公开(公告)号：US20240330096A1

公开(公告)日：2024-10-03

申请号：US18128370

申请日：2023-03-30

Applicant: International Business Machines Corporation

Inventor： Saurabh Jha ,  Larisa Shwartz ,  Robert Filepp ,  Frank Bagehorn ,  Jesus Maria Rios Aliaga

IPC: G06F11/07 ,  G06F11/30

CPC classification number: G06F11/079 ,  G06F11/3089

Abstract: Mechanisms are provided that detect an anomaly in performance of a hybrid application based on a specification of required performance and collected passive monitoring data, and that generate a causal generative model based on relationships between hybrid application components and computing system architecture components extracted from the passive monitoring data. Root cause identification (RCI) logic is executed on the causal generative model to identify a set of candidate root causes of the detected anomaly. One or more probes are identified for active monitoring data collection targeting the identified set of candidate root causes, which are then executed to collect probe data. Reinforcement learning is performed of the RCI logic to update the RCI logic based on the probe data. The set of candidate root causes is updated based on the reinforcement learning.

公开(公告)号：US20240385918A1

公开(公告)日：2024-11-21

申请号：US18198492

申请日：2023-05-17

Applicant: International Business Machines Corporation

Inventor： Saurabh Jha ,  Larisa Shwartz ,  Frank Bagehorn

IPC: G06F11/07 ,  G06F9/50

Abstract: Mechanisms are provided for migrating an application to a new cloud computing system. A causal model is generated based on configuration parameters for a first cloud computing system, monitoring data collected for an execution of the application in the first cloud computing system, and an inserted causal layer. Chaos engineering logic is executed on the causal model to perform a fault injection on the configuration parameters to emulate a second cloud computing system configuration. A mapping, by the causal layer, of the configuration parameters to the monitoring data is learned based on the fault injection. An artificial intelligence for information technology operations (AIOps) model is updated, based on the learned mapping of the causal layer, for monitoring the application execution in the new cloud computing system. The updated AIOps model is provided to an observability tool executing on the new cloud computing system.

公开(公告)号：US20240311201A1

公开(公告)日：2024-09-19

申请号：US18185134

申请日：2023-03-16

Applicant: International Business Machines Corporation

Inventor： Saurabh Jha ,  Larisa Shwartz ,  Frank Bagehorn

IPC: G06F9/50

CPC classification number: G06F9/505 ,  G06F9/5072

Abstract: In an approach to improve enhancing the provisioning cloud resources, embodiments receive a set of potential cloud resource providers and predict a performance for a distributed workload on each potential provider of the set according to a machine learning model. Additionally, embodiments inject fault into the set of potential cloud resource providers and measure an impact of the injected fault upon system performance. Embodiments utilize the injected fault to create a system intervention to ensure that the system intervention is carried out on a network in predetermined system. Further, embodiments provision the distributed workload among the potential cloud resource providers according to dynamic conditions output by the set of potential cloud resources based on the measured impact of the injected fault.

公开(公告)号：US20210286699A1

公开(公告)日：2021-09-16

申请号：US16818656

申请日：2020-03-13

Applicant: International Business Machines Corporation

Inventor： Saurabh Jha ,  Amos A. Omokpo ,  Karthick Rajamani ,  HariGovind Venkatraj Ramasamy

IPC: G06F11/34 ,  G06F17/18 ,  G06F9/54 ,  G06N20/00

Abstract: An embodiment includes extracting statistical data associated with invocation of an application programming interface (API) from a log and using the statistical data to calculate a performance value and generate an aggregate dataset that combines the performance value with performance values associated with other invocations of the API. The embodiment includes calculating metric values for performance values for respective time intervals of a time period and calculating mean and standard deviation values of the metric values for the time period. The embodiment includes selecting the API as a candidate API and detecting a Customer Impacting Event (CIE) by applying a machine learning algorithm using monitored values associated with the candidate API during a time frame defined by a rolling window. The embodiment also includes automatically initiating a selected alert from among a plurality of alert options based at least in part on the monitored values associated with the CIE.

公开(公告)号：US11256598B2

公开(公告)日：2022-02-22

申请号：US16818656

申请日：2020-03-13

Applicant: International Business Machines Corporation

Inventor： Saurabh Jha ,  Amos A. Omokpo ,  Karthick Rajamani ,  HariGovind Venkatraj Ramasamy

IPC: G06F11/34 ,  G06F17/18 ,  G06F9/54 ,  G06N20/00

Abstract: An embodiment includes extracting statistical data associated with invocation of an application programming interface (API) from a log and using the statistical data to calculate a performance value and generate an aggregate dataset that combines the performance value with performance values associated with other invocations of the API. The embodiment includes calculating metric values for performance values for respective time intervals of a time period and calculating mean and standard deviation values of the metric values for the time period. The embodiment includes selecting the API as a candidate API and detecting a Customer Impacting Event (CIE) by applying a machine learning algorithm using monitored values associated with the candidate API during a time frame defined by a rolling window. The embodiment also includes automatically initiating a selected alert from among a plurality of alert options based at least in part on the monitored values associated with the CIE.