Companies around the world are testing machine learning technologies for data center optimization.
Thereby to oversee and automate cooling infrastructure, power distribution elements, rack system, and physical security.
Artificial intelligence is able to simplify the management of complex computing facilities and allows data processing centers to function more efficiently and autonomously.
Artificial intelligence in data centers, for now, revolves around the use of machine learning, which comes from the idea that to perform specific tasks, the equipment can learn without being programmed.
i.e. algorithms that collect data, they learn from them and make determinations or predictions. Deloitte Global predicts machine learning deployments will double from 2017, and the numbers will increase again by 2020.
The efficient running of data centers is one of the biggest concerns for IT managers. Increasing performance, reducing, or minimizing future problems should be the key objectives.
One should always take into account the company’s growth projection and available budget. Below, highlighted are some of the main concerns regarding the optimization of data centers.
- Regulatory constraints
- Cost reduction
- Environmental responsibility
- Optimization of energy consumption
The data center is an economic and strategic organ of the company. However, it is complex by the multitude of technical environments such as electricity, fire, air conditioning, access control, alarms and often requires multiple specialized skills.
ML-driven systems have the potential to identify vulnerabilities, contribute to predictive and preventive maintenance, and drive efficiencies in data center operations that manual processes cannot.
At Delta Airlines Data Processing Center, for example, that was attributed to the power failure which caused a halt of around 2,000 flights over a three-day period in 2016 and cost the airlines about $150 million.
This is exactly the kind of scenario that machine learning-based automation could avoid.
As conditions change, the machine learning enhanced systems learn from the changes. It is trained to adjust rather than use specific programming instructions to perform tasks.
Machine learning really involves complex but repetitive decisions and automating them in a new way, and when you think about it that way, it’s hard to think of an area where it will not have any impact.
Machine learning is being used to improve energy efficiency, mainly by controlling temperature and adjusting cooling systems.
An example of ML-driven intelligence is condition-based maintenance that is applied to consumable items in a data center, such as cooling filters.
According to Gartner, 10 percent of operating expenses come from energy, one of the top priorities for corporate data center owners.
Optimizing energy use is definitely one of the top concerns, as electricity costs account for most of the costs of this type of infrastructure.
Energy costs increase by about 10 percent per year, as a result of the higher cost per kWh and the implied demand especially for high-energy servers.
In addition, about 10 percent of a data center’s operating expenses are energy, which will rise to 15 percent over the next five years.
Optimizing the data center with machine learning can bring many benefits to the company, including reduced electricity bills.
This is because energy-efficient local servers have been replaced by more modern and economic servers. Not to mention that all the information would be stored in one place.
Google’s temperature and cooling controls of its various data centers are handled by an artificial intelligence system that is responsible for gathering data and providing feedback with recommendations and the adoption of efficient energy consumption practices.
Following the footsteps of Google, Huawei a Chinese MNC that provides telecommunications equipment is beginning with mere practical measures.
Huawei is using pattern matching and AI to isolate and identify faults, spot evidence of refrigerant leaks, and to control the temperature.
In the future, the widespread application of machine learning in data Centers will provide companies with more insight as data processing centers make more informed decisions.
Data Center Infrastructure Management (DCIM) is an important and innovative tool for effectively managing and monitoring a data center.
It is a solution that enables much more efficient scope and action than conventional management functions, since it brings together, in a single and real-time management.
The IT and infrastructure areas, aiming at centralizing monitoring, control, and planning of critical systems, which provides a complete view of the project and performance of a data center in real-time.
By correlating data such as power or temperature resources to IT equipment and systems, DCIM provides proactive management, collecting or storing information, and issuing custom reports, enabling enterprises to identify and troubleshoot a data’s physical infrastructure center with minimal human intervention.
With increased management efficiency, ensuring higher availability of data center and thus optimizing equipment performance, another important consequence of the tool is reduced power consumption.
And, as if this full monitoring was not enough, there is still the possibility of real-time environmental supervision via tablets or mobile phones.
DCIM, therefore, is a strategic tool that replaces solutions that are often incompatible with each other; and enables truly efficient and secure management, which is essential for business continuity and growth.