Enhance Cloud Resilience with AI

Organizations' ability to manage data then detect and beat process failures with AI is key to resilience.

Kausik Chaudhuri

June 3, 2024

4 Min Read
Cloud resilience tips

Most cloud environments are already quite resilient, at least compared to their on-premises counterparts. The question is, could artificial intelligence make them even more resilient?

There's good reason to argue that it can. Here's a look at what crafting resilience entails, and how businesses can embrace cloud environments with help from AI.

The Fundamentals of Cloud Resilience

Cloud resilience is the ability of cloud workloads to recover from unexpected disruptions, such as a server failure or a networking configuration problem.

Generally speaking, the underlying infrastructure of public cloud environments is extremely resilient. Major public cloud providers boast availability rates on the order of over 99.5% for most of their services, meaning the chance of infrastructure downtime is nearly zero.

That said, a robust infrastructure is only part of the equation when it comes to cloud resilience. Problems with workload configurations and management processes can also trigger downtime — which is why detecting and getting ahead of process failures is the real key to optimizing cloud resilience.

For example, imagine that you've deployed an application on a cloud server. The cloud server is configured to provide a certain amount of virtual CPU and memory resources. If your application requires more CPU and memory than originally allocated, it may experience a degradation in performance, or crash entirely. If you notice ahead of time that the app is running out of sufficient resources, you can move it to a different server before a problem occurs.

Related:Data Stewardship Best Practices for Managed Service Providers

How AI Improves Cloud Resilience

It's possible to monitor for and respond to cloud workload problems such as the one previously described via manual processes, of course. However, it becomes an inefficient approach that requires substantial workforce resources and is challenging to measure. A more advanced approach involves using rule-based alerting. For instance, you could set up an alert to notify you when the CPU or memory usage on an application's host server reaches 90%. This introduces automation and scalability to cloud monitoring, but it still leaves reliance on manual operations to respond to issues. It's also an inherently reactive approach because it only allows for the discovery of problems once they start to emerge. It doesn't initially help you build the most resilient configurations.

With AI, you can enhance resilience significantly by employing predictive analytics, which use historical data, machine learning and algorithms to predict future events. Systems then can automatically detect complex patterns before the situation escalates into problems.

Based on data collected about past application resource-consumption trends, predictive analytics could alert to the fact that the type of application being deployed is likely to exceed its allocated resources before it is ever deployed. In turn, users can change the configuration so the performance issue never materializes.

Automation Can Boost Cloud Resilience

To optimize cloud resilience even further, organizations can pair predictive analytics with automated responses — the source of the real     power of modern cloud resilience. An automated response is the use of autonomous tools to apply changes automatically in response to problems predicted by AI tools.

Thus, instead of waiting on engineers to reconfigure an application likely to fail due to the resource allocation of its host server, automated response tools can automatically move the application to a different server or resize the existing one. This approach not only saves staff time, but it also reduces the risk of a failure occurring because engineers couldn't respond quickly enough or missed the alert altogether.

As another example, if predictive analytics identifies a high probability of a distributed denial-of-service (DDoS) attack, the system can automatically reroute traffic or scale up capacity to mitigate the attack's impact. Similarly, enterprises can proactively replace or repair affected components by predicting hardware failures, preventing downtime and ensuring uninterrupted service.

Data as a Foundation for Cloud Resilience

Unlocking the AI opportunities described above hinges on organizations' ability to collect, process and manage the data necessary to power predictive analytics and automated responses.

After all, predictive analytics tools can't generate insights the way a magician pulls a rabbit out of a hat. Users need to feed AI historical data about cloud workloads. The more data fed into the algorithms — and the higher the quality of that data — the more capable the algorithms are at identifying relevant insights. Complete, high-quality data also helps AI tools to determine which automated response actions to take, based on responses that were successful in the past.

Thus, while AI creates great opportunities to improve cloud resilience, the data operation first needs a healthy foundation. Don't expect to make the leap into AI-powered cloud resilience without first investing in effective data management and governance.

AI and Well-Managed Data Are the Future of Cloud Resilience

The potential to improve cloud reliability and performance with help from predictive analytics and automated response isn't hype. The technology is mature. Taking advantage of AI is simply a matter of ensuring that you can make the right data available for AI tools, while also effectively managing and governing such data.

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