Robots in Data Center

Started by minhtuyen19091, Sep 30, 2022, 06:46 AM

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In the process of digital transformation of the economy, humanity has to build more and more data processing centers. The 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 placed in them are expensive for business.
 Artificial intelligence and machine learning technologies are coming to the aid of engineers — in recent years they have been increasingly used to create more advanced data centers. That approach increases the level of readiness of facilities, reduces the number of failures and reduces operating costs.

How does it work

Artificial intelligence and machine learning technologies are used to automate operational decision-making based on data collected from various sensors. As a rule, such tools are integrated with DCIM (Data Center Infrastructure Management) class systems and allow you to predict the occurrence of emergency situations, as well as optimize the operation of IT equipment, engineering infrastructure and even maintenance personnel. Very often, manufacturers offer data center owners cloud web  services that accumulate and process data from many customers. Such systems generalize the experience of operating different data centers, so they work better than local products.

IT infrastructure management

HPE is promoting InfoSight cloud predictive analysis web service for 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 readings of 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, exhaustion of storage capacity, reduced performance of virtual machines, etc.d.) even before their occurrence.
For predictive analytics, VoltDB software is deployed in the cloud, using autoregressive forecasting models and probabilistic methods. A similar solution is also available for hybrid storage systems of Tegile Systems: the IntelliCare Cloud Analytics cloud service monitors the status, performance and resource usage of devices. Dell EMC also uses artificial intelligence and machine learning technologies in its solutions for high-performance computing. There are many similar examples, and almost all leading manufacturers of computing equipment and data storage systems are now following that path.

Power supply and cooling

Another area of application of AI in data centers is related to the management of engineering infrastructure and, above all, to cooling, the share of which in the total energy consumption of an object can exceed 30%. Google Corporation was one of the first to think about smart cooling: in 2016, together with DeepMind, it developed an artificial intelligence system for monitoring individual data center components, which allowed for a 40% reduction in energy consumption for air conditioning.
Initially, it only gave hints to the staff, but was subsequently modified and can now control the cooling of the machine rooms independently. A neural network deployed in the cloud processes data from thousands of indoor and outdoor sensors: it makes decisions taking into account the load on web  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 the staff can always turn off the automatic mode and start controlling the cooling manually. Nlyte Software, together with the IBM Watson team, has created a solution that collects data on temperature and humidity, energy consumption and workload of IT equipment. It allows you to optimize the work of engineering subsystems and does not require connection to the cloud infrastructure of the manufacturer — if necessary, the solution can be deployed directly in the data center.

Other examples

There are a lot of innovative smart solutions for data centers on the market and new ones are constantly appearing. Wave 2 Wave has created a robotic fiber-optic cable switching system for automated organization of cross-connections in traffic exchange nodes (Meet Me Room) inside the data center. The system developed by ROOT Data Center and LitBit is also used for monitoring backup DGS, and Romanet has made a self-learning software solution for infrastructure optimization.
The solutions created by Vigilant use machine learning to predict failures and optimize the temperature regime in the data center premises. The introduction of artificial intelligence, machine learning and other innovative technologies for process automation in data centers 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 manage them effectively manually.


The Japanese company NTT Communications is creating robots that will be able to web service data centers and take over some of the tasks from engineers.
In the future, the company sees a data center where there will be no people at all, and machines will do all the business.
According to Asian News International, the number of data centers is growing rapidly, and at the same time the need to manage them efficiently and safely (in a pandemic) is growing. And robots will be able to help here, which will not only remove routine tasks from people, but also reduce labor and maintenance costs.

Now NTT Communications presents a robot that does not yet know how to solve complex tasks (such as cable maintenance in server rooms), but will already be able to work in the reception area: detect an object and observe it. For example, a robot with facial recognition technology will be able to recognize a pre-registered visitor to the data center and guide him to the place. In addition, robots will be able to inspect the equipment in the data center and monitor its operation.


Alibaba engineers have created a more advanced system. The second-generation Tianxun robot developed by the Chinese operates on the basis of artificial intelligence and has the ability to work without human intervention, automatically replacing any faulty hard drives.
The entire replacement process, including automatic inspection, detection of a faulty disk, removal of the disk and installation of a new one, is performed quickly and smoothly, taking four minutes.

The social network Facebook is also experimenting with robots. In 2020, it turned out that the Internet company has a team of robotics specialists on staff, which since 2019 has been designing "robotic solutions for automating and scaling the procedures for operating the infrastructure of Facebook data centers." Among the well-known projects are robots that can move inside the data center, observing the state of the environment (similar to the IBM project).

The Switch colocation provider is betting on a future in which robotics will play a much more important role in the data center industry.
The company is developing its own robot Switch Sentry, which is, in fact, a mobile camera with a 360-degree view and thermal sensors. The device on a wheeled platform can additionally be used as a security guard.
The robot moves autonomously, but data center operators can control the machine remotely when an incident occurs. Representatives of the company announced Switch's intention to turn the design and manufacture of robots for data centers into a new business direction, offering products to other companies.