International Business Machines Corporation

Hugh Edward Hockett

Aaron J Quirk

Rohith Kottamangalam Ashok

Lin Sun

Roy Frank Brabson

Rahul Ghosh

Jerry Wayne Stevens

Jeffrey Douglas Haggar

Matt Richard Hogstrom

Wei Lung Chan

Matthew J Sheard

Michael John Burr

Maurice Isrel

Andre Tost

Christopher M Laffoon

Adam Robert Geiger

Kristin R Whetstone

Rachel H Fithian

Ajay Arvind Apte

Scott Christopher Moonen

Shu Chao Wan

Giribabu V Paramkusham

Michael S Law

James C Riordan

Keith Rafferty

Harish Sukhwani

Edward M Howard

Bruce H Ratcliff

Stephen R Valley

Michael G Fitzpatrick

Constantinos Kassimis

Antonio Abbondanzio

Andrew Russell Low

Benjamin P Segal

David Anthony Herr

Michael Jon Fox

Barry P Gower

Wolfgang Segmuller

James Patrick Robbins

Todd Elsworth Valler

Jeremy R Geddes

Ching-Yun Chao

Bret Ronald Olszewski

Ivan M Heninger

John Yow-Chun Chang

Terry Michael Bleizeffer

Ki Hong Park

Jose I Ortiz

Sivaram Gottimukkala

Yuhsuke Kaneyasu

Joseph Paul Wigglesworth

Lewis Lo

Leonardo A Uzcategui

Barbara J Vander Weele

Rohan Gandhi

Ankit Patel

Kin Ueng

Iqbal M Umair

Michael W Stayton

Edward Zebrowski, Jr

Vaibhav Vijay Gadge

Patrick L Davis

Thuy Thanh Duong

Nicholas Calvin Schambureck

Charbel S Alwan

Adam M Gunther

Eric Kirchstein

Gabriel Tamura

Hans Albert Muller

Ian Fraser Watts

David V Rooney

Miguel Angel Jimenez Achinte

Matthew D McClintock

Sagar Jadhav

David Avila

Stephanie K Hagadorn

Brian T Pegram

Aleksandra Brand

Daniel P Glaser

Disclosed are techniques for two-way synchronization of infrastructure-as-code templates and instances, including a method comprising detecting changes to a run-time state of a system and, in response to detecting a change, triggering an update of a current run-time state model. The method may further comprise, in response to updating the run-time state model, comparing the updated model to a current model using a template in a local repository instantiated as the current model. The method may further comprise, in response to the comparison determining a structural difference between the updated model and the current model, merging the updated model and the current model into a new model; and updating a local clone of a repository of the template with the new model. The method may further comprise, in response to the comparison determining no structural difference between the updated model and the current model, pushing changes to a remote repository.

Two-way synchronization of infrastructure-as-code templates and instances

A processor may identify an action of a first device of the IoT devices. The processor may initiate a transfer from the first device to one or more other devices of the IoT devices. The processor may pause each thread being executed by the first device and the one or more other devices. The processor may transfer a unit from the first device to the one or more other devices.

Computation sharing among devices using device OS clustering

Computer-implemented methods, systems and computer program products that leverage infrastructure code (IaC) to provision applications in a computing environment as virtual resources. A plurality of containers operating parallel to one another process resource provisioning requests. A central application provisioner orchestrates the containerized provisioning of resources using IaC. Application provisioner introspects IaC templates defining the provisioning request and creates a graph of the templates, mapping requested resources to containers based on the specification of the containers available. Application provisioner spins up idle containers and distributes resource provisioning tasks amongst the idle containers. An idle container provisions an assigned resource and outputs individual state information describing the provisioned resource. Application provisioner aggregates the individual state information from the containers into a completed state file describing the provisioned application and deploys the provisioned application. Provisioned resources can be destroyed by deprovisioning the resource from the assigned container and outputting modified state information.

Parallel resource provisioning

An approach is provided in which a system displays a user interface that includes cloud environment configuration parameters on a display. Each of the cloud environment configuration parameters is selectable by a user and corresponds to a new cloud environment. The system receives a set of user selections from the user that selects at least one of the cloud environment configuration parameters. Then, the system generates a recommended cloud profile based, at least in part, on the set of user selections, and configures the new cloud environment based on the recommended cloud profile.

Configuration and usage pattern of a cloud environment based on iterative learning

Automatically managing an entire life-cycle of a practice failover. The system uses a virtualization management system that hosts one or more business applications which has capability and authority to manage both the application hosting infrastructure as well as the storage infrastructure that is used by the applications in a disaster recovery configuration. The set of applications is replicated along with an application platform together with the applications.

