International Business Machines Corporation

Kyndryl, Inc.

Cory J Kleinheksel

Jason Allan Nikolai

David M Koster

Alexander Cook

Ryan Kirk Cradick

Bin Cao

Daniel Leonard Hiebert

David M Egle

James E Carey

Peter Andrew Nicholls

Christopher R Sabotta

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

A method, computer program product, and computer system for identifying, in a dynamically formed cloud, a plurality of voluntary compute nodes, wherein the plurality of voluntary compute nodes may include personal computing devices of a plurality of users, and wherein at least a portion of the plurality of voluntary compute nodes may be configured to communicate with each other to receive and send streaming data. A first compute node of at least the portion of the plurality of voluntary compute nodes may be identified to receive and distribute a streams operator of a streams graph requested by a client computing device. A second compute node of at least the portion of the plurality of voluntary compute nodes may be identified for verifying computational correctness of a tuple stream computation for the stream operator by replicating the tuple stream computations for the stream operator on the second compute node. A final result tuples of the graph may be sent to the client computing device.

System and method for dynamic cloud formations for stream computing

Disclosed aspects relate to cache management in a stream computing environment that uses a set of many-core hardware processors to process a stream of tuples by a plurality of processing elements which operate on the set of many-core hardware processors. The stream of tuples to be processed by the plurality of processing elements which operate on the set of many-core hardware processors may be received. A tuple-processing hardware-route on the set of many-core hardware processors may be determined based on a cache factor associated with the set of many-core hardware processors. The stream of tuples may be routed based on the tuple-processing hardware-route on the set of many-core hardware processors. The stream of tuples may be processed by the plurality of processing elements which operate on the set of many-core hardware processors.

Cache management in a stream computing environment that uses a set of many-core hardware processors

A computer-implemented method includes exchanging device data, associated with a first participating user device, with the one or more second participating user devices; receiving audio data associated with a voice command; exchanging the audio data with the one or more second participating user devices; identifying, by the first participating user device, a voice command based on exchanging the audio data; determining which one of the first participating user device or the one or more second participating user devices should respond to the voice command based on details of the voice command and the exchanging the device data; responding to the voice command based on determining that the first participating user device should respond to the voice command; and signaling a particular second participating user device to respond to the voice command based on determining that the particular second participating user device should respond to the voice command.

Coordinating the execution of a voice command across multiple connected devices

A method, computer program product, and computer system for defining, on at least one processor, one or more potential overflow paths for processing at least a portion of a data load through a plurality of stream operators deployed on a plurality of computing devices. Embodiments may include determining, on the at least one processor, a conditional availability for each computing device of the plurality of computing devices. Embodiments may also include identifying, on the at least one processor, a change in one or more of a configuration of the plurality of computing devices and the at least a portion of the data load. Embodiments may further include dynamically activating, on the at least one processor, one or more potential overflow paths in response to the identified change based upon, at least in part, the conditional availability for each computing device of the plurality of computing devices.

System and method for dynamic activation of real-time streaming data overflow paths

Disclosed aspects relate to resource usage management in a stream computing environment that includes a set of hosts having a set of processing elements which has a set of stream operators. A first activity indicator may be detected for a first subset of the set of stream operators. A resource action for performance with respect to the first subset of the set of stream operators may be determined based on the first activity indicator. The resource action may be performed with respect to the first subset of the set of stream operators to benefit resource usage in the stream computing environment.

Resource usage management in a stream computing environment

Methods and apparatus, including computer program products, implementing and using techniques for data stream processing in a runtime data processing environment. A stream processing graph that includes several connected operators is received. Source code of the operators is analyzed to identify hints describing whether an operator contains data structures, method parameters or other data that can be applied in a parallelization data processing environment. Performance metrics of the data processing environment within parallel regions is evaluated to determine whether data processing resources can be dynamically scaled up or down. In response to determining that the data processing resources can be dynamically scaled up, one or more operators are split to be processed on two or more parallel processing resources. In response to determining that the data processing resources can be dynamically scaled down, one or more operators are combined to be processed on a single parallel processing resource.

Dynamic stream operator fission and fusion with platform management hints

Methods for data stream processing in a runtime data processing environment. A stream processing graph that includes several connected operators is received. Source code of the operators is analyzed to identify hints describing whether an operator contains data structures, method parameters or other data that can be applied in a parallelization data processing environment. Performance metrics of the data processing environment within parallel regions is evaluated to determine whether data processing resources can be dynamically scaled up or down. In response to determining that the data processing resources can be dynamically scaled up, one or more operators are split to be processed on two or more parallel processing resources. In response to determining that the data processing resources can be dynamically scaled down, one or more operators are combined to be processed on a single parallel processing resource.

Pre-deployment of particular virtual machines based on performance and due to service popularity and resource cost scores in a cloud environment

Streamlining a stream computing environment by distributing tuple attributes to associated operators in the environment involves receiving a stream of tuples for processing by one or more stream operators. A tuple in the stream of tuples is analyzed to identify one or more data attributes required by the one or more stream operators during processing. Each of the identified attributes is distributed to particular operators that require the identified attribute.

Streamlining tuple processing by delivering tuple attributes to associated operators

Adjusting a computing environment for processing a data stream involves identifying one or more dummy tuples in the data stream processed by the computing environment. Propagation of the identified dummy tuples to be processed through a plurality of operators in the computing environment is controlled in order to control adjustment of the computing environment. The computing environment is adjusted based on the processing of the propagated dummy tuples.

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Ames, IA, United States of America

Cory J Kleinheksel