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公开(公告)号：US12105619B2
公开(公告)日：2024-10-01
申请号：US18353529
申请日：2023-07-17
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor： Pei Jian Liu , Bing Hua Zhao , Na Liu , Yan Liu , Mei Rui Su
CPC classification number: G06F11/3688 , G06F11/3608 , G06F11/3664 , G06N5/022 , G06N5/04
Abstract: Training a predict model with network traffic and data change messages generated by an existing web application running in a production environment. The predict model being is trained to predict data changes resulted from API calls embodied in network traffic. A stream of network traffic of the existing web application is replayed with an upgraded version of the existing web application to generate real data changes. The stream of network traffic is applied to the predict model to generate predicted data change messages. The predicted data change messages are comparing with real data change messages representing the real data changes. One or more existing APIs is identified as being possibly functionally degraded based on any inconsistency of the predicted data change messages with the real data change messages.
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公开(公告)号：US20240070167A1
公开(公告)日：2024-02-29
申请号：US17893317
申请日：2022-08-23
Applicant: International Business Machines Corporation
Inventor： He Fang Zhang , Yan Liu , Meng Zhao , Hai Long Shi
CPC classification number: G06F16/273 , G06F16/2365
Abstract: An example operation may include one or more of receiving a message from an agent installed at a data replication server, the message comprising a status identifier of a checksum validation of a data replication operation, identifying a latency value associated with the data replication server, determining whether a data loss has occurred based on the status identifier of the checksum validation and the latency value, and in response to a determination that the data loss has occurred, transmitting a notification of the data loss to a computing system associated with the data replication server.
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公开(公告)号：US20240031305A1
公开(公告)日：2024-01-25
申请号：US18479394
申请日：2023-10-02
Applicant: International Business Machines Corporation
Inventor： Yi Ming Wang , Rui Wang , Jing Bo Jiang , Yan Liu
IPC: H04L47/78 , H04L47/70 , H04L47/80 , H04L47/762
CPC classification number: H04L47/781 , H04L47/829 , H04L47/801 , H04L47/762
Abstract: In an approach for proactive service group based auto-scaling, a processor collects usage data generated in one or more services in a container platform. A processor predicts access situation and resource utilization of the one or more services based on the usage data. A processor constructs a dynamic correlation topology among the one or more services based on the access situation and resource utilization. A processor identifies associated services correlated with the one or more services based on the dynamic correlation topology. A processor, in response to a service request exceeding a pre-set threshold, expands the one or more services and associated services.
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公开(公告)号：US11838304B2
公开(公告)日：2023-12-05
申请号：US17005501
申请日：2020-08-28
Applicant: International Business Machines Corporation
Inventor： Pei Jian Liu , Yan Liu , Bing Hua Zhao , Mei Rui Su , Na Liu
CPC classification number: H04L63/1425 , G06F21/64
Abstract: Methods, apparatus, computer program products for tracking sensitive data are provided. A method for tracking sensitive data comprises identifying, by one or more processing units, for a type of sensitive data, at least one key interface that carries the type of sensitive data and recording the at least one key interface. The method further comprises generating, by one or more processing units, for the type of sensitive data, for each type of sensitive data, a series of service nodes based on the at least one key interface, and monitoring, by one or more processing units, for the type of sensitive data, corresponding data traffic flowing through corresponding series of service nodes, based on the identified at least one key interface.
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公开(公告)号：US11748244B2
公开(公告)日：2023-09-05
申请号：US17551345
申请日：2021-12-15
Applicant: International Business Machines Corporation
Inventor： Pei Jian Liu , Bing Hua Zhao , Na Liu , Yan Liu , Mei Rui Su
CPC classification number: G06F11/3688 , G06F11/3608 , G06F11/3664 , G06N5/022 , G06N5/04
Abstract: Training a predict model with network traffic and data change messages generated by an existing web application running in a production environment. The predict model being is trained to predict data changes resulted from API calls embodied in network traffic. A stream of network traffic of the existing web application is replayed with an upgraded version of the existing web application to generate real data changes. The stream of network traffic is applied to the predict model to generate predicted data change messages. The predicted data change messages are comparing with real data change messages representing the real data changes. One or more existing APIs is identified as being possibly functionally degraded based on any inconsistency of the predicted data change messages with the real data change messages.