Disaster recovery practice mode for application virtualization infrastructure

An approach is provided in which an information handling system receives a scaling request corresponding to an application that includes multiple workloads executing on a first cloud environment and a second cloud environment. The first cloud environment is managed by a first service provider and the second cloud environment is managed by a second service provider. The information handling system evaluates the scaling request against a first set of policies corresponding to the first service provider and against a second set of policies corresponding to the second service provider. In turn, the information handling system scales, in response to the evaluating, one or more first resources on the first cloud environment and one or more second resources on the second cloud environment to fulfill the scaling request.

Policy based requesting/approval system across multiple hybrid clouds

Embodiments provide optimized deployment of workloads. A first workload to be deployed in at least one data center of a plurality of data centers is received. A first plurality of candidate data centers is selected from the plurality of data centers, and the first workload is deployed to each of the first plurality of candidate data centers. A first performance metric is collected from each respective data center of the first plurality of candidate data centers based on execution of the first workload deployed at the respective data centers, and a first optimal data center from the first plurality of candidate data centers is identified based on the first performance metrics. The first workload is removed from each of the first plurality of candidate data centers, other than the identified first optimal data center.

Determining a location of optimal computing resources for workloads

A method, executed by a computer, for deploying a virtual system includes collecting requirements for a computing system from a user, determining a component pattern corresponding to the user requirements, enabling a user to edit the component pattern, presenting the component pattern on a deployment design grid, and deploying a virtual system corresponding to the component pattern. The component pattern may correspond to one or more components and associated attributes, as well as a distribution for the components. In one embodiment, enabling a user to edit the component pattern includes reserving system resources for the components such that the system resources are available to the components at runtime. A computer system and computer program product corresponding to the method are also disclosed herein.

Multi-target deployment of virtual systems

A method is provided for building patterns with high level pattern topology requirements for deployment across multiple systems. The patterns are built based on pattern capabilities and configurations of the multiple systems. Such patterns can meet the desired pattern capabilities including high availability and/or continuous capabilities.

Domain-specific pattern design

In one aspect, a computer-implemented method for compressing content based on a social factor, an environmental factor, and a relational factor is described. The method may include monitoring access to the content, forming, by a processor, a contextual evaluation based on the monitored access and determining an importance of the content based on the contextual evaluation. The method may further include storing the content in a compressed form, where the compressed form is selected based on the importance.

Dynamic compression of multimedia content based on social and relational context

An approach is provided in which a system receives a set of cloud requirements corresponding to a cloud environment and analyzes the set of cloud requirements using a set of machine logic based rules to generate a recommended cloud profile that is based, at least in part, on the set of cloud requirements and includes configuration setting values. In turn, the system configures configurable parameters of the cloud environment according to the configuration setting values of the recommended cloud profile.

A method, system and computer program product for managing pattern contents across multiple racks. The workload usage characteristics of the pattern contents on a primary rack in a domain of racks are collected based on monitoring the workload usage of the pattern contents in the pattern definition and in the deployed pattern. Furthermore, information regarding use of the pattern definition, the deployed pattern and the pattern contents by users ('human interaction usage patterns') is collected based on monitoring the human interaction usage patterns. The pattern contents are then ranked based on analyzing the collected workload usage characteristics and analyzing the collected information regarding human interaction usage patterns. Once the pattern contents are ranked, those contents that are highly ranked are deemed to be more likely to be utilized in the pattern and therefore are the contents that are copied from the primary rack to the other racks of the domain.

Intelligently managing pattern contents across multiple racks based on workload and human interaction usage patterns

Specific example debugging sessions are used to generate a generic decision tree (flow diagram) for use in future debugging sessions. Debug metadata is recorded for each debugging session. The debug metadata includes log file accesses and key traces. A generic decision tree is generated for use in future debugging sessions. Each file accessed by a developer during debug sessions creates a node in the generic decision tree. Branches of the decision tree are created between the nodes by aggregating the plurality of debug sessions together based on similarities and differences among the file accesses and results for the various debug sessions. The recorded metadata is genericized by identifying specific actions taken by developers and specific results thereof. The actions and results are abstracted to create more general types of actions and results such that the generic decision tree is applicable to a wider variety of problems in the software program.

Debug session tree recorder with generic playback

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Raleigh, NC, United States of America

Hugh Edward Hockett