking equipment yet speed and performance are let down by the storage system.

What can be done to reduce I/O bottlenecks in the enterprise and what benefits can be seen from doing so?

Storage and a big data infrastructure
Whilst other elements of enterprise computing have made massive advances in the past few years, data storage has remained something of a weak link. Memory bandwidth
grows at a significantly faster rate than disk and bus performance and disk channel speed, which can result in an annoying I/O bottleneck.

In this era of big data, storage capacity is obviously of the highest importance. But spped and performance is almost equally so. Businesses are generating such massive volumes of big data and the traditional storage modes are barely able to keep up.

So if an organisation is using an infrastructure designed and built to analyse and move big data at high speed, then the storage system is an integral part of that. It must perform its role effectively or the whole process falls down.

Addressing storage performance issues
But if your organisation is running big data applications and not achieving the desired performance, then it can be hard to identify exactly where the issues are when they are tied to storage I/O bottlenecks.

Businesses have addressed this by using local storage, Hadoop was thought to work with local, commodity storage. But the problem with lcoal storage is that it doesn’t allow organisations to enjoy cloud benefits such as scalability, replication and others.
What businesses should be looking at is a solution that combines raw computing power with storage power.

This has been our approach. When we launched our high performance infrastructure for big data in October this year, everything was designed to deliver the best performance. This involves using bare metal with no hypervisor, physical networking equipment and our storage is exclusively-SSD and centralised.

This means that with hot data and OS files stored on SSD disks, the risk of I/O bottlenecks is eliminated. Any I/O intensive applications will then see a dramatic increase in performance, ensuring the potential and power of

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  bare metal is not wasted in a bottleneck. Big data applications, which eat power and performance can be managed to their full potential.

If a customer wants local disks too, these can be added, ensuring the customers gets the full flexibility of the cloud but enjoys the optimum performance from dedicated resources and local SSD storage.

Storage is an intrinsic part of our proposition, a big data infrastructure that ensures storage performance as well as scalability.

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