A computer implemented method for managing job scheduling is provided. In one example, the method includes receiving a request to process a job for a first compute instance, the job having a predetermined wait time before requesting a second compute instance, and determining the status of a pool of existing instances potentially available to service the job. If the probability that a computing instance of the pool will become available before the predetermined wait time is less than a predetermined probability, the method schedules the job to a new instance of the pool of existing instances. If the probability that a computing instance will become available before the predetermined wait time is greater than the predetermined probability the method maintains the job with the first instance. In some examples, the compute instances relate to genomic sequence data processing and analysis.

Systems and method for the management of resource dependent workflows are provided. One or more resource control devices monitor usage of a computing resource by server computing devices. Each resource control device may direct a server computing device to proceed with a workflow or to pause processing a workflow. A resource control device may further direct a server computing device to take resource conserving actions. When a computing resource is determined to be available, a resource control device may direct a paused server computing device to resume a workflow.

A cloud management method and a cloud management device are provided. The cloud management method receives a resource allocation request for a specific service, calculates an idle resource current state score regarding each resource of each node of each cluster by monitoring virtual resource usage current states of a plurality of nodes included in a plurality of clusters, and determines a node to allocate resources for executing the requested specific service, based on the calculated idle resource current state score. Accordingly, a score is given to a resource current state of an associated cluster, and a resource candidate group is selected in response to a service scheduling request, based on the score, and an optimal node is selected based on required resources necessary for the service.

The present disclosure discloses a method, an apparatus and a storage medium for training a deep learning framework, and relates to the artificial intelligence field such as deep learning and big data processing. The specific implementation solution is: acquiring at least one task node in a current task node cluster, that meets a preset opening condition when a target task meets a training start condition; judging whether a number of nodes of the at least one task node is greater than or equal to a preset number; synchronously training the deep learning framework of the target task by the at least one task node according to sample data if the number of nodes is greater than the preset number; and acquiring a synchronously trained target deep learning framework when the target task meets a training completion condition.

A computer-implemented method for scheduling a series of recurring events including: receiving one or more requests to allocate resource(s) to a series of recurring events, wherein the one or more requests specify, for each event, a corresponding desired time period over which the resource(s) are to be allocated, and the one or more requests further specify one or more adjustment criteria for defining, for one or more of the events, one or more permissibly adjusted time periods from the desired time period; obtaining, for each event, resource availability data indicative of an availability of the resource(s) during the desired time period; and, for each event: determining, based on the resource availability data, a viable time period, wherein the viable time period is either the desired time period or a permissibly adjusted time period that satisfies the one or more adjustment criteria; and allocating the resource(s) to the viable time period.

An information handling system may include at least one processor; and a non-transitory memory coupled to the at least one processor. The information handling system may be configured to manage an upgrade of a cluster of N host systems by: receiving an indication of a number M of host systems of the cluster that are needed to satisfy an operational requirement of the cluster; removing N-M of the host systems from the cluster; causing the removed host systems to be upgraded in parallel; and adding at least one of the removed host systems back to the cluster after the at least one of the removed host systems has completed the upgrade.

There is provided a system and method of resource verification for an application, the method comprising: obtaining a checklist of resources required for running the application, wherein the checklist is generated by: obtaining source code and an executable file of the application; running the executable file, and monitoring resources accessed by the application during execution thereof and corresponding access type of each resource, giving rise to a first list of resources; performing static code analysis of the source code including searching the source code for one or more specific code patterns indicative of respective resources to be accessed and corresponding access types thereof, and generating a second list of resources; and mapping the first list and second list of resources to generate the checklist of resources. The method further comprises automatically verifying the checklist of resources, giving rise to a verification result.

A computer-implemented method including: obtaining resource availability data indicative of a set of one or more resources available at a future time period, wherein the set of one or more resources are allocable to a plurality of resource users; obtaining requirements data indicative of a requirement for a subset of the plurality of resource users to have access to a subset of the set of one or more resources: and generating a resource allocation plan for the future time period based on the resource availability data and the requirements data. Generating the resource allocation plan includes allocating, to the subset of the plurality of resource users, the subset of the set of one or more resources; and allocating, to one or more other resource users of the plurality of resource users, at least a portion of the resources remaining from the set of one or more resources.

Embodiments of the present disclosure relate to a method for allocating computing resources, an electronic device, and a corresponding computer program product. The method may include: obtaining an available resource list associated with a computing resource requester according to determination that a resource use request from the computing resource requester is received, wherein the available resource list includes the number and available time periods of computing resources that can be provided by at least one computing resource provider. In addition, the method further includes: allocating computing resources to the computing resource requester based on the available resource list and the resource use request, so that the computing resource requester uses the allocated computing resources to run a workload. The embodiments of the present disclosure can flexibly allocate the computing resources, thereby realizing full utilization of the computing resources.

Systems and methods for providing optional services for browser-based smart editing tools. In one implementation, a client extension on a client device receives a request to perform a function of a service. The client extension identifies an execution parameter for the service. The client extension determines a local service availability by evaluating a logical condition comprising the execution parameter. In view of the local service availability, the client extension causes the function of the service to be executed either on one of the client device or on a server device.