International Business Machines Corporation

Simon J Kofkin-Hansen

Richard T Lemelin

Setareh Mehrabanzad

Shawn P Raess

Ruchi Mahindru

Yu Deng

Dimitrios Pendarakis

Harigovind Venkatraj Ramasamy

Hui Lei

Soumitra Sarkar

David R Safford

Kenneth Alan Goldman

Vijay Krishnarao Naik

James Patrick Hoff

Long Wang

Shu Tao

Stefan Berger

Scott Christopher Moonen

Jayaram Kallapalayam Radhakrishnan

Devin A Conley

James R Barros

Chris Moss

Richard Christopher Smith

Jordan Shamir

Richard B Ehrhardt

William B Padman

Iain Mccown

Theodor Razvan Ionescu

Bryan M Buckland

Christopher Moss

A method comprises obtaining a set of log files for a software system. The set of log files applies to an extended window. A periodic pattern in a first set of error-event surges in the set of log files is identified. The error-event surges in the first set is identified as event noise. A second set of log files for the software system is obtained. The second set of log files applies to a shortened window. Timeseries analysis on the second set of log files is performed. A particular error-event surge in a detection period in the second set of log files that is abnormal as compared to the shortened window is detected based on the timeseries analysis. That the particular error-event surge does not fit into the periodic pattern is determined, the particular error-event surge is characterized as an anomaly, based on the determining.

Anomaly recognition in information technology environments

A mechanism is provided for identifying patterns of a set of software applications instances from their documents. The computer-implemented method begins with constructing different attribute vector types using a knowledge ontology. The knowledge ontology captures semantics based on keywords associated with resource attributes derived from one or more documents related to at least a portion of these software application instances. A knowledge base is built from the attribute vector types and the documents of these application instances. These are merged into the knowledge base with the knowledge base previously built from previous software application instances. Analytics are performed on the knowledge base to identify at least one of common patterns of deployments, configurations, or other attribute vector types, or a combination thereof.

Identifying patterns of a set of software applications

Aspects include allocating virtual resources to physical resources in a computer environment and aggregating the virtual resources, as a virtual resource pool, at a per virtual machine level in the computer environment. Other aspects include evaluating the virtual resources in the virtual resource pool against resource pool domain constraints. Other aspects include determining a resource allocation adjustment as a function of the evaluating. The resource allocation adjustment is configured to achieve a maximum specified virtual machine density. Other aspects include adjusting the virtual resources based on the resource allocation adjustment.

Controlling virtual machine density and placement distribution in a converged infrastructure resource pool

Examples of techniques for deploying an application on a cloud environment satisfying integrity and geo-fencing constraints are disclosed herein. A computer implemented method may include: receiving a guest application for deployment on a cloud environment; receiving the integrity constraints on the integrity of each of the plurality of host where the application is to be deployed; receiving geo-fencing constraints identifying a geographic location where the guest application is to be deployed; determining for which of the plurality of hosts the integrity constraints and the geo-fencing constraints are satisfied; and deploying the guest application on at least one of the plurality of hosts that satisfy the integrity constraints and the geo-fencing constraints.

Application deployment and monitoring in a cloud environment to satisfy integrity and geo-fencing constraints

Aspects include identifying physical resources in a computer environment, allocating virtual resources to the physical resources, and aggregating the virtual resources, as a virtual resource pool, at a per virtual machine level in the computer environment. Other aspects include evaluating the virtual resources in the virtual resource pool against resource pool domain constraints, and determining a resource allocation adjustment as a function of the evaluating. The resource allocation adjustment is configured to achieve a maximum specified virtual machine density.

A method and associated systems for automated deployment of a private modular cloud-computing environment. A processor receives and analyzes user requirements, then uses those requirements to select and optimize module packages that direct automatic installations & configurations of modules of the private modular cloud-computing environment. These packages provide resources and instructions that let the processor first validate a base computing platform as being able to physically support desired virtualized cloud platform and services, then automatically install and configure the cloud's virtualization layer, cloud-management stack, virtual infrastructure, cloud services, applications, middleware, and other components onto the base platform. The processor analyzes and confirms the success of each deployment step of each module package as the step is performed. Module packages may be saved, revised, and reused to identify, update, duplicate, or optimize modules of the deployed cloud platform.

Automated deployment of a private modular cloud-computing environment

Growing community of inventors

Cary, NC, United States of America

Simon J Kofkin-Hansen