for server landscapes
The operation of server landscapes in companies results in enormously high energy consumption. Since servers are usually inadequately adapted to the actual workload, server landscapes are exposed to unnecessary excess consumption.
The goal was to develop a small hardware box that serves as a central collection point for all energy indicators for the operation of servers and their corresponding energy consumption during data center operation.
This hardware box can use artificial intelligence to make smart forecasts and initiate measures that highlight the individual energy-saving potential for companies from a financial and energy perspective. This not only makes more efficient management of these servers, but also reduces the burden on administrative staff and the environment.
In practice, the workloads of server landscapes are constantly changing. Thus, the Green-IT KI-Box should shut down a server landscape to minimum operation, if a decrease in utilization has been reported or forecast. As a result, the CO2 emissions of server landscapes are reduced and cost savings are achieved as energy consumption is minimized.
In enterprises, servers have significantly higher workloads during core working hours and very few servers are actually used overnight. In reality, however, almost all servers are preconfigured by manufacturers to perform as well as possible in performance tests. Only very few companies employ their own administrators who make the appropriate settings or reconfigure server/application landscapes every day after office hours or before and after the weekend.
Thus, server monitoring by the Green-IT KI-Box offers CO2 savings and accordingly extreme cost savings.
The box manages, collects and evaluates all energy figures with the help of AI algorithms, so that savings potentials become visible and consequently fully automated control commands can be sent sensibly. The box as a physical device would be comparable to firewall applications and necessary to ensure the highest possible level of data protection. In addition, only a physical box can ensure that, in the event of a fault, all the settings necessary for the proper operation of servers can be made and, if necessary, the system can be reset to a „default“ state. All consumption figures would thus remain protected within the server cabinet and not be outsourced to third-party cloud services.
The following is an example based on an online store:
A study published by A. Poleshova in 2018 addressed the question of when consumers prefer to store in online stores. The resulting results clearly show that the fewest customers purchase goods online between 0:00 and 06: a.m.. If we compare the more active time periods with the most visitors (6 p.m. to midnight) with the time period in which the fewest visitors store online (0 a.m. to 6 a.m. and 6 a.m. to 12 a.m.), it quickly becomes apparent that there is enormous savings potential here.
This means that server activity can be adapted to consumer activity.
Such energy-efficient operation on the individual servers can be switched on and off fully autonomously by the Green-IT AI box. Peaks can be calculated based on forecasts. This ensures that there are no performance problems at peak times and that secure operation is still guaranteed. Such peaks can currently be read from the metrics of all applications and servers. The intelligent forecast using artificial intelligence is calculated directly on the Green-IT AI box. For this purpose, it is essential to adapt the algorithms to the components and chips installed within the box. This is the only way to ensure energy-efficient operation while at the same time being able to perform the necessary, highly complex calculations.
In figures, this would mean a total saving of approx. 8.377 t CO2* per year. At current electricity prices (February 2022), this would be a total saving of around €4,000 per year.
Since servers do not have to be set to maximum load even at peak times, it can be assumed that in a real environment the savings potential is higher than the value calculated here.
*Values based on a data center with ten servers.