Artificial Intelligence key to improve efficiency of data centers Posted on : Jul 16 - 2018

Artificial Intelligence (AI), has arrived. The academic contemplation of machine intelligence, that has been around since 1950’s is finally a reality. As per IDC, cognitive and AI spending will grow to $52.2 billion in 2021 and achieve a compound annual growth rate (CAGR) of 46.2% over the 2016-2021 forecast period. Most industries are experimenting with AI to drive innovation.

As the amount of data generated, shared and stored continues to grow so too does the pressure on data centers to increase efficiency while trying to reduce subsequent energy consumption. AI can help here, by mitigating data centers’ energy consumption, while improving uptime and reducing costs without compromising performance.

There are many ways it can vastly improve data center infrastructure:-

Configuration management: The term “community wisdom” is not quite new. For instance, large storage enterprises which are moving towards autonomous management for their customers also contain information on what configurations/deployment models are popular amongst their customer base. They know the infrastructure configuration - be it storage, network or compute, works well for customers running application of a particular type. AI/ML helps in gathering these tidbits of wisdom.

Energy Saving: In 2014, Google acquired an AI startup, DeepMind, to slash costs and improve efficiencies in its data centers. The AI engine automatically managed power usage by discovering and reporting inefficiencies across 120 data center variables - fans, cooling systems, windows etc. The results have been positive. Google has been able to reduce its total data center power consumption by 15 percent, saving the company millions over the next several years. Additionally, the company saved 40 percent alone on power consumed for cooling purposes. AI systems thus, can help analyze power, cooling as well as the overall health and status of critical backend systems to save improve efficiency while saving energy.

Proactive failure predictions, troubleshooting and automated fixes: Many AI based deep learning (DL) applications are working towards predicting failures ahead of time. This is based on the sequence of observed events, and performance problems using infrastructure telemetry data. In such an autonomous management system, corrective action enforcement is also expected. Using ML techniques based recommendation systems, one can also enforce the most appropriate fixes in the system. View More