International Business Machines Corporation

Rohit Madhukar Khandekar

Kun-Lung Wu

Deepak Rajan

Joel Leonard Wolf

Kirsten Weale Hildrum

Sujay Sunil Parekh

Vibhore Kumar

Maja Vukovic

Jinho Hwang

Anup Kalia

Bugra Gedik

Muhammed Fatih Bulut

Henrique Andrade

Jin Xiao

Andrey Balmin

Raghav Batta

Anshul Dawra

Ahmed Eldawy

Larisa Shwartz

Srinivasan Parthasarathy

Qing Wang

Hongtan Sun

Bing Zhou

Leman Akoglu

Sunjay S Parekh

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

Systems, computer-implemented methods, and computer program products that can facilitate creating and querying a knowledge base of identified topics, computer code actions, and parameters, 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 parameter component that can identify a result parameter in ones of one or more results, wherein the one or more results comprise topics and computer code actions. The computer executable components can further comprise a result component that can select a result of the one or more results based on a mapping of a query to the one or more results, the mapping being based on the result parameter identified in the result and a criterion.

Discovery of computer code actions and parameters

Methods, systems and computer program products for providing automated script review utilizing crowdsourced inputs are provided. Aspects include receiving a new script including a script text and a script description. Aspects include comparing the new script to each of a plurality of previously classified scripts to determine a degree of similarity. Each of the previously classified scripts and the new script have an associated set of attributes. Responsive to determining that the degree of similarity is below a predetermined threshold, aspects include mapping the new script to a crowdsourcing platform to identify a similar script. Aspects also include receiving information indicative of one or more features from the crowdsourcing platform. Responsive to inputting the one or more features into an acceptance model, aspects include generating an acceptance recommendation associated with the new script.

Automated script review utilizing crowdsourced inputs

A system, program product, and method for use with an information handling system to detect and resolve faults in a run-time environment. As faults are detected, one or more corresponding general query responses are identified and subject to a ranking based on relevance criteria. At least one modified response is transformed into a command, selectively blended with context, and encoded as a context aware instruction. The instruction is subject to testing with corresponding output being subject to measurement.

Generating context aware consumable instructions

A method for allocating parallel, independent, data tasks includes receiving data tasks, each of the data tasks having a penalty function, determining a generic ordering of the data tasks according to the penalty functions, wherein the generic ordering includes solving an aggregate objective function of the penalty functions, the method further including determining a schedule of the data tasks given the generic ordering, which packs the data tasks to be performed.

Scheduling parallel data tasks

A method, an apparatus and an article of manufacture for processing a random-walk based vertex-proximity query on a graph. The method includes computing at least one vertex cluster and corresponding meta-information from a graph, dynamically updating the clustering and corresponding meta-information upon modification of the graph, and identifying a vertex cluster relevant to at least one query vertex and aggregating corresponding meta-information of the cluster to process the query.

Vertex-proximity query processing

Techniques for scheduling one or more MapReduce jobs in a presence of one or more priority classes are provided. The techniques include obtaining a preferred ordering for one or more MapReduce jobs, wherein the preferred ordering comprises one or more priority classes, prioritizing the one or more priority classes subject to one or more dynamic minimum slot guarantees for each priority class, and iteratively employing a MapReduce scheduler, once per priority class, in priority class order, to optimize performance of the one or more MapReduce jobs.

Scheduling MapReduce jobs in the presence of priority classes

Techniques for scheduling multiple flows in a multi-platform cluster environment are provided. The techniques include partitioning a cluster into one or more platform containers associated with one or more platforms in the cluster, scheduling one or more flows in each of the one or more platform containers, wherein the one or more flows are created as one or more flow containers, scheduling one or more individual jobs into the one or more flow containers to create a moldable schedule of one or more jobs, flows and platforms, and automatically converting the moldable schedule into a malleable schedule.

Scheduling flows in a multi-platform cluster environment

Techniques for determining feasibility of a set of one or more operator partitioning constraints are provided. The techniques include receiving one or more sets of operator partitioning constraints, wherein each set of one or more constraints define one or more desired conditions for grouping together of operators into partitions and placing partitions on hosts, wherein each operator is embodied as software that performs a particular function, processing each set of one or more operator partitioning constraints to determine feasibility of each set of one or more operator partitioning constraints, creating and outputting one or more candidate partitions and one or more host placements for each set of feasible partitioning constraints, and creating and outputting a certificate of infeasibility for each set of infeasible partitioning constraints, wherein the certificate of infeasibility outlines one or more reasons for infeasibility.

Determining operator partitioning constraint feasibility

Techniques for partitioning an operator flow graph are provided. The techniques include receiving source code for a stream processing application, wherein the source code comprises an operator flow graph, wherein the operator flow graph comprises a plurality of operators, receiving profiling data associated with the plurality of operators and one or more processing requirements of the operators, defining a candidate partition as a coalescing of one or more of the operators into one or more sets of processing elements (PEs), using the profiling data to create one or more candidate partitions of the processing elements, using the one or more candidate partitions to choose a desired partitioning of the operator flow graph, and compiling the source code into an executable code based on the desired partitioning.

Partitioning operator flow graphs

A system for clustering vertices in a streaming graph includes a structural sampler configured to receive a stream of edges. The structural sampler includes a reservoir manager configured to receive the stream of edges and create a structural reservoir and a support reservoir and a graph manager configured to receive the structural reservoir from the reservoir manager and to create a sampled graph from the structural reservoir, wherein the sampled graph includes one or more clusters that each include one or more connected vertices.

Clustering streaming graphs

Embodiments of the invention include methods for identifying one or more clusters in a streaming graph, the method includes receiving a stream of edges and sampling the stream of edges to create a structural reservoir and support reservoir. The method also includes creating a sampled graph from the structural reservoir and identifying the one or more clusters in the sampled graph by grouping one or more connected vertices in the sampled graph.

A method for scheduling a data processing job includes receiving the data processing job formed of a plurality of computing units, combining the plurality of computing units into a plurality of sets of tasks, each set including tasks of about equal estimated size, and different sets having different sized tasks, and assigning the tasks to a plurality of processors using a dynamic longest processing time (DLPT) scheme.

Growing community of inventors

Elmsford, NY, United States of America

Rohit Madhukar Khandekar