A method includes, in response to a memory error indication indicating an uncorrectable error in a faulted segment, associating in a remapping table the faulted segment with a patch segment in a patch memory region, and in response to receiving from a processor a memory access request directed to the faulted segment, servicing the memory access request from the patch segment by performing the requested memory access at the patch segment based on a patch segment address identifying the location of the patch segment. The patch segment address is determined from the remapping table and corresponds to a requested memory address specified by the memory access request.

A disclosed load estimation method includes: collecting run information of a processor being executing a predetermined program; specifying execution status of the processor based on the collected run information; and estimating a load of the predetermined program based on a result of comparison between the execution status of the processor and execution characteristics of the processor. Each of the execution characteristics is stored in association with a load level of the predetermined program.

Storage devices in a pool are divided into at least one group with a first number of storage devices in an existing group not higher than a range. When a second number of storage devices are added to the resource pool, a sum of the first number and the second number is determined. A new group is created based on at least a portion of the second number of storage devices when the sum does not satisfy the range; and another portion of the second number of storage devices are added to the existing group. A first storage space portion in each of a set of shared storage devices selected from the existing group is allocated to the existing group, and a second storage space portion in each of the set of shared storage devices is allocated to the new group. The storage space utilization rate can be increased.

A method, system and computer readable medium for running a legacy application on a non-legacy device. Operating parameters of the non-legacy device when running the legacy application are set based on one or more pre-determined heuristics for adjustment of operating parameters of the newer system when running the legacy application on the non-legacy device from one or more performance metrics and other performance information.

A method for managing provenance information associated to one or more interconnected provenance entities in a provenance system for data processing pipelines in a distributed cloud environment over a network interface, wherein each of the data processing pipelines is configured to read in data, transform the data, and output transformed data is disclosed. The method comprises steps being performed by a configuration component of obtaining at least one declarative intent representing a configuration indicative of requirements and levels of priority for storage of provenance information for each of the data processing pipelines, deriving the requirements and levels of priority for storage of provenance information for each of the data processing pipelines based on the obtained at least one declarative intent, wherein one of the levels of priority—first level of priority—is higher than the other levels of priority—second levels of priority, estimating storage capacity for storage of provenance information in the provenance system based on the derived requirements and levels of priority, storing the provenance information according to the derived requirements and levels of priority for storage of provenance information and for each of the data processing pipelines, and when actual storage consumption for storage of provenance information in the provenance system meets a threshold of storage capacity set based on the estimated storage capacity: reducing a data amount for storage of provenance information of the second levels of priority in the provenance system. Corresponding computer program product, arrangement, configuration component, and system are also disclosed.

Certain embodiments described herein relate to an improved disk usage growth prediction system. In some embodiments, one or more components in an information management system can determine usage status data of a given storage device, perform a validation check on the usage status data using multiple prediction models, compare validation results of the multiple prediction models to identify the best performing prediction model, generate a disk usage growth prediction using the identified prediction model, and adjust the available space of the storage device according to the disk usage growth prediction.

An object of the present disclosure is to provide a control apparatus that controls a plurality of communication systems so that the plurality of communication systems can perform analysis with high accuracy. The control apparatus (30) according to the present disclosure includes a communication unit (31) and a determination unit (32). The communication unit (31) receives, from an analysis apparatus (10) configured to perform machine learning using communication logs collected from a communication apparatus in order to generate a learning model, statistical information about each of the communication logs and information about the learning model. The determination unit (32) determines an analysis apparatus (20) to which the information about the learning model is applied based on the statistical information.

Solutions preparing container images and data for container workloads prior to start times of workloads predicted through workload trend analysis. Local storage space on the node is managed based on workload trends, optimizing local storage of image files without requiring frequent reloading and/or deletion of image files, avoiding network intensive I/O operations when pulling images to local storage by workload scheduling systems. Systems perform collection of historical data including image and workload properties; analyze historical data for workload trends, including predicted start times, image files needed, number of nodes and types of nodes. Based on predicted future workload start times, nodes are selected from an ordered list of node requirements and workload properties. Selected nodes' local storage is managed using predicted future start times of workloads, to avoid removing image files having sooner start times, while removing (as needed) images files predictively utilized for workloads further into the future.

A method includes receiving a request from a vehicle to perform a computing task, selecting a machine learning model from among a plurality of machine learning models based at least in part on the request, and predicting an amount of computing resources needed to perform the computing task using the selected machine learning model.

A real-time computer network application in the aeronautic industry for in-flight reconfiguring of the integrated modular avionics modules onboard aircraft, via spatial and temporal partitioning of modules conforming a data processing system including a computer-implemented method for in-flight Integrated Modular Avionics reconfiguration, a data processing system for the mentioned reconfiguration, a computer program product and a computer-readable medium, both to perform such reconfiguration.