Amid a global economic slowdown, companies are doing everything they can to cut costs. Energy consumption for data centres is topmost on everyone’s minds across the globe. So, optimising data centre costs amid rising demand for digital services is going to be crucial for 2020.
A data center building is a place where there are tens of components like servers, cooling gear, and networking, among others. Running of the data center is controlled by a combined functioning of all data center components, allowing several designs to coordinate together.
This, in turn, needs vital means to keep them cool. In the past, this has driven businesses to make them at sea or picking locations with lower temperatures, like in Europe. At the same time, AI can also help decrease the amount of power data centers use for cooling, regardless of the location.
Why This Is Important
Energy consumption by data centers may become a costly affair for the tech industry. Along with these data centers, companies also need to hire skilled specialists to maintain and monitor data centers. Operating data centers and hiring staff can be expensive for every organisation.
Furthermore, supervising and managing teams is an extra task. Therefore, organisations are continually looking for better options for conventional manual systems.
Even though there are cooling systems, nonetheless, they cannot run at the ideal effectiveness point. New AI-based systems leverage deep learning to figure out how to draw the fitting relationships between different types of cooling gear with IT workloads and condition factors.
AI frameworks accomplish this by breaking down a huge measure of historical data and their effect on energy utilisation to produce predictive modeling, using specific parameters that are transmitted to different control frameworks. By gathering data from the power supply framework, AI-fueled systems can foresee approaching equipment failures to caution staff early.
What Google Did
In this regard, Google has also figured out the best way to alter cooling frameworks — fans, ventilation, and other hardware — to bring down power utilisation. Google’s system recently made proposals to its data center administrators, leading to around 40% cost cuts in those cooling frameworks.
Developed in collaboration with DeepMind, Google’s algorithm employs a method known as reinforcement learning, which learns by trial and error. This led to AlphaGo, the DeepMind program which defeated human professionals of the board game Go.
DeepMind served its new algorithm data collected from Google data centers and allowed it to discover what cooling systems would decrease energy expenditure. The project could produce millions of dollars in electricity savings and may help the company reduce its carbon footprint, according to Google.
