International Business Machines Corporation

Robert M Abrams

Yuk Lung Chan

Nicholas C Matsakis

Anuja Deedwaniya

Anthony Thomas Sofia

Karla Kay Arndt

Gisela C Cheng

Kin Ng

Victor G Alonzo

Vaughn C Page

Daniel Nieves

Timothy John Slegel

Christian Jacobi

Dan F Greiner

Donald W Schmidt

Mark S Farrell

Peter Kenneth Szwed

Marc Henri Coq

Peter Jeremy Relson

James H Mulder

Dieter Wellerdiek

Andrew M Sica

Nicholas R Jones

Barbara J Bryant

William James Schoen

Horst Sinram

Desmond Fitzpatrick

Donald T Durand

Ming-Yin Ng

Friedrich Matthias Gubitz

Eugene Sale

Patrick Reed Tracy

Examples described herein provide a computer-implemented method that includes receiving a list of objects with anomaly detection results associated therewith. The method further includes generating a resource graph, wherein the resource graph comprises at least two nodes, each of the at least two nodes being associated with one of the objects. The method further includes determining a relationship between the at least two nodes. The method further includes assigning an anomaly score to at least one of the objects. The method further includes calculating for at least one of the nodes, an overall anomaly score for the at least one of the nodes based on the anomaly score for the at least one of the objects and based on the anomaly score for another node related to the at least one of the nodes.

Aggregating results from multiple anomaly detection engines

Examples described herein provide a computer-implemented method that includes defining a key performance indicator associated with a non-instrumented object of a processing system. The method further includes determining a current anomaly level of the key performance indicator for an instrumented object having a relationship with the non-instrumented object. The method further includes assigning an anomaly level to the non-instrumented object based on the current anomaly level.

Assigning an anomaly level to a non-instrumented object

Examples described herein provide a computer-implemented method that includes detecting an anomaly associated with an object of a computer system and determining an importance classification of the object. An object relationship of the object is determined with respect to one or more other objects of the computer system. An impact score of the anomaly is determined based on the importance classification and the object relationship. An anomaly report is output with the impact score.

Anomaly detection with impact assessment

A method, system, and computer program product are provided for performance anomaly detection. Velocity data is periodically received from a workload manager for one or more address spaces. An expected velocity value is created for each of the one or more address spaces. A factor of the expected velocity value is compared to a current velocity value from the velocity data. Based on the current velocity value being lower than the factor, a remedial action is generated indicating an anomaly.

Programmatic performance anomaly detection

According to an aspect, a computer-implemented method includes configuring a disaster recovery computer system as a test environment of a mainframe computer system as a mirror image of a production environment, where the disaster recovery computer system is a backup of a primary production computer system. Test cases are executed in the test environment of the disaster recovery computer system. A stress and load impacts can be monitored on a plurality of computer system resources of the disaster recovery computer system based on execution of the test cases. The test environment can be disabled, and the disaster recovery computer system can be reconfigured as a production system based on a failure of the primary production computer system.

Testing in a disaster recovery computer system

Systems and methods for tracking computing resources are provided. Aspects include receiving, by a workload manager, a workload, wherein the workload is executable on a customer machine, and wherein the customer machine comprises a plurality of computing resources. The workload is analyzed to determine a workload profile. A token associated with the workload is received and associated with the workload profile. The token is authenticated and the workload is executed based at least in part on authentication of the token.

Tracking usage of computing resources

Present disclosure relates to methods, processing systems and computer program products of analyzing contention data and following resource blockers to find root causes of computer problems. The method may include: detecting one or more resource waiters in a computer system, iteratively determining whether the resource blockers are a resource waiter, until a final resource blocker not waiting for another resource is found, determining, whether final resource blocker is caused by a resource blocker in a different computer system, iteratively executing, the method on the different computer system to find the final resource blocker not waiting for another resource is found, determining, whether the final resource blocker has more than one symptom that may or may not be a contention problem, selecting a symptom that has the highest priority as the root cause of the computer problems, and generating, using the processor, a report of root causes of the computer problems.

Analyzing contention data and following resource blockers to find root causes of computer problems

Technical solutions are described for transferring standalone dump data of a system to a diagnosis system. An example method includes detecting a creation of a multi-volume standalone dump dataset. The method also includes, in response to detecting the creation, creating an entry in a list of standalone dump datasets, the entry corresponding to the multi-volume standalone dump dataset created. The method also includes detecting a change in data stored in the multi-volume standalone dump dataset; and in response to detecting the change, updating the corresponding entry in the list of standalone dump datasets, and generating a sequential standalone dump based on the multi-volume standalone dump dataset. The method also includes alerting the diagnosis system of the sequential standalone dump. The method also includes transferring the sequential standalone dump for access by the diagnosis system.

Detection and automatic transfer of standalone system dumps

Tail gate veil

Examples of techniques for routing a representational state transfer (REST) request among a plurality of virtual processing systems are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method may include: receiving, at a load balancer, the REST request; assigning, by the load balancer, the REST request to one of a plurality of web servers, each of the plurality of web servers executing on a virtual processing system; routing, by the one of the plurality of web servers, the REST request to one of the plurality of virtual processing systems; and storing, by the one of the plurality of web servers, state information regarding the REST request to a data store.

Representational state transfer request routing

Examples of techniques for invoking a restricted access service through a representational state transfer (RESTful) interface are disclosed. In one example implementation according to aspects of the present disclosure, a method may include: receiving, at a web server, a RESTful application program interface (API) request to access a RESTful API from a user on a mobile device; determining whether the user is authorized to access the RESTful API using an authorization service; responsive to determining that the user is authorized to access the RESTful API, routing the RESTful API request from the web server to the restricted access service to process the request; receiving, by the web server, return data from the restricted access service after processing the request; and invoking a data transformer associated with the RESTful API to transform the return data into transformed data.

Invoking a restricted access service through a restful interface

A computer-implemented method for monitoring server stability based on a stability time specification of a server includes specifying a stability time for the server, wherein the stability time is defined as a time between a starting state of the server and a stability point of the server. The server activity is monitored by an availability manager to determine an availability status of the server. Responsive to the server activity progressing to the stability point within the stability time, an embodiment determines that the server is stable. Responsive to the server activity failing to progress to the stability point within the stability time, an embodiment determines that the server is unreliable.

Server restart management via stability time

A computer system is configured to monitor server stability based on a stability time specification of a server. An embodiment specifies a stability time for the server, wherein the stability time is defined as a time between a starting state of the server and a stability point of the server. The server activity is monitored by an availability manager to determine an availability status of the server. Responsive to the server activity progressing to the stability point within the stability time, an embodiment determines that the server is stable. Responsive to the server activity failing to progress to the stability point within the stability time, an embodiment determines that the server is unreliable.

A method of identifying errors in a computing system operation is provided and includes identifying that a certain record of interest in system trace information has a number of entries that exceeds a predefined number and inferring from the excessive number of entries that a work unit associated with the certain record of interest is affected by an error.

Error identification

One or more registers used to form an address usable in accessing storage are examined to determine if a zero address event has occurred in forming the address. In response to an indication that a zero address event has occurred in address formation, an alert is provided to the program using the address to access storage.

Growing community of inventors

Wappingers Falls, NY, United States of America

Robert M Abrams