Jun 19, 2020

Choosing the right cloud is key to digital transformation

Cloud
CAPEX
opex
AI
Scott Leatherman
4 min
Here’s how you can rationalise your infrastructure and determine if there are cloud expenses you can reclaim
Here’s how you can rationalise your infrastructure and determine if there are cloud expenses you can reclaim...

If you’ve been operating in the cloud for some time now, chances are your business has changed since you first made that move and particularly during the current climate. Has your cloud usage grown considerably—and your OpEx costs? Is that just the cost of doing business in the cloud? It doesn’t have to be. Here’s how you can rationalise your infrastructure and determine if there are cloud expenses you can reclaim and even if it makes sense to move some of your cloud deployments into co-location.

The rush to the public cloud has now slowed as organisations realised that it is not a ‘one size fits all’ solution. The main issue is the lack of deep visibility into the performance of applications provided by the host. Our own research has recently revealed that 32% of public cloud resources are currently under-utilised, and without proper direction and guidance this will remain the case. What is needed is real-time data and intelligent recommendations to lower costs and assure performance.

In order to optimise cloud resources, a third party AIOps based resource is needed. This will provide an independent and granular view of how applications are using capacity and if it is right-sized. In addition, it will monitor the performance of the applications in real time and provide metrics and analytics to eliminate bottlenecks. The allocated capacity can also be monitored to ensure an accurate match to workload requirements via real-time performance data.

Although the major hosts provide cost optimisation tools, these are not very accurate. Analysis of billing and how it matches capacity over time as well as in real time is what is needed for the cloud to remain a vital part in IT infrastructure. Armed with this information you can plan capacity purchases and discover wasted spend. By using a single platform for cloud management, you can monitor your infrastructure, plan capacity, and eliminate performance risks. Performance bottlenecks can be predicted before they affect clients and SLAs with multi-conditional alerting powered by advanced anomaly detection.

Cloud solutions are not only publicly provided by the likes of AWS and Azure. Co-location is also a strong option where your applications are managed on your behalf by a system integrator. This is increasingly becoming a stronger option for more business-critical applications. But to determine which is best for you, you need to start with the facts.

The “Cloud” promises IT organisations unprecedent value in the form of business agility, faster innovation, superior scalability and most importantly - cost savings. For many organisations, it is at the core of their IT digital transformation strategy. It is a disruptive force that requires application workload behavior knowledge, careful planning and collaboration from well-informed, trusted advisors.

As a first step, enterprises frequently target a subset of their less critical on-premises applications for migration to the public cloud. Typically, organisations will take one of two paths to the cloud.

A. Going cloud native. Rewrite your application to use resources offered by a cloud provider.

B. Lift and shift. Very minimal or zero code changes to the application. Largely, just replicate the application in the cloud.

The faster time-to-production choice is to “lift and shift” the targeted applications to a Cloud Service Provider’s Infrastructure as a Service (IaaS). In the lift and shift option, the advantage is reduction in the cost incurred in the physical infrastructure like hardware, floor space, cooling, security etc. and the management of that infrastructure. Savings will differ depending on your unique computing resource needs, workload refactoring and business models.

Even in its simplest form, IaaS migrations must be carefully planned requiring answers to some fundamental questions:

1. Will my application perform as expected in a public cloud? (Application Fitness)

2. How much will it cost to run my applications in a public cloud? (OpEx)

3. Which cloud service provider is the best choice for my applications? (Cost and Fit)

IT managers need answers to these questions before the actual migration is performed. As most internal IT organisations don’t have deep cloud expertise, the question becomes who you can trust to provide you with the answers – to help you make better business decisions.

As technology and the cloud stands to play an ever-increasing role throughout organisations, ensuring that you’re adopting the right type of infrastructure specifically for your business has never been more vital for continued success. Choosing a service that provides the answers to your key questions before the actual migration takes place and prepares you with vital insights into your applications and workloads targeted for cloud migration has to be an important part in the decision-making process.

As organisations continue to battle the COVID-19 storm, understanding the product that will overhaul your IT infrastructure, before you fully buy into it, is going to provide the confidence and assurance you need to make that decision a little less cloudy.