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Patent Agency Ranking
公开(公告)号：US20230155916A1
公开(公告)日：2023-05-18
申请号：US17985066
申请日：2022-11-10
Applicant: International Business Machines Corporation
Inventor： Bo Shen , Yao Dong Liu , Jing James Xu , Lei Gao , Yan Liu
IPC: H04L43/50 , G06F17/18 , H04L43/0817 , H04L43/045 , H04L43/067 , H04L43/0864
CPC classification number: H04L43/50 , G06F17/18 , H04L43/0817 , H04L43/045 , H04L43/067 , H04L43/0864
Abstract: A computer-implemented method, system and computer program product for accurately identifying an execution time of a performance test. Network latency data is grouped into clustered groups of network latency data. Furthermore, the performance test execution times for the same group of performance tests run in the local and remote cluster environments are obtained. The test execution times impacted by network latency (compensation times) are then determined based on such obtained performance test execution times in the local and remote cluster environments. Such compensation times are then grouped into clustered groups of compensation times. A regression model is built to predict a performance test execution time impacted by network latency (compensation time) using the clustered groups of network latency data and compensation times. The execution time of a performance test run in the remote cluster environment is then generated that takes into consideration the compensation time predicted by the regression model.
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公开(公告)号：US20230119654A1
公开(公告)日：2023-04-20
申请号：US17451495
申请日：2021-10-20
Applicant: International Business Machines Corporation
Inventor： Jin Wang , Lei Gao , Kai Li , A Peng Zhang , Yan Liu , Jia Xing Tang , Xin Feng Zhu
IPC: G06N20/00
Abstract: Identifying node importance in a machine learning pipeline is provided. Changes in accuracy of the machine learning pipeline are recorded for each respective node setting change in a randomly generated group of node settings inputted into each corresponding node included in the machine learning pipeline. A regression model is generated to determine a relationship between each respective node setting change in the randomly generated group of node settings inputted into each corresponding node and the changes in the accuracy of the machine learning pipeline. A node of importance is identified in the machine learning pipeline using the regression model based on the relationship between each respective node setting change in the randomly generated group of node settings inputted into each corresponding node and the changes in the accuracy of the machine learning pipeline.
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公开(公告)号：US20230119568A1
公开(公告)日：2023-04-20
申请号：US17504662
申请日：2021-10-19
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor： Jin Wang , Lei Gao , A PENG ZHANG , Kai Li , Yan Liu
Abstract: A computer-implemented method includes: obtaining, by a computing device, data from sensors that collect the data in a system during a time, wherein the data is multi-dimensional time series data; creating, by the computing device, matrices based on the data; determining, by the computing device using a first computer-based numerical modeling method, patterns based on the matrices; creating, by the computing device using a second computer-based numerical modeling method, a single time series model based on the patterns; and predicting, by the computing device, a future condition of the system using the time series model with current data of the system.
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公开(公告)号：US20250110707A1
公开(公告)日：2025-04-03
申请号：US18476435
申请日：2023-09-28
Applicant: International Business Machines Corporation
Inventor： Lei Gao , Jin Wang , A Peng Zhang , Kai Li , Yan Liu , Geng Wu Yang
IPC: G06F9/38
Abstract: Computer-implemented methods for discovery and reuse of a high value pipeline segment are provided. Aspects include defining a set of datasets associated with a processing pipeline based on a set of data operations of the processing pipeline. Aspects also include generating a library of pipeline segments based on the processing pipeline and at least one dataset of the set of datasets. In some aspects, generating the library of pipeline segments includes adding a pipeline segment of the processing pipeline to the library based on one or more characteristics of a dataset generated by the pipeline segment, where the dataset is included in the set of datasets.
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公开(公告)号：US20230359549A1
公开(公告)日：2023-11-09
申请号：US18353529
申请日：2023-07-17
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor： Pei Jian Liu , Bing Hua Zhao , Na Liu , Yan Liu , Mei Rui Su
CPC classification number: G06F11/3688 , G06F11/3608 , G06N5/04 , G06N5/022 , G06F11/3664
Abstract: Training a predict model with network traffic and data change messages generated by an existing web application running in a production environment. The predict model being is trained to predict data changes resulted from API calls embodied in network traffic. A stream of network traffic of the existing web application is replayed with an upgraded version of the existing web application to generate real data changes. The stream of network traffic is applied to the predict model to generate predicted data change messages. The predicted data change messages are comparing with real data change messages representing the real data changes. One or more existing APIs is identified as being possibly functionally degraded based on any inconsistency of the predicted data change messages with the real data change messages.
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