Patent AU-2020201712-A1 (AU)
Title: Prescriptive analytics based committed compute reservation stack for cloud computing resource scheduling
Inventor: SRINIVASAN MADHAN KUMAR
IPC: G06F9/00
Filed: 2020-03-06
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. 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