By Scott Leatherman, CMO, Virtana 

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May 7, 2021

AI Shows its Value; Governments Must Unleash its Potential

AI
Technology
digitisation
Digital
His Excellency Omar bin Sultan...
4 min
His Excellency Omar bin Sultan Al Olama talks us through artificial intelligence's progress and potential for practical deployment in the workplace.
His Excellency Omar bin Sultan Al Olama talks us through artificial intelligence's progress and potential for practical deployment in the workplace...

2020 has revealed just how far AI technology has come as it achieves fresh milestones in the fight against Covid-19. Google’s DeepMind helped predict the protein structure of the virus; AI-drive infectious disease tracker BlueDot spotted the novel coronavirus nine days before the World Health Organisation (WHO) first sounded the alarm. Just a decade ago, these feats were unfathomable. 

Yet, we have only just scratched the surface of AI’s full potential. And it can’t be left to develop on its own. Governments must do more to put structures in place to advance the responsible growth of AI. They have a dual responsibility: fostering environments that enable innovation while ensuring the wider ethical and social implications are considered.

It is this balance that we are trying to achieve in the United Arab Emirates (UAE) to ensure government accelerates, rather than hinders, the development of AI. Just as every economy is transitioning at the moment, we see innovation as being vital to realising our vision for a post-oil economy. Our work in his space has highlighted three barriers in the government approach when it comes to realising AI’s potential. 

First, addressing the issue of ignorance 

While much time is dedicated to talking about the importance of AI, there simply isn’t enough understanding of where it’s useful and where it isn’t. There are a lot of challenges to rolling out AI technologies, both practically and ethically. However, those enacting the policies too often don’t fully understand the technology and its implications. 

The Emirates is not exempt from this ignorance, but it is an issue we have been trying to address. Over the last few years, we have been running an AI diploma in partnership with Oxford University, teaching government officials the ethical implications of AI deployment. Our ambition is for every government ministry to have a diploma graduate, as it is essential to ensure policy decision-making is informed. 

Second, moving away from the theoretical

While this grounding in the moral implications of AI is critical, it is important to go beyond the theoretical. It is vital that experimentation in AI is allowed to happen for its own sake and not let ethical problems stymie innovations that don’t yet exist. Indeed, many of these concerns – while well-founded – are born out in the practical deployment of these end-use cases and can’t be meaningfully discussed on paper.

If you take facial recognition as an example, looking at this issue in abstract quickly leads to discussions over privacy concerns with potential surveillance and intrusion by private companies or authorities’ regimes. 

But what about the more specific issue of computer vision? Although part of the same field, the same moral quandaries do not arise, and the technology is already bearing fruit. In 2018, we developed an algorithmic solution that can be used in the detection and diagnosis of tuberculosis from chest X-rays. You can upload any image of a chest X-ray, and the system will identify if a person has the disease. Laws and regulations must be tailored to unique use-cases of AI, rather than lumping disparate fields together.

To create this culture that encourages experimentation, we launched the RegLab. It provides a safe and flexible legislation ecosystem to supports the utilisation of future technologies. This means we can actually see AI in practice before determining appropriate regulation, not the other way around. Regulation is vital to cap any unintended negative consequences of AI, but it should never be at the expense of innovation. 

Finally, understanding the knock-on effects of AI

There needs to be a deeper, more nuanced understanding of AI’s wider impact. It is too easy to think the economic benefits and efficiency gains of AI must also come with negative social implications, particularly concern over job loss. 

But with the right long-term government planning, it’s possible to have one without the other; to maximise the benefits and mitigate potential downsides. If people are appropriately trained in how to use or understand AI, the result is a future workforce capable of working alongside these technologies for the better – just as computers complement most people’s work today.

We’ve to start this training as soon as possible in the Emirates. Through our Ministry of Education, we have rolled out an education programme to start teaching children about AI as young as five years old. This includes coding skills and ethics, and we are carrying this right through to higher education with the Mohamed bin Zayed University of Artificial Intelligence set to welcome its first cohort in January. We hope to create future generations of talent that can work in harmony with AI for the betterment of society, not the detriment.

AI will inevitably become more pervasive in society, digitisation will continue in the wake of the pandemic, and in time we will see AI’s prominence grow. But governments have a responsibility to society to ensure that this growth is matched with the appropriate understanding of AI’s impacts. We must separate the hype from the practical solutions, and we must rigorously interrogate AI deployment and ensure that it used to enhance our existence. If governments can overcome these challenges and create the environments for AI to flourish, then we have a very exciting future ahead of us.

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