A system to facilitate infrastructure management is described. The system includes one or more processors and a non-transitory machine-readable medium storing instructions that, when executed, cause the one or more processors to execute an infrastructure management controller to automatically balance utilization of infrastructure resources between a plurality of on-premise infrastructure controllers.

Techniques for managing containerized workloads in a container computing environment are disclosed. For example, a method comprises the following steps. In a container computing environment configured to create an instance of a containerized workload for executing a microservice, the method computes a parameter based on a first set of execution conditions for the microservice, wherein the parameter represents a resource utilization value at which at least one additional instance of the containerized workload is created for executing the microservice. The method then re-computes the parameter based on a second set of execution conditions for the microservice.

Examples described herein include systems and methods for efficiently scaling an SDDC. An example method can include storing resource utilization information for a variety of resources of the SDDC. The example method can also include predicting a future resource utilization rate for the resources and determining that a predicted utilization rate is outside of a desired range. The system can determine how long it would take to perform the scaling, including adding or removing a host and performing related functions such as load balancing or data transfers. The system can also determine how long the scaling is predicted to benefit the SDDC to ensure that the benefit is sufficient to undergo the scaling operation. If the expected benefit is greater than the benefit threshold, the system can perform the scaling operation.

An information management system includes a storage manager for managing backup and/or restore operations for one or more client computing devices. The storage manager may be in communication with a resource administrator of a computing resource cluster, wherein the resource administrator instantiates one or more computing pods using the computing resource cluster. The resource administrator may receive a request for computing resources from the storage manager and provision the computing pods based on the request. The resource administrator may then select a pre-configured container image from one or more pre-configured container images based on the computing resource request, wherein the pre-configured container image configures a computing pod to create secondary copies of primary data from a particular primary data source of the information management system. The resource administrator may then communicate a message to the storage manager informing the storage of the availability of the provisioned computing pods.

An information handling system may predict, for a first time period, a first resource usage of a first information handling system resource for an application. The information handling system may also predict, for the first time period, a second resource usage level of a second information handling system resource different from the first information handling system resource for the application. The information handling system may adjust one or more performance parameters for the information handling system based on the first resource usage level and the second resource usage level.

Disclosed herein are systems, devices, and methods for providing auto-scaling in a cluster of device instances. In one embodiment, a method is disclosed comprising updating, using a distributed counter, a metric associated with one or more instances executing a network application; identifying that the metric has exceeded a threshold defined in a scaling policy based on comparing the distributed counter to the scaling policy; identifying a command to execute in response to the metric exceeding the threshold; and executing the command to modify the one or more instances.

A method for managing tasks in a storage system, the method may include: (a) obtaining, by a scheduler, a shared budget for background storage tasks and foreground storage tasks; (b) obtaining, by the scheduler, a background budget for background storage tasks; wherein the background budget is a fraction of the shared budget; (c) allocating, by the scheduler, resources to pending storage tasks according to the shared budget and the background budget; wherein the allocating comprises (i) allocating the shared budget while prioritizing foreground storage tasks over background storage tasks; and (ii) allocating the background budget to background storage tasks; and (d) participating, by the scheduler, in executing of storage tasks according to the allocation.

The present disclosure relates to a method, device and computer program product for managing jobs in a processing system. The processing system comprises multiple client devices. In the method, based on a group of jobs from the multiple client devices, a current workload of the group of jobs is determined. A group of job descriptions associated with the group of jobs is determined based on configuration information of various jobs in the group of jobs. A future workload associated with the group of jobs is determined based on associations, comprised in a workload model, between job descriptions and future workloads associated with the job descriptions. The group of jobs in the processing system are managed based on the current workload and the future workload. With the foregoing example implementation, jobs in the processing system may be managed more effectively, and latency in processing jobs may be reduced. Further, there is provided a device and computer program product for managing jobs in a processing system.

Systems and methods described herein support compartment quotas in a cloud infrastructure environment. Cloud administrators do not generally have the ability to restrict resource usage in existing clouds. Granting a user permission to create resources allows them to create any number of resources up to a predefined account limit. Compartment quotas allow admins to restrict a user's resource usage to the appropriate level allowing fine-tuned cost control.

A method can include obtaining information on at least one of the following: resource occupation of a reconfigurable functional unit associated with hardware accelerator resources or GPP resources, power consumption of a hardware accelerator associated with hardware accelerator resources, and power consumption of a server associated with GPP resources. The method can also include performing processing on the reconfigurable functional unit based on the obtained information, the processing including at least one of configuration, reconfiguration, and migration. The method and apparatus of certain embodiments may increase efficiency of resource management of the edge cloud, lower system energy consumption, and/or enable more efficient virtualization mechanisms for hardware accelerator resources.