International Business Machines Corporation

Srinivasan Parthasarathy

Deepak Srinivas Turaga

Jinho Hwang

Larisa Shwartz

Michael Elton Nidd

Frank Bagehorn

Qing Wang

Altynbek Orumbayev

Ota Sandr

Nagui Halim

Venkata Nagaraju Pavuluri

Jakub Krchák

Yanjie Fu

Michal Mýlek

Sushma Ravichandran

Zhen Liu

Charu Chandra Aggarwal

Hao Yang

Wei Fan

Daby Mousse Sow

Fabio A Oliveira

Long H Vu

Tomás Ondrej

Olivier Verscheure

Tamar Eilam

Anand Ranganathan

Lisa Amini

Jing Gao

Udayan Khurana

Michal Mylek

Kun-Lung Wu

Dimitrios Pendarakis

Minkyong Kim

Bugra Gedik

Douglas Lee Schales

Reiner Sailer

Henrique Andrade

Josyula R Rao

Mihai Christodorescu

Deepak Rajan

Rohit Madhukar Khandekar

Mitchell Adam Cohen

Vibhore Kumar

Alain E Biem

Tengfei Ma

Mark C Johnson

Leman Akoglu

Randall M George

Tejaswini Pedapati

Wietse Z Venema

Priya A Ashok Nagpurkar

Oliver Verscheure

Tomáš Ondrej

Fabio A Oliveria

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

Systems, computer-implemented methods, and computer program products to facilitate fault localization and alert aggregation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a graph component that employs an algorithm to generate a directed graph of computing elements having performance alerts in one or more abstraction layers of a computing environment. The computer executable components can further comprise a fault localization component that employs a topological sort algorithm to identify one or more of the computing elements causing the performance alerts based on the directed graph.

Fault localization and alert aggregation

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

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

Computerized interactive feature visualization is carried out on a data set&#x2014;a plurality of insight classes rank a plurality of features of the data set. Via a computerized user interface, user feedback is obtained based on the interactive feature visualization&#x2014;a user selects and ranks a subset of the features. At least one transformation function is applied to at least one feature of the subset of features selected by the user, to automatically construct, with a computer, at least one additional feature for the data set. The data set with the at least one additional feature is a transformed data set. In some cases, a supervised task is carried out on the final data set; accuracy of a machine learning system implementing the at least one supervised task can be enhanced by the at least one additional feature, and/or a physical system can be controlled based on results of the at least one supervised task.

Mixed initiative feature engineering

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 system, computer program product, and method are provided for orchestrating a multi objective optimization of an application. A set of two or more key performance indicators (KPIs) and one or more parameters associated with the application are received. A machine learning (ML) based surrogate function learning model in combination with an acquisition function is leveraged to conduct one or more adaptive trials. Each trial consists of a specific configuration of the one or more parameters. A pareto surface of the KPIs of the application is computed based on the observations of KPI values from each adaptive trial. The pareto surface is explored and an optimal operating point is selected for the application. The application is then executed at the selected operating point.

Multi objective optimization of applications

A specification of a topology of a microservices application is obtained as a plurality of nodes corresponding to a plurality of microservices of the microservices application. The plurality of nodes includes a root node, each of the plurality of nodes other than the root node has a timeout parameter and a retries parameter. Via constrained black box optimization, optimized values are selected for the timeout parameter and the retries parameter for each of the plurality of nodes other than the root node, subject to satisfying a specified end-to-end latency for the microservices application and minimizing an error rate for the microservices application. The microservices application is configured in accordance with the optimized values. At least one external request to the root node is responded to with the microservices application configured in accordance with the optimized values.

Constrained optimization of cloud micro services

An encoding system for encoding an event time series, the system including an inter-arrival time computing device configured to transform inter-arrival times between a plurality of input events into discrete time symbols and map the input events and the discrete time symbols using a dictionary to output a time gram representing a temporal dimension between a sequences of events.

Encoding for time grams

A method and system of optimizing parameters of a microservice-based application is provided. A microservice infrastructure of the microservice-based application is determined. One or more optimization objectives related to the microservice-based application are determined. Different combinations of timeout and retry values are tested for each microservice. A reward value is calculated for each of the different combinations of timeout and retry values. The microservice infrastructure is set to a combination of timeout and retry values having a highest reward value for the one or more optimization objectives.

Auto tuner for cloud micro services embeddings

Embodiments relate to analyzing dataset. A method of analyzing data is provided. The method obtains a description of a dataset. The method automatically generates a plurality of analysis options from the description of the dataset. The method generates a plurality of queries based on the analysis options. The method deploys the queries on the dataset to build a plurality of statistical models from the dataset.

Automatic enumeration of data analysis options and rapid analysis of statistical models

An automated outlier detection system implements an unsupervised set of processes to determine feature subspaces from a dataset; determine candidate exploratory actions, where each candidate exploratory action is a specific combination of a feature subspace and a parameterized instance of an outlier detection algorithm; and identify a set of optimal exploratory actions to recommend for execution on the dataset from among the candidate exploratory actions. Outlier scores obtained as a result of execution of the set of optimal exploratory actions are processed to obtain one or more outlier views such that each outlier view represents a consistent characterization of outliers by each optimal exploratory action corresponding to that outlier view.

Automated outlier detection

Growing community of inventors

White Plains, NY, United States of America

Srinivasan Parthasarathy