Techniques for implementing rollback of infrastructure changes in an infrastructure orchestration service are described. In certain examples, an infrastructure orchestration service is disclosed that manages both provisioning and deploying of infrastructure assets within a cloud environment. The service receives a plan comprising a set of instructions associated with a set of infrastructure assets of an execution target and identifies a first state of the set of infrastructure assets. The service executes the set of instructions in the plan to achieve a second state for the set of infrastructure assets. Based in part on the executing, the service receives a trigger for rolling back the plan to restore the set of infrastructure assets in the plan to the first state and executes a rollback plan for the plan. The service then transmits a result associated with the execution of the rollback plan.

A data management method includes receiving, by a management server, a first request, determining, based on an identifier of a first user in the first request, whether a shadow tenant bucket associated with the identifier of the first user exists, and if the shadow tenant bucket associated with the identifier of the first user exists, storing, in the shadow tenant bucket associated with the identifier of the first user, an acceleration engine image (AEI) that the first user requests to register, where a shadow tenant bucket is used to store an AEI of a specified user, and each shadow tenant bucket is in a one-to-one correspondence with a user.

Techniques described herein relate to a method for allocating graphics processing unit partitions for a computer vision environment. The method includes obtaining, by a computer vision (CV) manager, an initial graphics processing unit (GPU) partition allocation request associated with a CV workload; in response to obtaining the initial GPU partition allocation request: obtaining CV workload information associated with the CV workload; obtaining first CV environment configuration information associated with the GPU partition allocation request; generating an optimal GPU partition allocation based on the first CV environment configuration information and the CV workload information using a GPU partition model; and initiating performance of the CV workload in a CV environment based on the optimal GPU partition allocation.

Disclosed herein are system, apparatus, article of manufacture, method, and/or computer program product embodiments for monitoring long-standing computing resources. An apparatus may operate by receiving a cloud monitoring notification, where the cloud monitoring notification may indicate an occurrence of a monitored condition. The apparatus may then operate by scanning a cluster computing system for resource having a client assigned resource identifier and a computing resource attribute based on a resource identifier scan parameter and a resource attribute scan parameter. The apparatus may further operate by generating a resource notification request based on the scanning of the cluster computing system and transmitting the resource notification request to a communications system to notify a user that the resource has a computing resource attribute that match the resource attribute scan parameter.

A device suspension method and a computing device are provided. In the method, before a device enters a suspended state, memory space occupied by a background process that is unrelated to a foreground process is released. In this way, the background process unrelated to the foreground process is not saved in a memory of the device. In other words, it reduces data stored in the memory when the device is suspended. Therefore, when the device needs to be woken up, only a relatively small amount of data needs to be read from the memory, and a working state can be rapidly restored. This can reduce a delay of reading data from the memory when the device is woken up, thereby accelerating a wakeup speed of the device. In addition, the data is stored in the memory when the device is suspended.

A computer based system and method for multilateral computing resource reallocation and asset transaction migration may include: receiving a resource transaction request; determining a policy for the request; identifying, in a resource monitoring database, resources to service the request and choosing resources matching the policy determined for the request; and documenting the choosing of resources in the monitoring database. Embodiments may further include automatically reallocating occupied resources to alternative transactions and/or migrating currently-running tasks to idle resources, for example according to predefined conditions. Embodiments of the invention may allow performing various dynamic, granular computational resource and/or asset reallocation and/or transaction migration procedures which may involve dynamic composition granular individual resources and/or assets (e.g. of multiple types and/or sizes) into functional resources (to be used by, e.g., various workload execution instances) by a resource reallocation hub, which may further include various dedicated modules and/or engines and/or components.

In general, the invention relates to providing computer implemented services using information handling systems. One or more embodiments of the invention includes receiving a request to decompose a composed information handling system, wherein the composed information handling system comprises a hardware resource, obtaining a cleaning requirement for the hardware resource, initiating, based on the cleaning requirement, a cleaning operation on the hardware resource, receive a confirmation that the cleaning operation is complete, and after receiving the confirmation, set a state of the hardware resource to allocatable.

Systems and methods related to integrated memory pooling and direct swap caching are described. A system includes a compute node comprising a local memory and a pooled memory. The system further includes a host operating system (OS) having initial access to: (1) a first swappable range of memory addresses associated with the local memory and a non-swappable range of memory addresses associated with the local memory, and (2) a second swappable range of memory addresses associated with the pooled memory. The system further includes a data-mover offload engine configured to perform a cleanup operation, including: (1) restore a state of any memory content swapped-out from a memory location within the first swappable range of memory addresses to the pooled memory, and (2) move from the local memory any memory content swapped-in from a memory location within the second swappable range of memory addresses back out to the pooled memory.

A system to facilitate a container orchestration cloud service platform is described. The system includes a controller to manage Kubernetes cluster life-cycle operations created by each of a plurality of providers. The controller includes one or more processors to execute a controller micro service to discover a provider plugin associated with each of the plurality of providers, and perform the cluster life-cycle operations at a container orchestration platform as a broker for each of a plurality of providers.

Techniques for operating a computing system to perform neural network operations are disclosed. In one example, a method comprises receiving a neural network model, determining a sequence of neural network operations based on data dependency in the neural network model, and determining a set of instructions to map the sequence of neural network operations to the processing resources of the neural network processor. The method further comprises determining, based on a set of memory access operations included in the set of instructions, a first set of memory references associated with a first location of an external memory to store the input data and a second set of memory references associated with a second location of the external memory to store the output data, and generating an instruction file including the set of instructions, the first set of memory references and the second set of memory references.