US 12,436,859 B2
Server network resource reallocation
Robert Martin Tougher, Snoqualmie, WA (US); Randy Lehner, Redmond, WA (US); and Daniel Christopher Gidycz, Seattle, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Nov. 10, 2023, as Appl. No. 18/388,683.
Application 18/388,683 is a continuation of application No. 17/107,129, filed on Nov. 30, 2020, granted, now 11,853,185.
Prior Publication US 2024/0078162 A1, Mar. 7, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 15/173 (2006.01); G06F 9/455 (2018.01); G06F 9/50 (2006.01); G06F 11/07 (2006.01); G06F 11/30 (2006.01); G06F 11/32 (2006.01)
CPC G06F 11/3024 (2013.01) [G06F 11/076 (2013.01); G06F 11/3017 (2013.01); G06F 11/3075 (2013.01); G06F 11/328 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
accessing a source data set that indicates processor utilization rates of a plurality of servers over a plurality of sampling periods, wherein each row of the source data set comprises a source service attribute, a source region attribute, an identifier of a virtual machine, a source time stamp attribute indicating a time the periodic processor utilization sample was taken, and a processor value attribute that represents an average processor utilization rate within the periodic processor utilization sample;
defining a target data set that comprises a plurality of processor utilization range buckets corresponding to the plurality of sampling periods, wherein each row of the target data set comprises a target service attribute, a target region attribute, a target time stamp attribute, and a bucket attribute indicating a count of samples of processor utilization rates being within a processor utilization range bucket;
generating an updated target data set based on the source data set by incrementing the stored count in one of the plurality of processor utilization range buckets upon each new sample whose processor utilization rate falls within the utilization range corresponding to that bucket; and
generating a graphical user interface (GUI) based on the updated target data set, the GUI indicating percentages of samples corresponding to the processor utilization range buckets over time.