An intelligent method of selecting a data recovery site upon receiving a data recovery request. The backup system collects historical activity data of the storage system to identify work load of every data recovery site. A predicted activity load for each data recovery site is then generated using the collected data. When a request for data recovery is received, the system first identifies which data recovery site has copies of the files to be recovered. Then it uses the predicted work load for these data recovery sites to determine whether to use a geographically local site or a site that may be remote geographically, but has a lower work load.

A system for providing computer implemented services using information handling systems includes a composed information handling system that provides, at least in part, the computer implemented services and a system control processor manager. The system control processor manager instantiates a utilization monitor in a system control processor of the composed information handling system; and monitors, using the utilization monitor, a use rate of computing resources of the composed information handling system by a client.

The current document is directed to methods and subsystems that instantiate and manage mobile-network computational infrastructure. The currently disclosed improved mobile-network-computational-infrastructure orchestration system employs several layers of containerized-application orchestration and management systems. For increased efficiency and security, mobile-network-specific operators are added to the containerized-application orchestration layers in order to extend the functionalities of the containerized-application orchestration layers and move virtualization-layer dependencies from the mobile-network-computational-infrastructure orchestration system down into the containerized-application orchestration layers. The improved mobile-network-computational-infrastructure orchestration system is responsible for generating, from an input mobile-network computational-infrastructure specification, one or more workload resource specifications and a node policy that are input to a containerized-application-orchestration layer. The containerized-application-orchestration layers instantiate and manage worker nodes that execute mobile-network application instances that implement VNFs and CNFs according to the one or more workload resource specifications and the node policy.

Systems, apparatuses, and methods for implementing continuation analysis tasks (CATs) are disclosed. In one embodiment, a system implements hardware acceleration of CATs to manage the dependencies and scheduling of an application composed of multiple tasks. In one embodiment, a continuation packet is referenced directly by a first task. When the first task completes, the first task enqueues a continuation packet on a first queue. The first task can specify on which queue to place the continuation packet. The agent responsible for the first queue dequeues and executes the continuation packet which invokes an analysis phase which is performed prior to determining which dependent tasks to enqueue. If it is determined during the analysis phase that a second task is now ready to be launched, the second task is enqueued on one of the queues. Then, an agent responsible for this queue dequeues and executes the second task.

An automated improving of quality of service of a data center. Transients of a power grid fed to a power supply unit are monitored by a probe. Information on transients is provided across an interface to a server of the data center. Based on characteristics of the transients, a reliability of the data center is subjected to automated updating. A request for migration of workload requiring a higher reliability than the updated reliability can be sent to a central management. When the central management has identified another data center that can meet the required reliability, the central management migrates or relocates the workload to the another data center.

Various techniques are described for classifying synthetic data tasks and orchestrating a resource allocation between groups of eligible resources for processing the synthetic data tasks. Received synthetic data tasks can be classified by identifying a task category and a corresponding group of eligible resources (e.g., processors) for processing synthetic data tasks in the task category. For example, synthetic data tasks can include generation of source assets, ingestion of source assets, identification of variation parameters, variation of variation parameters, and creation of synthetic data. Certain categories of synthetic data tasks can be classified for processing with a particular group of eligible resources. For example, tasks to ingest synthetic data assets can be classified for processing on a CPU only, while a task to create synthetic data assets can be classified for processing on a GPU only. The synthetic data tasks can be queued and routed for processing by an eligible resource.

Methods, non-transitory computer readable media, workload management devices, and network traffic management systems that optimize systems with idempotent workloads are illustrated. With this technology, an identification is made when a status indicates a jobs is deferred. A determination is then made when the job is preempted based on a type of the job, when the identification indicates the job is deferred and the type and an identifier of the job matches another job. Another status is adjusted to indicate the other job is deferred. The status is then modified to indicate that the job is preempted, or the job is removed, when the determination indicates the job is preempted. Accordingly, jobs are selectively preempted, such as based on idempotency of the associated workload, to achieve intended consistent states for objects faster, with increased reliability, and with reduced overhead.

Example aspects include techniques for implementing resource governance in multi-tenant environment. These techniques may include receiving a service request for a multi-tenant service from a client device, and predicting a resource utilization value (RUV) resulting from execution of the service request based on text of the service request, an amount of data associated with the client device at the multi-tenant service, and/or a temporal execution value. In addition, the techniques may include determining that the RUV is greater than a preconfigured threshold identifying an expensive request, and applying a load balancing strategy to the service request based on the RUV being greater than the preconfigured threshold.

A cluster computing system is provided. The cluster computing system includes: a host including a first processor and a first buffer memory; computing nodes, each of which includes a second processor and a second buffer memory configured to store data received from the host; a network configured to connect the host and the computing nodes; and storage devices respectively corresponding to the computing nodes. The first processor is configured to control a task allocator to monitor a task performance state of each of the computing nodes, select at least one of the computing nodes as a task node based on the task performance state of each of the computing nodes, and distribute a background task to the task node, and the second processor of the task node is configured to perform the background task on sorted files stored in the second buffer memory, the sorted files being received by the second buffer memory from the first buffer memory via the network.

A plurality of containers can be configured for running applications associated to at least one node of a distributed computing environments. The containers of the plurality of containers includes integrated intelligence that provides an in memory state component that detects how container instances are running. A quorum synchronization component of the integrated intelligence can coordinate the activities of the containers. A first container can be initiated for running a first node application. The memory state component can determine if a topology exists in the plurality of containers that is running an existing application matching the first node application. The quorum synchronization component of the integrated intelligence can coordinate running of the first node application with the first container with the existing application.