International Business Machines Corporation

Globalfoundries Inc.

International Business Corporation

Peter Hans Westerink

Norman Bobroff

Arun Kwangil Iyengar

Frank Andre Schaffa

Olivier Verscheure

Lisa Amini

Paolo Dettori

Chitra Venkatramani

Liana Liyow Fong

Pascal Frossard

Krishna Chaitanya Ratakonda

Chai Wah Wu

Julio Nogima

Hao Yang

Bryce Allen Curtis

Deepak Srinivas Turaga

Michael H Dawson

William F Abt, Jr

Peter J Parente

Zhenghua Fu

Rajesh Rajagopalan

Dinesh C Verma

Elliot N Linzer

Jeffrey Bruce Lotspiech

Kang-Won Lee

Sumedh Wasudeo Sathaye

Martin J Hirzel

Xiaoqiao Meng

Evelyn Duesterwald

Seetharami R Seelam

Ligang Lu

Anuradha Bhamidipaty

Stephen Peter Wood

Todd W Mummert

Cesar Augusto Gonzales

William Russell Belknap

Martin G Kienzle

Ophir Azulai

Andrew Lawrence Frenkiel

Roderick Michael West

Stefan Nusser

Michelle Yoonkyung Kim

Alan Braz

Glenn E Brew

Durga Sai Phaneendhar Vemuru

Gayathri Shaikh

Durga Sai Phaneendar Vemuru

Daniel Smilkov

Zhengua Fu

Michael Hilton Dawson

Pascal Frossad

Lisa D Amini

Chitra Venka-Tramani

Techniques facilitating guiding machine learning models and related components are provided. In one example, a computer-implemented method comprises identifying, by a device operatively coupled to a processor, a set of models, wherein the set of models includes respective model components; determining, by the device, one or more model relations among the respective model components, wherein the one or more model relations respectively comprise a vector of component relations between respective pairwise ones of the model components; and suggesting, by the device, a subset of the set of models based on a mapping of the component relations.

Guiding machine learning models and related components

A method, information processing system, and computer readable storage medium, periodically monitor, with a processing system, information related to an application's memory usage including a maximum heap memory size and a garbage collection activity level. Based on determining that the garbage collection activity level is below a first threshold, releasing memory from the application by reducing the maximum heap memory size.

Computer memory usage by releasing unused heap space

A method for operating a teleconference includes receiving a first video and audio signal from a first teleconference participant and receiving a second video and audio signal from a second teleconference participant. The method further includes relaying the first video and audio signal to the second teleconference participant and relaying the second video and audio signal to the first teleconference participant. The method additionally includes analyzing the first video and audio signal using a plurality of behavior analytics to determine a level of participant engagement of the first teleconference participant. The method further includes determining that the first teleconference participant is not engaged when the determined level of participant engagement is below a predetermined threshold.

Multimedia conferencing system for determining participant engagement

A method, information processing system, and computer readable storage medium, periodically monitor, with a processing system, information related to an application's memory usage including a maximum heap memory size, an in use heap memory size, and a garbage collection activity level. Based on determining that the garbage collection activity level is below a first threshold, estimating an amount of memory that can be released from the application by reducing the maximum heap memory size. Based on determining that the estimated amount of memory that can be released from the application is above a second threshold, attempting to release memory from the application.

Memory use for garbage collected computer environments

Techniques are provided for performing automated operations to determine maturity of a specified project. Information is received regarding each of a plurality of artifacts associated with the project, such as project documentation, source code repositories, and a tracked issue database for the project. A data sufficiency level associated with each provided artifact is determined, and each artifact is provided to one or more of multiple analysis engines. The analysis engines are executed to produce one or more weighted feature vectors for each of the artifacts associated with the specified project, and input to a prediction engine in order to provide a maturity rating for the project based on the weighted feature vectors.

Evaluating project maturity from data sources

A method, information processing system, and computer readable storage medium, vary a maximum heap memory size for one application of a plurality of applications based on monitoring garbage collection activity levels for the plurality of applications, each application including a heap memory, and unused memory in the heap memory being reclaimed by a garbage collector.

