Revolutionizing data centers through Machine Learning and Artificial Intelligence

by July 17, 2018 0 comments

Authored by: Naveen Lawrence, Director Sales, DatacenterDynamics India

Noted periodical The Economist recently declared data to be the world’s most valuable resource, becoming the ‘new oil’. Today, companies and entities in the financial, telecommunications, advertising, retail, and even policy domain are working hard to make sense of information collected from various sources to translate it into tangible results.

Storing this data and making it accessible for these functions is just one element of the larger digital transformation process, but its impact is quite significant. The data centres that these entities set up today will be relied upon by the autonomous AI-driven systems of the future to optimize how business is conducted in the world today.

The data centre is the backbone of the digital revolution, says Ravin Mehta (Managing Director, The unbelievable Machine Company); and without an increasing volume of data to feed new-age technologies, the digital transformation revolution will remain incomplete. Whether you are a food processing company or manufacturer of automobiles, you will not be able to harness the power of AI to optimize your production process without relevant data.

Some organizations are already using AI and Machine Learning in the process of designing and maintaining these data centres. Google’s proprietary DeepMind AI, finding time even as it is busy beating the world’s best players at various games and e-sports, helped Google reduce its Data Center cooling bill by 40%.

Leveraging these technologies to optimize data centres can help make them more efficient and efficient in terms of operational efficiency, data classification and management, and design optimization.

Operational efficiency and maintenance

Through Machine Learning, algorithms can learn from existing data to identify correlating factors and flag up problems. In a data centre, these systems can track temperature variation and other environmental factors to ensure that energy use and cooling systems are optimized.

This is how DeepMind was able to lower Google’s expenses on cooling systems – evaluating the millions of variables and their complex interactions are impossible for human beings, but these algorithms can easily analyze this data. This also has massive potential in terms of the maintenance of data centres – these systems can also measure the health of existing infrastructure, and can rely on a network of sensors to flag machines that need maintenance and even provide probable causes.

With enough data, these systems will even be able to predict problems and suggest fixes even before they happen. This helps data centres operate at the highest level of efficiency, and reduces the probability of downtime to zero.

Network optimization, data classification, and data management

Whether a company has its own data centres or chooses to rely on multi-tenant third-party centres, there is always going to be some data stored on-site and some on the cloud. AI-driven systems can track patterns of usage and measure access speeds, and choose to classify and store the full dataset in a way to optimize easy and seamless access.

Small bits of information that is frequently required can be kept on-site, whereas larger files that will be required more infrequently can be placed on the cloud. Further, the system can identify information that is asked for from multiple locations more often, and ensure that such data is on the cloud and easily accessible from all touchpoints across a network.

Even at multi-tenant data centres, these systems can help ensure equitable resource allocation across multiple clients to ensure seamless and optimized access.

Data centre design optimization

With the quantum of data being generated and stored by humanity continuing to increase, increasing data centre efficiency and reducing the overall energy consumption are major concerns.

However, the sheer quantum of data means that there will be an inevitable demand for physical data centre space, and for new data centres. AI and Machine Learning can be leveraged to design open-ended, dynamic, and efficient data centres that are more effective and efficient than any human design.

Just as every industry and sector in the market is contemplating the integration of AI and Machine Learning into their legacy processes, they must also reinvent their data collection and storage process. Using AI and Machine Learning offers them the chance of doing so in a data-driven and future-ready manner, granting them greater efficiency and optimization.

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