Methods and systems provide power management for workspaces operating on an Information Handling System (IHS). Power management capabilities of the IHS are registered with a workspace orchestration service that is remote from the IHS and that manages deployment of workspaces on the IHS. The workspaces are instantiated on the IHS according to a respective workspace definition that is provided by the workspace orchestration service. The remote workspace orchestration service provides a first workspace with a handle for accessing the registered power management capabilities of the IHS. A remote access controller of the IHS receives reports of application events occurring in the first workspace, wherein the reports are received by the handle provided by the remote workspace orchestration service. Based on the application events received from the first workspace, power provided by a power supply of the IHS is allocated to each of a plurality of processors of the IHS.

Techniques for graph-based handling of service requests are disclosed. The techniques include: receiving a service request that requires at least one resource from multiple available resources; computing a graph-based opportunity cost metric associated with satisfying the service request; and handling the service request based at least on the graph-based opportunity cost metric.

A method, system, and apparatus are provided for accelerated atomic resource allocation on a multiprocessor platform. In particular, a resource allocation engine (RAE) performs the following: counting available units for each of the one or more resources; parsing a multi-resource ticket (MRT) for a processor, wherein the parsing identifies one or more requested resource types, each resource type being paired with a requested resource units; comparing the multi-resource ticket to one or more resource queues for the requested resource types, wherein the comparing determines an availability status of at least one the requested resource types; and based on the availability status, calculating whether or not all of the requested resource types can be allocated for the processor, wherein the calculating is completed before allocating a next requested resource for a next processor.

Disclosed are various embodiments for the controlling the amount of active updates that can occur during a given time on devices that are associated with tenants (e.g., organizations) and subtenants (e.g., sub-organizations) in a multi-tenant environment. In particular, each tenant and subtenant is assigned throttle corresponding to different update parameters (e.g., an amount of devices executing an active update, an amount of data to be downloaded during a campaign, a time for completing the update campaign, etc.). When an update campaign is established, the update campaign can define the different devices that are to be updated. In some situations, the number of active updates required may exceed the allotted resources for a given subtenant. When a subtenant requires additional resources than what is assigned to complete the update, the subtenant can borrow resources defined by the update parameters from a subtenant peer that has a surplus.

Systems, methods, and software described herein provide enhancements for initiating application services across a virtual environment. In one implementation, a method of deploying application services includes initiating configuration process for a first service and a second service across virtual nodes, wherein the first service is dependent on the availability of the second service. The method further includes holding the configuration of the first service, completing the configuration of the second service, and responsively transferring a notification from the virtual node or nodes of the second service to the virtual node or nodes of the first service, permitting the configuration of the first service to be completed.

Disclosed is a method of managing computer resources in a dynamic computing environment. The method includes identifying available resources from an available pool based on an augmented model, the available pool including resources unallocated resources, allocating the identified available resources in accordance with the augmented model, identifying reserve resources from a reserve pool based on the augmented model, the reserve pool including resources not allocated and not configured, and upon determining the available pool includes a number of resources below a threshold, replenishing the available pool with the identified reserve resources.

Techniques are described for optimizing the allocation of computing resources provided by a service provider network—for example, compute resources such as virtual machine (VM) instances, containers, standalone servers, and possibly other types of computing resources—among computing workloads associated with a user or group of users of the service provider network. A service provider network provides various tools and interfaces to help businesses and other organizations optimize the utilization of computing resource pools obtained by the organizations from the service provider network, including the ability to efficiently schedule use of the resources among workloads having varying resource demands, usage patterns, relative priorities, execution deadlines, or combinations thereof. A service provider network further provides various graphical user interfaces (GUIs) to help users visualize and manage the historical and scheduled uses of computing resources by users' workloads according to user preferences.

Systems, apparatuses, methods, and computer programs for overprovisioning resources are disclosed. Resource usage statistics may be gathered for a plurality of client virtual machines (“VMs”). Statistical characteristics of resource usage by the plurality of client VMs may be calculated. It may also be determined which of the plurality of client VMs requesting resources to allocate resources to, as well as an amount of the resources to allocate, in a given time slot based on the calculated statistical characteristics.

A method for scheduling computing resources with container migration includes determining a resource availability for one or more hosts, a resource allocation for one or more virtual machines (VMs), and a resource usage for one or more containers. The method includes identifying the hosts on which VMs and containers can be consolidated based on resource availability. The method also includes calculating a target resource configuration for one or more VMs. The method further includes removing or adding resources to the VMs for which a target resource configuration was calculated to achieve the target resource configuration. The method further includes allocating the one or more VMs on the one or more hosts based on the resource availability of the one or more hosts, and allocating the one or more containers on the one or more VMs based on the resource configuration of each VM and the resource usage of each container.

Described are systems and methods for executing a pipeline command sequence designed for execution on a single node across a fleet of nodes. An example method may commence with receiving the pipeline command sequence. Based on a type of the pipeline command sequence, a subset of available nodes for optimal execution of the pipeline command sequence across the fleet of nodes may be determined. The method may continue with defining a plurality of tasks for the subset of available nodes. The method may further include translating the pipeline command sequence into the plurality of tasks and executing the plurality of tasks on the subset of available nodes.