Examples include a method of live migrating a virtual device by creating a virtual device in a virtual machine, creating first and second interfaces for the virtual device, transferring data over the first interface, detecting a disconnection of the virtual device from the virtual machine, switching data transfers for the virtual device from the first interface to the second interface, detecting a reconnection of the virtual device to the virtual machine, and switching data transfers for the virtual device from the second interface to the first interface.

This disclosure describes techniques for improving allocation of computing resources to computation of machine learning tasks, including on massive computing systems hosting machine learning models. A method includes a computing system, based on a computational metric trend and/or a predicted computational metric of a past task model, allocating a computing resource for computing of a machine learning task by a current task model prior to runtime of the current task model; computing the machine learning task by executing a copy of the current task model; quantifying a computational metric of the copy of the current task model; determining a computational metric trend based on the computational metric; deriving a predicted computational metric of the copy of the current task model based on the computational metric; and, based on the computational metric trend, changing allocation of a computing resource for computing of the machine learning task by the current task model.

Provided are methods, systems, computer program products for scheduling jobs. The method may include receiving a request for allocating resources for a first job, a job comprising information regarding maximum amount of resources required by the job; determining a type of the first job; obtaining at least one backfill of the first job based on the determined type; allocating the maximum amount of resources from system resources to the first job; searching a second job in waiting jobs to be allocated resources, the second job being suitable to be allocated used resources by the first job from the maximum amount of resources allocated to the first job during the at least one backfill; allocating the resources unused by the first job from the maximum amount of resources allocated to the first job to the second job in response to the first job running to the at least one backfill.

There is included a method and apparatus comprising computer code configured to cause a processor or processors to perform obtaining an input of at least one of a task and a workflow, setting a timeout for the input of the at least one of the task and the workflow, determining whether the at least one of the task and the workflow observes a lack of data of the input for a duration equal to the timeout, determining, in response to determining that the at least one of the task and the workflow observed the lack of data of the input for the duration equal to the timeout, an unavailability of further data of the input, applying an update to the at least one of the task and the workflow based on determining the unavailability, and processing the at least one of the task and the workflow.

Transaction authorization systems may include a transaction processor and an authorization server system. The transaction processor obtains transaction requests authorizations for those requests from the authorization server system. The transaction processor may require an authorization be provided within a threshold time; otherwise, the transaction may be processed without authorization. The authorization server system may be hosted using one or more nodes in a distributed system. Degradation of the performance of the distributed system may cause the performance of the authorization server system to fall below the required performance threshold and transactions may not be authorized before automatic processing. Transaction authorization systems may monitor the health of the individual nodes and/or the distributed system and automatically adjust the routing of authorizations based on current and/or future performance degradation. The transaction authorization system may also allocate additional resources and/or reroute authorizations to a separate distributed system to avoid performance degradations.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

Systems are provided for logging transactions in heterogeneous networks that include a combination of one or more instrumented components and one or more non-instrumented components. The instrumented components are configured to generate impersonated log records for the non-instrumented components involved in the transaction processing hand-offs with the instrumented components. The impersonated log records are persisted with other log records that are generated by the instrumented components in a transaction log that is maintained by a central logging system to reflect a complete flow of the transaction processing performed on the object, including the flow through the non-instrumented component(s).

In some examples, a computing system receives an indication of an increased workload portion to be added to a workload of a storage system, the workload comprising buckets of operations of different characteristics. The computing system computes, based on quantities of operations of the different characteristics in the workload, factor values that indicate distribution of operations of the increased workload portion to the buckets of operations of the different characteristics, and distributes, according to the factor values, the operations of the increased workload portion into the buckets of operations of the different characteristics.

The present invention relates to an artificial intelligence method and system for event predication, comprising: receiving, user messages, user activity data, event data, user identification information and transaction data; scraping webpages for additional event data; applying a natural language processing module to process the event data; constructing a training data set using the processed event data; constructing user preferences from the user messages, the user activity data, the user identification information and the transaction data; training a predictive model using the training data set to determine at least one upcoming event predictions determining to display the at least one event predictions based on the user profile; if it is determined to display one of the at least one event predictions, generating a graphical user interface display with a calendar depicting the at least one event prediction; and presenting the graphical user interface display to the user.

A transfer method is performed by an information processing apparatus. The method includes: selecting, based on a load status of the information processing apparatus, candidate transfer data that is among the received data and to be transferred to one or more other information processing apparatuses; selecting, based on load statuses of multiple other information processing apparatuses, one or more candidate transfer destination apparatuses among the multiple other information processing apparatuses as candidate transfer destinations of the data; determining, based on throughput between the information processing apparatus and the candidate transfer destination apparatuses, data to be transferred among the candidate transfer data, transfer destination apparatuses of the data to be transferred among the candidate transfer destination apparatuses, and the sizes of data groups including the data to be transferred; and transferring, to the transfer destination apparatuses determined for the determined data groups, the determined data to be transferred.