Optimizing memory usage across multiple applications based on garbage collection activity

A method and structure for dynamic memory re-allocation for an application runtime environment (ARE) includes receiving, through an interface of an application runtime environment (ARE), a first set of internal operational metrics of the ARE executing at a current setting S1 on a processor of a computer. A first performance P1 of the ARE is determined at the current setting S1 using the received first set of internal operation metrics. The current setting S1 of the ARE is varied to a new setting S2. A second set of internal operational metrics of the ARE executing at the new setting S2 is received through the interface of the ARE. A second performance P2 of the ARE is determined at the new setting S2, using the received second set of internal operation metrics. A memory allocation for the ARE is re-allocated, based on the determined performances P1 and P2.

Active control of memory for java virtual machines and other application runtime environments

Optimizing memory usage across multiple applications in the presence of garbage collection

Optimizing memory usage across multiple garbage collected computer environments

An apparatus includes at least one processor executing a method for managing memory among a plurality of concurrently-running virtual machines, and a non-transitory memory device that stores a set of computer readable instructions for implementing and executing said memory management method. A memory optimization mechanism can reduce a memory usage of a virtual machine at a cost of increasing a central processing unit (CPU) usage. Information on a memory usage and a CPU usage of each virtual machine is periodically collected. When a first virtual machine exhibits high memory use, at least one second virtual machine with an extra CPU capacity is identified. A memory optimization mechanism is applied to the second virtual machine to reduce memory used by the second virtual machine, thereby providing a portion of freed memory that is then allocated to the first virtual machine.

System and method for improving memory usage in virtual machines at a cost of increasing CPU usage

A method for allocating memory among a plurality of computing systems includes assigning a free memory threshold to each computing system, dynamically varying at least one free memory threshold for at least one computing system based on a past memory usage of the at least one computing system, periodically monitoring each computing system for memory usage and in response to the monitoring determining that an amount of free memory for the computer system is below the free memory threshold for the computing system, allocating memory from the free memory pool to the computing system.

Dynamically optimizing memory allocation across virtual machines

A tool for autoscaling applications in a shared cloud resource environment. The tool registers, by one or more computer processors, one or more trigger conditions. The tool initiates, by one or more computer processors, a scaling event based, at least in part, on at least one of the one or more trigger conditions. The tool determines, by one or more computer processors, a scaling decision for the scaling event based, at least in part, on one or more scaling rules related to the one or more trigger conditions.

Autoscaling applications in shared cloud resources

A method (and system) for managing memory among virtual machines in a system having a plurality of virtual machines, includes providing at least one memory optimization mechanism which can reduce memory usage of a virtual machine at a cost of increasing CPU usage. Information on memory usage and CPU usage of each virtual machine is periodically collected. In response to detecting that a first virtual machine exhibits a high level of memory use, at least one second virtual machine with extra CPU capacity is identified. The at least one memory optimization mechanism is applied to the at least one second virtual machine, to reduce memory used by the at least one second virtual machine, thereby providing a portion of freed memory. The portion of freed memory is then allocated to the first virtual machine.

System and method improving memory usage in virtual machines by releasing additional memory at the cost of increased CPU overhead

Methods and apparatus are provided for encoding video, such as surveillance video. A video frame is encoded by evaluating a level of activity in video frame relative to one or more neighboring frames; encoding the video frame using an inter-coding technique if the evaluation satisfies one or more predefined inter-coding criteria, wherein said inter-coding technique is applied to said video frame without a Group of Picture structure having a maximum number of consecutive inter-coded frames; and encoding the video frame using an intra-coding technique if the evaluation satisfies one or more predefined intra-coding criteria. The evaluating step may comprise, for example, determining whether differences between the video frame and the one or more neighboring frames exceed a predefined threshold.

Method and apparatus for encoding surveillance video

Growing community of inventors

Ossining, NY, United States of America

Peter Hans Westerink