International Business Machines Corporation

Vinay G Rajagopal

Rohit Pradeep Shetty

Arun Ramakrishnan

Logeswaran T Rajamanickam

Anindya Neogi

Geetha Adinarayan

Dilip Krishnaswamy

Managing transaction data during times of low network connectivity by organizing billing information for prioritized processing during periods of higher network connectivity. During low connectivity events, billing information is organized based, at least in part, on a combination of age and revenue to communicate important billing information upon reconnection.

Data resiliency of billing information

A computer-implemented method includes receiving, from a first log agent, a first log collection. The computer-implemented method further includes receiving a first policy, wherein the first policy includes a definition of a first pattern and a definition of a procedure. The computer-implemented method further includes scanning the first log collection against the first policy to determine a match between a portion of the first log collection and the first pattern, with the matching portion of the first log collection being identified as a first data artefact. The computer-implemented method further includes, responsive to identifying the first data artefact, executing the procedure defined by the first policy, wherein the procedure includes: filtering the first log collection to yield a first group of filtered log entries, receiving a first data collection, and sending the first group of filtered log entries and the first data collection to a recipient system.

Policy based dynamic data collection for problem analysis

An expert system extracts events associated with executing an application from log files generated by various topological resources in a distributed computing environment. The events are plotted as plot points on a time series graph. Patterns are identified in the plot points that are associated with application problems, along with the computing environment configurations both before the problem and after the problem was resolved. The difference in the configurations represents a corrective action for the application problem, and the expert system links the corrective action to the pattern. When a pattern repeats in conjunction with another application problem, the corrective action is identified as a possible solution to the new problem. A confidence level associated with the pattern/corrective action may be increased when a user accepts the corrective action and may be decreased when a user rejects the corrective action.

Identifying solutions to application execution problems in distributed computing environments

A computer-implemented method includes receiving, from a first log agent, a first log collection. The computer-implemented method further includes receiving a first policy, wherein the first policy includes a definition of a first pattern and a definition of a procedure. The computer-implemented method further includes scanning the first log collection against the first policy to determine a match between a portion of the first log collection and the first pattern, with the matching portion of the first log collection being identified as a first data artifact. The computer-implemented method further includes, responsive to identifying the first data artifact, executing the procedure defined by the first policy, wherein the procedure includes: filtering the first log collection to yield a first group of filtered log entries, receiving a first data collection, and sending the first group of filtered log entries and the first data collection to a recipient system.

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Yelahanka New Town, India

Vinay G Rajagopal