International Business Machines Corporation

Cisco Technology, Inc.

Qing Wang

Larisa Shwartz

Jinho Hwang

Michael Elton Nidd

Srinivasan Parthasarathy

Frank Bagehorn

Ota Sandr

Altynbek Orumbayev

Jakub Krchák

Michal Mýlek

Tomás Ondrej

Michal Mylek

Seema Nagar

Ajay Gupta

Rohit Madhukar Khandekar

Pooja Aggarwal

Hailei Sheng

Tengfei Ma

Hongtan Sun

Guangbing Liu

Bing Zhou

Randall M George

Jianzhong Ding

Boris Sobolev

Tomáš Ondrej

Localizing a faulty microservice in a microservice architecture is achieved by developing healthy execution sequence data for comparison to execution sequences during system failures. Oftentimes the faulty microservice does not emit a failure signal. Frequent sub-sequences arising from log template time series data during healthy execution facilitates localization of faulty services when there is no failure signal from the faulty service.

Fault localization in a distributed computing system

A method, a computer system, and a computer program product for a shiftleft topology construction is provided. Embodiments of the present invention may include collecting datasets. Embodiments of the present invention may include extracting topological entities from the datasets. Embodiments of the present invention may include correlating a plurality of data from the topological entities. Embodiments of the present invention may include mapping the topological entities. Embodiments of the present invention may include marking entry points for a plurality of subgraphs of the topological entities. Embodiments of the present invention may include constructing a topology graph.

Shiftleft topology construction and information augmentation using machine learning

Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can dynamically determine one or more endpoints to fulfill a user request. Embodiments of the present invention can select the dynamically determined one or more endpoints as the one or more endpoints that fulfill the user request. Embodiments of the present invention can execute the selected one or more endpoints to fulfill the user request.

Just in time assembly of transactions

A method, a computer system, and a computer program product for mapping operational records to a topology graph. Embodiments of the present invention may include generating an event frequent pattern using operational records. Embodiments of the present invention may include integrating topology-based event frequent patterns. Embodiments of the present invention may include mapping the operational records with an embedding engine. Embodiments of the present invention may include predicting incident events. Embodiments of the present invention may include receiving labeled patterns to the embedding engine for an active learning cycle.

Automatic mapping of records without configuration information

Systems, computer-implemented methods, and computer program products that facilitate synthetic system fault generation are provided. According to an embodiment, a system can comprise a processor that executes the following computer-executable components stored in a non-transitory computer readable medium: a generator component that employs a trained artificial intelligence (AI) model to generate a synthetic system fault, represented as a combination of discrete parameters and continuous parameters that define a system state; and a fault assembler component that analyzes the synthetic system fault and generates textual content corresponding to the synthetic system fault.

Synthetic system fault generation

In an approach to linking operational data with issues, a new event is received. The new event is associated to a story, where the story is related to an identified problem within the system, and further where the new event is associated with the story using machine learning techniques. The story is associated to related change requests based on a similarity between the story and related change requests, where the similarity between the story and the related change requests is associated using the machine learning techniques. A cost is calculated for the story. Responsive to associating the new event with a specific change request, the priority of the specific change request is updated based on the cost for the story.

Linking operational events with system changes

An approach to recommending corrective action to computing system event errors. The approach may include generating a textual description of an event error. The approach may include transforming the textual description into feature vectors with a domain-specific word embedding module. The approach may also include generating a recommendation to correct the event error based on an analysis of the feature vectors. Additionally, the recommendation may be presented for verification.

Computing system event error corrective action recommendation

A computer implemented method for identifying an application topology includes identifying a sandbox environment corresponding to an application of interest, analyzing the sandbox environment to identify a set of communication links between services within the sandbox environment indicating a first topology, identifying a production system corresponding to the application of interest, querying the production system to identify a set of structural dependencies indicating a second topology, and creating a complete topology of the cloud application by combining the first topology and the second topology. A computer program product and computer system for identifying an application topology are additionally disclosed herein.

Application topology discovery

A computer system, non-transitory computer storage medium, and a computer-implemented method of assessing technical risk using visual pattern recognition in an Information Technology (IT) Service Management System. A data visualization engine and a time series generation engine receive the operational data, respectively. A first representation of the data is generated by the data visualization engine, and a second representation of the data is generated by the time series generation engine. Anomaly patterns are identified by a pattern recognition engine configured to perform feature extraction and data transformation. An ensembler is configured to accept the outputs from two AI anomaly engines and make a final decision of whether anomaly patterns are captured. Risk scores based on the identified anomaly patterns are output by a pattern recognition engine to an automated management system. The anomalies includes information regarding vulnerabilities of devices or components of the IT Service Management System.

Assessing technical risk in information technology service management using visual pattern recognition

In accordance with one example, a presenter device is configured to participate in a collaboration session with one or more attendee devices. A change in an image displayed at a screen of the presenter device is detected and captured to produce a captured image. The captured image is compressed to form a compressed lossless file and first and second compressed lossy files. The compressed lossless file and the first compressed lossy file are transmitted to the one or more attendee devices, and a determination is made as to whether a further change to the image displayed at the screen occurs within a predetermined period of time. The compressed lossy file is transmitted to the one or more attendee devices when no further change to the image displayed at the screen occurs within the predetermined period of time.

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Chappaqua, NY, United States of America

Qing Wang