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BUPA boosts DaaS delivery and multi-cloud readiness with Nutanix

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A BUPA surgeon.


Nutanix, a specialist in hybrid multicloud computing, has announced that BUPA is harnessing Nutanix solutions to address performance, scalability and management issues impacting its business-critical Citrix environment that supports more than 5,000 users.

These issues were quickly resolved by migrating from a legacy infrastructure to the Nutanix Cloud platform and Nutanix Acropolis Hypervisor (AHV). Beyond this initial phase, BUPA has also begun using Nutanix Calm to fully automate management of its DaaS computing system and allow for rapid deployment of this and other workloads to any cloud in accord with long term multicloud strategy.

With DaaS computing seen as a business-critical application by leading health insurance and healthcare provider BUPA, the company had invested heavily in a state-of-the-art Citrix solution tailored to meet its exacting requirements. The legacy infrastructure supporting that software, however, was approaching end of life and its replacement needed to address a number of long-standing issues.

Scalability, in particular, had become a real headache requiring consensus across multiple stakeholders just to get approval for minor upgrades and changes. Even then, those changes could still take days or sometimes weeks, severely limiting BUPA’s ability to respond quickly to rapidly moving business demands. Added to which it was a very complex setup calling for expert skills across multiple disciplines just to keep the infrastructure lights on, let alone enhance or develop it further.

The BUPA tech team decided that switching to the Nutanix Cloud Platform would be best for its Citrix DaaS solution. The configuration chosen called for Nutanix clusters for each of two sites, to support the 3,000 existing DaaS computing users whilst also allowing for future growth. These were quickly delivered then subsequently deployed across two sites to meet rapid failover and disaster recovery requirements.

Users saw a marked improvement in desktop performance straight away and behind the scenes, Nutanix and Citrix worked to optimise the environment. More than that, the whole system was a lot simpler and easier to manage with on-demand scaling available at the press of a button and full end-to-end visibility from a single console. Support too has been streamlined with just one point of contact for all issues, whether hardware or software related, including the hypervisor. All can now be managed from the same console, eliminating the need for expert skills thereby freeing up staff to concentrate on other tasks, including plans to use Nutanix Calm to automate all aspects of the application lifecycle.

Rick Jagger, technical services manager, BUPA, said: “By migrating our DaaS computing environment from a legacy 3-tier platform to Nutanix we’ve gained a lot more than just a more scalable, performant and agile infrastructure. We’ve kept operational costs down by using the AHV hypervisor and further lowered support overheads with Nutanix Calm which is enabling us to both automate day to day management tasks and move forward with confidence to a multicloud future.”

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Like most large enterprises BUPA expects the Cloud to play an increasing role in its IT strategy. It is already looking at using Calm to enable it to redeploy rather than upgrade applications in place. BUPA is also impressed by Calm’s ability to quickly deploy DaaS computing and other applications onto any cloud and, where possible, balance workloads across what could, eventually, become a global multicloud network supporting other parts of the BUPA business.

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TECHNOLOGY

How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning

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How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning


In the event of a severe incident for your business, you need to analyze what exactly changed (the root cause) to understand its impact.

Using machine learning for root cause analysis can help identify the event that caused the change quickly and easily.

Things can sometimes go wrong in your business’s daily operations. It can be a minor issue, such as a system outage lasting for a couple of minutes. Or it can be something severe as a cyberattack.

Generally, such events result from a chain of actions that eventually culminate in the event. Identifying the root cause is the best way to solve the issue. But manual root cause analysis takes time and often doesn’t provide the exact cause of a mishap. Using machine learning for root cause analysis can automate the process, helping identify the underlying cause quickly and with higher accuracy.

Power of Machine Learning for Root Cause Analysis

To understand why an issue occurred, you need to identify the root cause. But root cause analysis can often be complex and provide inaccurate results. Using machine learning for root cause analysis helps solve this issue.

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Log Analysis

Using machine learning for root cause analysis can help zero in on the exact location of the problem. You don’t have to scroll through infinite logs to identify which components were impacted and when. The machine learning program can automatically and quickly find the root cause by analyzing a given log data set. 

Moreover, the machine learning program can even predict future incidents as more and more data is available. The program compares real-time data with historical data to predict future outcomes and warns you of any unwanted incident beforehand. This will help improve your incident response, reduce downtime and improve productivity.

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Benefits of Using Machine Learning for Root Cause Analysis

There are many benefits of using machine learning for root cause analysis. It can help teams take the right action at the right time, minimizing your losses. Some of the benefits are discussed below.

Reduces Costs

The cost of solving the issue is reduced as your teams don’t have to guess and work around blind spots. Machine learning tools locate the exact line of code responsible for a performance issue, and your team can start working on fixing it right away.

Saves Time

The time spent fixing the issue is significantly reduced as it helps solve business pain faster by locating the cause quickly and accurately.

Provides Long-Lasting Solutions

Machine learning tools provide a permanent solution for your problems and foster a productive and proactive approach.

Grows Your Business

Using machine learning for root cause analysis helps improve the efficiency and productivity of your organization, which eventually leads to business growth.

 

No system is perfect. Incidents will happen, no matter what. But what you do afterward is in your control. Root cause analysis should be done as soon as possible. Using machine learning for root cause analysis not only improves your incident response, but over time, it can also help prevent incidents from happening in the first place.



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