International Business Machines Corporation

Hewlett Packard Enterprise Development LP

Salesforce Com, Inc.

Hewlett-packard Development Company, L.p.

Swaroop Jayanthi

Eric Philip Fried

Narsimha Reddy Challa

Thangadurai Muthusamy

Amartey S Pearson

Sailaja R Keshireddy

Charles Scott Graham

Adam Thomas Stallman

Brahmanand Vuppuluri

Sudheer Yetukuri

Rajesh Teeparti

Chetan V P

Nikunj Aggarwal

Surya Charan Alluri

Janakirama Raju Mantena

Sripadwallabha Dattatraya Kollur

System and methods are described for automated recovery from errors occurring during replication of a database. The method includes getting text from one or more log files generated during database replication processing in a cloud computing environment, transforming the text into a structured language form represented by vectors, and identifying patterns in the vectors. The method further includes classifying one or more errors based on the identified patterns using supervised learning as either a recoverable error or an unrecoverable error, analyzing the one or more errors to determine one or more recovery jobs associated with database replication processing in the cloud computing environment for each of the recoverable errors, and invoking the one or more recovery jobs.

Database replication error recovery based on supervised learning

System and methods are described for specifying, deploying, and using plugins during replication of a database. The method includes receiving a replication plugin service configuration specifying a plurality of plugins, getting a first database plugin specified by the replication plugin service configuration, getting one or more reader plugins specified by the replication plugin service configuration, deploying the first database plugin and the one or more reader plugins to a relay communicating with a source database, and getting data from the source database using the first database plugin and the one or more reader plugins.

Database replication plugins as a service

System and methods are described for creating an instance of a database in a cloud computing environment. One method includes receiving a request to deploy an instance of a database in a cloud computing environment, the request including a database deployment model defining deployment of the database instance, building a deployment model decision tree based on the database deployment model, and initializing a cloud deployment strategy using the database deployment model for enabling one or more cloud service providers of the cloud computing environment to deploy the database instance. The method further includes provisioning cloud computing resources for the database instance according to the cloud deployment strategy, initializing sets of services for the database instance according to the deployment model decision tree, creating the database instance, and indicating the database instance as active in the cloud computing environment.

Adaptable database deployment

Examples described relate to container images. An example method may include generating a mapping of respective unique identifiers of container images of a software container and respective metadata of the container images, validating the mapping against a rule to identify a unique identifier of a mapped container image that meets the rule, generating a target mapping that includes the unique identifier of the mapped container image that meets the rule and the action specified in the rule, and performing the action specified in the rule on the mapped container image corresponding to the unique identifier in the target mapping.

Container images

A cloned configuration of a source machine is created by determining a first set of physical location codes for a source machine. A map is generated based on the sorted physical location codes that maps the first set of physical location codes to a first set of generic location codes. A second set of physical location codes associated with a second set of adapter slots in a target machine is generated. A second map is generated based on the sorted second set of physical location codes that maps the second set of physical location codes to a second set of generic location codes. A third set of physical location codes is generated based on the first set of generic location codes and the second map. If an entry in the third set of physical location codes is not present the second set of physical location codes, an error is generated.

Generic physical location codes for virtual system and partition cloning

Techniques of workload deployment in a computing network are described. For example a computing system may receive a workload deployment request for deployment of a workload on resources of the computing network. The computing system may determine unavailability of interoperable resources for deployment of the workload, based on interoperability information associated with each of the resources, and identify at least one set of resources for deployment of the workload, where deploying the workload on each set of resources from amongst the at least one set of resources comprises changing configuration of at least one resource included within corresponding set of resources. The computing system may further rate each of the set of resources based on deployment parameters, and select a first set of resources from amongst the at least one set of resources for deployment of the workload based on the rank of each of the set of resources.

Workload deployment in computing networks

A method, system, and program product for hypervisor controlled redundancy for Input/Output (I/O) paths using virtualized I/O adapters is provided. A hypervisor detects a virtual machine exceeding a pre-defined bandwidth threshold. The hypervisor searches a shared pool of free virtual functions (VF) for a free VF. If the shared pool of free VFs is not depleted, a free VF is allocated to the virtual machine (VM). If the shared pool of free VFs is depleted, the active VMs are examined for VF capacity that may be donated to the triggering VM.

Hypervisor controlled redundancy for I/O paths using virtualized I/O adapters

Examples described relate to validating network parameters of a containerized application. In an example, the current state of a network parameter configured for a containerized application in a cloud computing environment may be determined. The current state of the network parameter may be validated against a container application profile of the containerized application, wherein the container application profile specifies a desired state of the network parameter. In response to a determination that the current state of the network parameter does not meet the desired state of the network parameter specified in the container application profile, a determination may be made whether a network interface that meets the desired state of the network parameter is available in the cloud computing environment. In response to a determination that the network interface is available in the cloud computing environment, the containerized application may be migrated to the available network interface.

Validating network parameters of a containerized application

A method, system and computer program product are provided for implementing dynamic adjustment of Input/Output bandwidth for Virtual Machines of a Single Root Input/Output Virtualization (SRIOV) adapter. The SRIOV adapter includes a plurality of virtual functions (VFs). Each individual virtual function (VF) is enabled to be explicitly assigned to a Virtual Machine (VM); and each of a plurality of VF teams is created with one or more VFs and is assigned to a VM. Each VF team is enabled to be dynamically resizable for dynamic adjustment of Input/Output bandwidth.

Implementing dynamic adjustment of I/O bandwidth for virtual machines using a single root I/O virtualization (SRIOV) adapter

Methods for network device management are described. Load balancing for one or more proxy agents, in a network, is implemented based on a threshold load and a threshold interval. The load on each of the proxy agentsis compared with the threshold load to determine whether a given proxy agent is overloaded, under-loaded, or balanced.

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Bengaluru, India

Swaroop Jayanthi