US 12,481,532 B2
Identifying hotspots and coldspots in forecasted power consumption data in an it data center for workload scheduling
Mantej Singh Gill, Bangalore (IN); Dhamodhran Sathyanarayanamurthy, Bangalore (IN); and Arun Mahendran, Bangalore (IN)
Assigned to Hewlett Packard Enterprise Development LP, Spring, TX (US)
Filed by HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP, Houston, TX (US)
Filed on Jul. 12, 2022, as Appl. No. 17/862,989.
Prior Publication US 2024/0020157 A1, Jan. 18, 2024
Int. Cl. G06F 9/50 (2006.01); G06N 20/00 (2019.01)
CPC G06F 9/5027 (2013.01) [G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computing component, comprising:
at least one processor; and
a memory unit operatively connected to the at least one processor, the memory unit including instructions that, when executed, cause the at least one processor to:
train a machine learning model to obtain forecasted power consumption data of a server for a next future time period with at least one dataset, received from the server, of input periodic data from a past time period;
calculate an exponential mean average (EMA) of the forecasted power consumption data;
compare points of the forecasted power consumption data to the EMA;
identify consecutive points of forecast power consumption above the EMA as a time window of hotspots and consecutive points of forecast power consumption below the EMA as a time window of coldspots; and
schedule a workload to be executed during the time window of coldspots.