{"id":33095,"date":"2019-10-31T21:50:42","date_gmt":"2019-10-31T18:50:42","guid":{"rendered":"https:\/\/prohoster.info\/blog\/roboty-v-data-tsentre-chem-mozhet-byt-polezen-iskusstvennyj-intellekt\/"},"modified":"2019-10-31T21:50:42","modified_gmt":"2019-10-31T18:50:42","slug":"roboty-v-data-tsentre-chem-mozhet-byt-polezen-iskusstvennyj-intellekt","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/administrirovanie\/roboty-v-data-tsentre-chem-mozhet-byt-polezen-iskusstvennyj-intellekt","title":{"rendered":"Robots in the data center: how can artificial intelligence be useful?","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

In the process of digital transformation, humanity is building more and more data centers. These data centers themselves must also transform: issues of their resilience and energy efficiency are more important than ever. The facilities consume vast amounts of electricity, and failures of the critical IT infrastructure housed within them can be costly for businesses. Engineers are turning to artificial intelligence and machine learning technologies \u2014 in recent years, these are increasingly being used to create more advanced data centers. This approach improves the readiness of facilities, reduces failure rates, and lowers operational costs.<\/p>\n

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Initially, a check is performed: does the client device support power via PoE? A voltage of 2.8 to 10 volts is supplied, and the input resistance is determined. If the results obtained are satisfactory for powering via PoE, the power device proceeds to the next stage.<\/h2>\n

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Artificial intelligence and machine learning technologies are used to automate operational decision-making based on data collected from various sensors. Typically, such tools are integrated with DCIM (Data Center Infrastructure Management) systems and allow for predicting the occurrence of emergencies, as well as optimizing the performance of IT equipment, engineering infrastructure, and even maintenance personnel. Often, manufacturers offer data center owners cloud services that accumulate and process data from multiple clients. These systems aggregate the operational experiences of different data centers, which makes them more effective than local products.<\/p>\n

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IT infrastructure management<\/h2>\n

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HPE promotes a predictive analytics cloud service InfoSight<\/a><\/noindex> 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 readings from installed sensors in the hardware, processing over a million events per second while constantly learning. The service not only identifies faults but also predicts potential issues with the IT infrastructure (hardware failures, capacity exhaustion of storage systems, decreased performance of virtual machines, etc.) before they occur. For predictive analytics in the cloud, VoltDB's software is deployed, utilizing autoregressive forecasting models and probabilistic methods. Such solutions are also available for hybrid data storage systems from Tegile Systems: the IntelliCare Cloud Analytics cloud service monitors the state, performance, and resource usage of devices. Artificial intelligence and machine learning technologies are also employed by Dell EMC in their high-performance computing solutions. Many such examples exist, and nearly all leading hardware and data storage system manufacturers are now following this path.<\/p>\n

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Power Supply and Cooling<\/h2>\n

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Another application of AI in data centers is related to managing engineering infrastructure, primarily regarding cooling, which can account for over 30% of the total energy consumption of the facility. One of the first to consider smart cooling was Google: in 2016, in collaboration with DeepMind, it developed an artificial intelligence system<\/a><\/noindex> for monitoring individual components of the data center, which allowed for a 40% reduction in cooling energy costs. Initially, it only provided hints to the staff, but was later improved and can now manage the cooling of server rooms independently. Deployed in the cloud, the neural network processes data from thousands of internal and external sensors: it makes decisions based on server load, temperature, outdoor wind speed, and many other parameters. The instructions provided by the cloud system are sent to the data center and are checked again for safety by local systems, while staff can always disable the automatic mode and start managing the cooling manually. Nlyte Software, together with the IBM Watson team, created the decision<\/a><\/noindex>, which collects data on temperature and humidity, energy consumption, and the load on IT equipment. It allows for the optimization of the engineering subsystems and does not require connection to the manufacturer's cloud infrastructure \u2014 if necessary, the solution can be deployed directly in the data center. <\/p>\n

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Other examples<\/h2>\n

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There are many innovative smart solutions for data centers on the market, with new ones constantly emerging. Wave2Wave has created a robotic system for switching fiber optic cables to automate the organization of cross connections in Meet Me Rooms inside the data center. The system developed by ROOT Data Center and LitBit uses AI to monitor backup diesel generators, while Romonet has developed a self-learning software solution for infrastructure optimization. Solutions created by Vigilent utilize machine learning to predict failures and optimize temperature control in data center rooms. The implementation 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 directions for the industry's development. Modern data centers have become too large and complex to be efficiently managed manually.<\/p>\n

Source: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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