International Business Machines Corporation

Globalfoundries Inc.

Kyndryl, Inc.

Bin Cao

James E Carey

Mathew Richard Odden

Lance Bragstad

Michael J Branson

Brian Robert Muras

Jessica R Eidem

Jingdong Sun

Daniel Leonard Hiebert

Kirubel Z Seifu

Bradley L Manning

Jim C Chen

Tanveer Zubair

John Matthew Santosuosso

Jay Symmes Bryant

Xi Lun Chen

Xiao Bin Zhang

David M Egle

Cory J Kleinheksel

Lin Cai

Lauren A Somers

Michael John Fork

Yongwen Wu

Charles J Archer

Phillip V Mann

A computer-implemented method includes: monitoring, by a computing device, performance of currently deployed virtual machines (VMs) that implement particular services; determining, by the computing device, optimal configuration options for deployment of new VMs that implement one or more of the particular services based on the monitoring the performance of the currently deployed VMs; and outputting, by the computing device, information regarding the optimal configuration options to a user requesting the deployment of a new VM implementing one or more of the particular services.

Optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment

Embodiments of the preset disclosure include a method, a system, and a computer program product for rearranging a set of filters within a data streams environment. The method includes setting a first filter ordering of two or more filter processing elements. The method includes determining a filtering efficiency of the first filter ordering. The determining of the filtering efficiency includes monitoring a runtime, one or more tuples, and an amount of tuples retained after filtering of the first filter ordering. The method includes creating a second filter ordering of the two or more filter processing elements. The method includes comparing the filtering efficiency of the second filter ordering to the first filter ordering. The method includes selecting the second filter ordering in response to comparing the filtering efficiency of the second filter ordering to the filtering efficiency of the first filter ordering.

Filter optimizer for data streams

A computer-implemented method includes receiving, by a computing device via an application programming interface (API) or user interface, compliance definitions identifying compliance checks for endpoints in a cloud network; generating, by the computing device, a compliance check inventory identifying the compliance checks and actions associated with the compliance checks; storing, by the computing device, the compliance check inventory in a computer storage system identifying, by the computing device, redundant actions in the compliance check inventory; identifying, by the computing device, outdated actions in the compliance check inventory; and updating, by the computing device, the compliance check inventory by eliminating the redundant actions and the outdated actions.

Optimization of cloud compliance services based on compliance actions

Methods, apparatuses, and computer program products for visualizing migration of a resource of a distributed computing environment are provided. Embodiments include displaying, within a graphical user interface (GUI), one or more graphical resource representations. Each graphical resource representation represents a resource of a distributed computing environment. Each graphical resource representation is displayed in a particular location within the GUI according to a location of the resource within the distributed computing environment. Embodiments also include displaying, within the GUI, a first graphical migration representation. The first graphical migration representation represents a first transfer operation of a first resource of the distributed computing environment. Embodiments also include updating based on a progress of the first transfer operation, within the GUI at least one of: the first graphical migration representation and a location of at least one of the one or more graphical resource representations.

Visualizing migration of a resource of a distributed computing environment

A stream application receives a stream of tuples to be processed by a plurality of processing elements. The plurality of processing elements operating on one or more compute nodes. Each processing element has one or more stream operators. The stream application assigns one or more processing cycles to one or more segments of software code. The segments of software code are embedded in a tuple of the stream of tuples. The software-embedded tuple identifies a set of target tuples based upon operation criteria. The set of target tuples are a part of the stream of tuples. The software-embedded tuple performs an operation based on the set of identified target tuples.

Smart tuple dynamic grouping of tuples

An appliance migration mechanism (AMM) optimizes migration of a live virtual appliance with virtual objects between cloud environments to minimize temporary connections that use significant cloud resources during the migration. The AMM determines a cost of connections of each virtual object in the virtual appliance and produces an order of migration for the virtual objects. The cost of connections of the virtual objects may be determined by the total number of connections and the maximum number of connections. Alternatively the cost of the connections of the virtual objects for migration could be determined by a weighting the costs of the connections where the weighting may consider loading or other factors on the connection.

Optimized migration of virtual objects across environments in a cloud computing environment

A stream application receives a stream of tuples to be processed by a plurality of processing elements. The plurality of processing elements operate on one or more compute nodes. Each processing element has one or more stream operators. Segments of software code are embedded in a tuple of the stream of tuples. The tuple retrieves one or more compute node metrics. The compute node metrics describe one or more resources of a first compute node. The tuple obtains tuple information of one or more tuples of the stream of tuples to be processed by a first stream operator that operates on the one or more resources. The tuple determines a prospective resource disparity related to the first stream operator based on the obtained tuple information and the compute node metrics. The tuple transmits a resource request to the stream application based on the determined prospective resource disparity.

Smart tuple resource estimation

A method is provided for applying tasks. The method can include receiving a plurality of tasks for a plurality of applications, an application of the plurality of applications is hosted by a computing environment utilizing a monitoring agent, and a task is performed for an operation of the application during a first period of time. The method can also include determining a first resource type and a first resource amount from the computing environment for performing the task. The method can also include grouping the plurality of tasks into a set of tasks based on the first resource type, at least two tasks from the set of tasks being performable in parallel during the first period of time. The method can also include determining whether the first resource amount of the first resource type is present in the computing environment during the first period of time.

Managed services coordinator

A stream application receives a stream of tuples to be processed by a plurality of processing elements operating on one or more compute nodes. Each processing element has one or more stream operators. The stream application assigns one or more processing cycles to one or more segments of software code. The segments of software code are embedded in one or more tuples of the stream of tuples. The stream application determines that a condition is met based on the processing cycles. The stream application performs an operation based on the processing cycles.

Smart tuple condition-based operation performance

A mobile data network supports making subscriber data addressable as devices in a mobile data network. Each data chunk is assigned a device address in the mobile data network. The data chunk can then be addressed as a device in the mobile data network. Data chunks corresponding to a subscriber are distributed across multiple devices in the mobile data network. The location of the subscriber's data chunks is tracked by the subscriber's mobile device and also by a tracking table in the mobile data network.

Distributing subscriber data in a mobile data network

Performance of parallel portions of a streaming application implemented in multiple virtual machines (VMs) is monitored and logged. When there is a need to replicate operators in an existing virtual machine to a new virtual machine, the logged performance data is used to determine a desired configuration for the new virtual machine based on a past implementation of the existing virtual machine.

Replicating a virtual machine implementing parallel operators in a streaming application based on performance

A simulation mechanism manages deployment of a simplified computing solution (SCS) and a corresponding simulation model that simulates a scaled multiple of the SCS to allow a deployment configuration for a large scale computing solution to be determined and tested before actually deploying the large scale computing solution.

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Rochester, MN, United States of America

Bin Cao