Patent AU-2020201712-B2 (AU)
Title: Prescriptive analytics based committed compute reservation stack for cloud computing resource scheduling
Inventor: SRINIVASAN MADHAN KUMAR
IPC: G06Q10/06
Filed: 2020-03-06
Granted: 2022-01-06
Abstract: A system including network interface circuitry configured to receive historical 
utilization data for a set of virtual machines, receive consumption metric data for the set 
of virtual machines, receive tagging data defining a functional grouping for a first virtual 
machine of the set of virtual machines, send a reservation matrix to a host interface 
configured to control static reservation and dynamic requisition for at least the first virtual 
machine, reservation circuitry in data communication with the network interface circuitry, 
the reservation circuitry configured to execute a committed compute reservation (CCR) 
stack, the CCR stack including a data staging layer, an input layer, a transformation layer, 
an iterative analysis layer, and a prescriptive engine layer, the CCR stack configured to 
obtain, via a data control tool at the input layer, the historical utilization data, the 
consumption metric data, and the tagging data, store, at the data staging layer, the 
historical utilization data, the consumption metric data, and the tagging data, access, at 
the transformation layer, the historical utilization data and the consumption metric data 
via a memory resource provided by the data staging layer, process, at the transformation 
layer, the historical utilization data and the tagging to generate a time-mapping of active 
periods for virtual machines within the functional grouping, store, via operation at the data 
staging layer, the time-mapping, obtain, at the iterative analysis layer, a current CC state 
for the set of virtual machines, where the CCR stack is configured to obtain the current 
CC state based on an initial CC state describing a pre-analysis distribution of static 
reservation and dynamic requisition, and the transformation layer is configured to obtain 
the initial CC state from an activation timetable generated using an activation timetable 
stack, access, at the iterative analysis layer, the consumption metric data, within an 
analysis space of CC states, use non-linear search to generate a search region around 
the current CC state, based on the time-mapping, the consumption metric data, and the 
previous CC state, determine a consumption-constrained CC state including a static 
reservation prescription for at least the first virtual machine, store, via operation at the 
data staging layer, the consumption-constrained CC state, access the consumption
constrained CC state at the prescriptive engine layer, analyze the consumption
constrained CC state responsive to a feedback history based on commands from a CC 
control interface, responsive to analyzing the consumption-constrained CC state, 
generate, at the prescriptive engine layer, a CC-window presentation for the CC control 
interface, and after generation of the CC-window presentation, generate the reservation 
matrix based on at least the consumption-constrained CC state and the feedback history.
100 
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Data Staging Layer 105 
Input Layer 110 
Transformation Layer 120 
Iterative Analysis Layer 122 130 
Prescriptive Engine Layer 150 
CC Control Interface 154 
CC-Window Presentation 156 
Reservation Matrix 158 
Data Export Layer 160 
JSON L XML TBWX 
CSV ] F H TML 
Flat File L Streaming Web Service 
Fire 1