Abstract: 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.

Abstract: 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.

Abstract: A method of deploying artificial intelligence (AI) model resources includes storing at least one AI model in a model store memory in a plurality of different versions, each different version having a different level of fidelity. When a request to exercise the AI model is received, a processor determines which version of the AI model to exercise for the received request. The determined AI model version is used to serve the received request by exercising input data accompanying the received request. The result of the exercised AI model version is used to respond to the received request.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method, processing system, and computer readable storage medium, reduce heap memory used by an application, where unused memory in the heap memory is reclaimed by a garbage collector. A processor periodically monitors the application's memory usage including maximum heap memory size, committed heap memory size, in use heap memory size, and a garbage collection activity level. The processor, based on determining that the monitored garbage collection activity level is below a threshold, releases unused heap memory from the application by reducing the maximum heap memory size.

Abstract: 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.

Abstract: Techniques for securely and adaptively delivering multimedia content are disclosed in which a set of alternate access units for each time slot is obtained. Then, the encryption stream index of each access unit from the set of alternate access units of the previous time slot are obtained. An encryption stream index is then assigned to each access unit in the set of alternate access units in the current time slot, such that the encryption index increases over time. Thus, the invention overcomes the problem of encrypting a multimedia stream that may have multiple access units for each time slot by selecting the encryption index for each access unit such that the encryption index increases, regardless of which access unit the delivery system (e.g., server) selects for transmission.

Abstract: 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.