公开(公告)号：US11886864B1

公开(公告)日：2024-01-30

申请号：US17813048

申请日：2022-07-18

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Chander Govindarajan ,  Kavya Govindarajan ,  Mudit Verma

IPC: G06F8/65

CPC classification number: G06F8/65

Abstract: Edge application deployment in a network is provided. The network includes a plurality of edge sites with edge computing infrastructure. Edge application deployment is performed, including deploying a pseudo application instance (pApp) of the edge application at each edge site of a first group of edge sites of the plurality of edge sites, and deploying a real application instance (rApp) of the edge application at each edge site of a second group of one or more edge sites of the plurality of edge sites. The pApp is a lightweight, application-specific instance of the rApp with less application functionality than the rApp. Further, the first group of edge sites is larger than the second group, and a user device interaction with the edge application is through a selected pApp of the first group of edge sites to an rApp of the second group.

公开(公告)号：US20230176939A1

公开(公告)日：2023-06-08

申请号：US17541453

申请日：2021-12-03

Applicant: International Business Machines Corporation

Inventor： Joshua M. Rosenkranz ,  Pranita Sharad Dewan ,  Mudhakar Srivatsa ,  Praveen Jayachandran ,  Chander Govindarajan ,  Priyanka Prakash Naik ,  Kavya Govindarajan

IPC: G06F11/07

CPC classification number: G06F11/076 ,  G06F11/079 ,  G06F11/0709

Abstract: An ensemble of autoencoder models can be trained using different seeds. The trained ensemble of autoencoder models can be run on new time series data to generate a prediction associated with the new time series data. The new time series data can include multiple dimensions per time step. Reconstruction errors can be determined for the prediction. Dimensions having highest reconstruction errors can be selected among the multiple dimensions based on a threshold. The prediction can be segmented based on bursts of the reconstruction errors over time, where temporal segments can be obtained. At least one common pattern including a set of dimensions among the selected dimensions across the temporal segments can be obtained to represent a failure fingerprint.

公开(公告)号：US11656927B1

公开(公告)日：2023-05-23

申请号：US17541453

申请日：2021-12-03

Applicant: International Business Machines Corporation

Inventor： Joshua M Rosenkranz ,  Pranita Sharad Dewan ,  Mudhakar Srivatsa ,  Praveen Jayachandran ,  Chander Govindarajan ,  Priyanka Prakash Naik ,  Kavya Govindarajan

IPC: G06F11/07

CPC classification number: G06F11/076 ,  G06F11/079 ,  G06F11/0709

Abstract: An ensemble of autoencoder models can be trained using different seeds. The trained ensemble of autoencoder models can be run on new time series data to generate a prediction associated with the new time series data. The new time series data can include multiple dimensions per time step. Reconstruction errors can be determined for the prediction. Dimensions having highest reconstruction errors can be selected among the multiple dimensions based on a threshold. The prediction can be segmented based on bursts of the reconstruction errors over time, where temporal segments can be obtained. At least one common pattern including a set of dimensions among the selected dimensions across the temporal segments can be obtained to represent a failure fingerprint.