Example capacity adjustment methods, computing devices, and computer storage media are provided. One example capacity adjustment method includes creating a scaling group. A first instance is created on a first server set for the scaling group. A second instance is created on a second server set for the scaling group. A quantity of instances deployed on the first server set for the scaling group is limited by an upper limit value.

Systems described herein may allow for the intelligent configuration of containers onto virtualized resources. Different configurations may be generated based on the simulation of alternate placements of containers onto nodes, where the placement of a particular container onto a particular node may serve as a root for several branches which may themselves simulate the placement of additional containers on the node (in addition to the container(s) indicated in the root). Once a set of configurations are generated, a particular configuration may be selected according to determined selection parameters and/or intelligent selection techniques.

A first category is determined of a first task being performed at a given time. A first asset that is configured for use with the first category is identified. A next task object is constructed. By analyzing a set of tasks that were performed during a period prior to the given time, a candidate next task is identified. The candidate next task has been performed sometime after a previous performance of the first task during the period. From the first asset, a link to a second asset is selected. The second asset is configured for use with a second category of the candidate next task. The next task object is populated with the link. The candidate next task is designated as a second task that will occur sometime after the first task.

A system is provided for dynamically modifying media processing functions to control optimization of a media production. The system includes a media processing function library that stores media processing functions that each include a subgraph embedded therein that has pre-calculated parameters and undefined variable parameters. Media processing engines execute the plurality of media processing functions to create the media production. Moreover, the system includes a media function management controller that places the media processing functions in a job queue to be executed by one or more of the media processing engines. A resource manager receives a media production instruction from a client device to partially deploy the media processing functions in the job queue. During execution, the media processing engine can receive an input deliverable that constrains the undefined variable parameters, such that the partially deployed media processing functions is dynamically modified during runtime to create the media production.

A method for cost-efficient repeated random sampling from a dynamically-changing sampling pool includes defining an array of elements to be selectively masked and unmasked throughout repeated random sampling operations from a first sampling pool. The first sampling pool includes unmasked elements of the array and excludes masked elements of the array. The method further includes identifying an exclusion element within the first sampling pool that is to be excluded from a sampling operation and removing the exclusion element from the first sampling pool. Removing the exclusion element is achieved by moving the exclusion element to a new position by swapping an array index of the exclusion element with an array index that was, during an immediately prior sampling operation, included within and bounding the first sampling pool and by masking the array index corresponding to a new position of the exclusion element. Following the swapping and masking operations, the first sampling pool is randomly sampled.

A load-balancing computing device receives a load-balance request for a processing of a workload request associated with a workload. The load-balancing computing device selects a member node of a distributed computing system to process the workload request. The member node is selected from amongst a pool of member nodes of the distributed computing system. The selecting includes: determining a member node for a baseline assignment for the workload; and selecting a member node based on an outcome of a mathematical operation performed on an identifier of the workload, the baseline cardinality of member nodes, and on the cardinality of member nodes in the pool. Next, the processing of the workload request is assigned to the selected member node.

Technologies for providing attestation for function as a service flavors include a compute device including circuitry configured to obtain function definition data indicative of a set of operations to be performed in a function and a set of hardware resources to be utilized by the function, execute a benchmark operation to produce benchmark data indicative of a measured performance of the function, and sign the function definition data and the benchmark data to produce function flavor data. The circuitry is also configured to provide the function flavor data to one or more other compute devices for validation that the function, when executed on the hardware resources, provides the measured performance and write, to a distributed ledger, the function flavor data.

An illustrative embodiment disclosed herein is an apparatus comprising a processor and a memory. In some embodiments, the memory includes programmed instructions that, when executed by the processor, cause the apparatus to receive a first object request to execute an input/output (I/O) on an object stored in an on-premises object store, select a target cloud object store of a plurality of cloud object stores, translate the first object request to a second object request to execute the I/O on the object, and send the second object request to the target cloud object store. In some embodiments, the first object request is in accordance with a first protocol of the on-premises object store. In some embodiments, the plurality of object stores is coupled to the on-premises object store. In some embodiments, the second object request is in accordance with a second protocol of the target cloud object store.

A method of verifying resource protection statuses for resources for address-based resources may include receiving a request for verification of resource protection from a client device for an address-based resource. The request includes an address of a resource. The intermediate system is programmed to receive resource protection verification requests from a plurality of client devices, and to receive resource protection verifications from a plurality of resource protection systems that are in communication with the intermediate system. The method also includes determining that none of the resource protection systems in the plurality of resource protection systems currently protect the resource; retrieving information that is securely stored for the resource and a user associated with the request; and sending the information to one or more of the plurality of resource protection systems as a request to protect the resource.

Methods and apparatuses are provided for data processing. The method includes receiving a first data packet and a second data packet; associating first codes with the first data packet and second codes with the second data packet to generate a combined data packet after receiving the first data packet and the second data packet, wherein the first codes and the second codes specify processing to be performed to the a combined data packet; generating the combined data packet comprising the first data packet and the second data packet in response to determining that the first data packet and the second data packet are correlated; and performing the processing to the combined data packet in accordance with the first codes or the second codes.