Robots in the data center: how can artificial intelligence be useful?
In the process of digital transformation of the economy, mankind has to build more and more data centers. Data centers themselves must also be transformed: the issues of their fault tolerance and energy efficiency are now more important than ever. Facilities consume a huge amount of electricity, and failures of the critical IT infrastructure located in them cost the business a lot of money. To the aid of engineers come the technologies of artificial intelligence and machine learning - in recent years they have been increasingly used to create more advanced data centers. This approach increases the availability of facilities, reduces the number of failures and reduces operating costs.
How it works?
Artificial intelligence and machine learning technologies are used to automate the adoption of operational decisions based on data collected from various sensors. As a rule, such tools are integrated with DCIM (Data Center Infrastructure Management) systems and allow predicting the occurrence of emergency situations, as well as optimizing the operation of IT equipment, engineering infrastructure, and even staff. Very often, manufacturers offer data center owners cloud services that collect and process data from many customers. Such systems summarize the operating experience of different data centers, therefore they work better than local products.
IT Infrastructure Management
HPE Promotes InfoSight Cloud Predictive Analysis Servicefor managing IT infrastructure built on Nimble Storage and HPE 3PAR StoreServ storage systems, HPE ProLiant DL / ML / BL servers, HPE Apollo rack systems and the HPE Synergy platform. InfoSight analyzes the sensors installed in the equipment, processing more than a million events per second and constantly self-learning. The service not only detects malfunctions, but also predicts possible problems with the IT infrastructure (equipment failures, storage capacity exhaustion, virtual machine performance degradation, etc.) even before they occur. For predictive analytics, VoltDB software has been deployed in the cloud using autoregressive forecasting models and probabilistic methods. A similar solution is available for Tegile Systems hybrid storage systems: The IntelliCare Cloud Analytics cloud service monitors the status, performance, and use of device resources. Artificial intelligence and machine learning technologies are also used by Dell EMC in their high-performance computing solutions. There are many similar examples, almost all the leading manufacturers of computing equipment and data storage systems are now taking this path.
Power Supply and Cooling
Another area of application of AI in data centers is associated with the management of engineering infrastructure and, above all, with cooling, whose share in the total energy consumption of an object can exceed 30%. Google was one of the first to think about smart cooling: in 2016, together with DeepMind, it developed an artificial intelligence system.to monitor the individual components of the data center, which reduced by 40% the energy consumption for air conditioning. Initially, it only gave hints to staff, but was subsequently refined and now can control the cooling of machine rooms on its own. A neural network deployed in the cloud processes data from thousands of internal and external sensors: it makes decisions taking into account the load on the servers, temperature, as well as wind speed on the street and many other parameters. The instructions offered by the cloud system are sent to the data center and there they are once again checked for security by local systems, while staff can always turn off the automatic mode and start controlling cooling manually. Nlyte Software and IBM Watson team create a solution, which collects data on temperature and humidity, power consumption and workload of IT equipment. It allows you to optimize the work of engineering subsystems and does not require connection to the manufacturer’s cloud infrastructure - if necessary, the solution can be deployed directly to the data center.
Other examples
There are a lot of innovative smart solutions for data centers in the market and new ones are constantly appearing. Wave2Wave has created a robotic fiber-optic cable switching system for the automated organization of cross-connections in traffic exchange nodes (Meet Me Room) inside the data center. The system developed by the ROOT Data Center and LitBit uses AI to monitor standby diesel engines, and Romonet made a self-learning software solution for optimizing the infrastructure. The solutions created by Vigilent use machine learning to predict failures and optimize temperature conditions in the data center. The introduction of artificial intelligence, machine learning and other innovative technologies in process centers for process automation began relatively recently, but today it is one of the most promising areas of industry development. Modern data centers have become too large and complex to be effectively managed manually.