Future of Data Centre is Autonomous

The digitalisation of our everyday life, combined with newer technologies and tech-savvy populations, creates large volumes of data, which increase our dependence on data centres to process and store this data. As a result, organizations need to streamline the management of their data centres by addressing existing inefficiencies and ensure uninterrupted access to data for all their growing applications.

However, the increasing digitisation of applications makes IT infrastructures undeniably complex, which can’t be effectively tackled with conventional data centre management tools.

Today, there is a growing need for a data centre infrastructure that no longer needs constant attention, manual tuning, and reactive troubleshooting. Autonomous data centre, which fits the bill, is an Artificial Intelligence (AI) powered infrastructure that uses intelligent algorithms, powered by sensor data from the systems, to effectively run itself.

This intelligent AI engine is capable of automatically detecting glitches, bottlenecks, or faulty configurations and has the potential to resolve them autonomously. Moreover, it can even blacklist previously detected errors, to avoid repetition and avoid previously experienced issues.

Apart from detecting and rectifying issues, AI in the data centre can also proactively recommend improvements. By leveraging the data and insights generated, it can identify opportunities for systems optimisation and better performance, which in turn has a positive impact on business processes, the effectiveness of the IT team, and customer experience.

So, how does an autonomous data centre achieve all of this? Firstly, the AI in the data centre enables simultaneous monitoring of all systems in an installed base. This allows the system to learn the ideal operating environment for every workload and application, and then identify unusual behaviour through recognition of the regular, underlying I/O patterns.

As the data generated within the business grows, it also increases the effectiveness of the AI system as it recognises regular data patterns. This extends the life of the AI system and continuously improves the IT infrastructure by rectifying new problems that emerge and suggests new ways to optimise and improve processes.

Moreover, the system leverages deep telemetry data to create a foundation of data that exploits the experiences of every system connected to its AI engine globally. If an issue is detected, the AI can learn to predict the issue and use pattern-matching algorithms to determine if any other system in the installed base is susceptible.

Additionally, this insight also enables application performance to be modelled and tuned for new infrastructure based on historical configurations and workload patterns, thereby reducing the risk for new IT deployments and implementation costs.

Based on the predictive analytics, the AI determines the appropriate recommendations required to improve and ensure the ideal environment and applies these changes automatically on behalf of IT administrators. If automation is unavailable, specific recommendations are delivered through support case automation, which frees the IT staff and eliminates the guesswork in managing the infrastructure.

AI can make the infrastructure smarter and more reliable. By placing it at the heart of data centre infrastructure management, businesses can quickly predict, prevent and resolve issues, thereby ensuring significant gains in efficiency and operational improvements. This enables IT teams to focus on tasks that add value to business and improve the quality of the customer experience.

-- Vikram K, Senior Director, Hybrid IT, Hewlett Packard Enterprise India.