Patent AU-2019202091-B2 (AU)
Title: Comparative multi-forecasting analytics service stack for cloud computing resource allocation
Inventor: SRINIVASAN MADHAN KUMAR
IPC: G06F9/50
Filed: 2019-03-26
Granted: 2020-05-28
Abstract: A system including network interface circuitry configured to access, via a data lake interaction layer of an analytics service stack, cloud computing utilization data from a data lake storage system, and send a utilization schedule adjustment command to a cloud provisioning server, and processing circuitry in data communication with the network interface circuitry, the processing circuitry configured to at an input layer of the analytics service stack receive an indication of the cloud computing utilization data, and receive an analytics model selection for generation of forecasted utilization data based on the cloud computing utilization data, responsive to the indication, cause the network interface circuitry to access the cloud computing utilization data, receive the cloud computing utilization data at a transformation layer of the analytics service stack, determine a format transformation to prepare the cloud computing utilization data for processing by a multi-forecasting engine layer of the analytics service stack, at the transformation layer, execute the format transformation on the cloud computing utilization data to generate transformed utilization data, pass the transformed utilization data to a data treatment layer of the analytics service stack, at the data treatment layer generate treated utilization data by filtering the transformed utilization data in accord with a removal criterion, and supplementing the transformed utilization data in accord with a value insertion criterion, send the treated utilization data to a data partitioning layer of the analytics service stack, at the data partitioning layer, create a test dataset and a validation dataset from the treated utilization data, receive the test dataset at the multi-forecasting engine layer, responsive to the analytics model selection, determine a selected analytics model to apply to the test dataset to generate the forecasted utilization data, receive the validation dataset and the forecasted utilization data at a comparative evaluation layer of the analytics service stack, determine an accuracy metric for the forecasted utilization data by comparing the forecasted utilization data to the validation dataset at the comparative evaluation layer, at the multi-forecasting engine layer, determine a prescriptive utilization forecast responsive to the forecasted utilization data for a prediction period, responsive to the prescriptive utilization forecast, generate a utilization schedule adjustment command, and cause the network interface circuitry to send the utilization schedule adjustment command to the cloud provisioning server to generate an adjusted cloud computing resource allocation for the prediction period. 100 Communication \ 1/10 Interface Circuitry Input Layer 110 Data Lake Interaction Layer 120 Data Lake Storage System -122 Transformation Layer 130 DataTretmen Laer 10 1User Interface 118 Data Treatment Layer 140 182 GUI Data Partitioning Layer 150 Training Dataset Test Dataset Validation Dataset 0 0 Multi-Forecasting Engine (MFE) Layer 160 FT]IETS Box-Cox EE ARI MAX MR Forecast Bo-x IARI MA Box-Cox Diff Forecast IARIMA Box-Coxl ARIMA Seasonality-Diff Analytics Models Comparative Evaluation Layer 170 MAPE 11 RMSE L MAD Evaluation Metrics Presentation Layer 180 Figure 1