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, 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 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.

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: 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.

Abstract: 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.

Abstract: 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.

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 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.

Abstract: 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.

Abstract: 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.