Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

The present disclosure describes transaction-enabling systems and methods. A system can include a controller and a fleet of machines, each having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement. The controller may include a resource requirement circuit to determine an amount of a resource for each of the machines to service the task requirement for each machine, a forward resource market circuit to access a forward resource market, and a resource distribution circuit to execute an aggregated transaction of the resource on the forward resource market.

In a computing environment, a set of executing processes each having associated resources are provided. Aggregate resources for the computing environment include multiple different types of resources. A utilization level for each of the resources within the computing environment is evaluated to determine an unconsumed capacity for each of the resources below a utilization threshold. The utilization threshold is resource-dependent. An indication of at least a portion of unconsumed capacity for each of the resources below the utilization threshold is gathered. The unconsumed portion for each of the resources below the utilization threshold is exposed for consumption by other executing processes.

In one example, the present disclosure describes a device, computer-readable medium, and method for scaling network capacity predictively, based on customer interest. For instance, in one example, a method includes predicting an interest of a first customer in data content that will be available for consumption over a data network at a time in the future, wherein the predicting is based on customer data including at least a search pattern associated with the first customer, flagging the data content when the predicting indicates at least a threshold degree of likelihood that the first customer will be interested in the data content, and scaling an allocation of resources of the data network to the first customer, based on the flagging.

Techniques are provided for allocation of shared computing resources using source code feature extraction and machine learning techniques. An exemplary method comprises obtaining source code for execution in a shared computing environment; extracting a plurality of discriminative features from the source code; obtaining a trained machine learning model; and generating a prediction of an allocation of one or more resources of the shared computing environment needed to satisfy one or more service level agreement requirements for the source code. The generated prediction is optionally adjusted using a statistical analysis of an error curve, based on one or more error boundaries obtained by the trained machine learning model. The trained machine learning model can be trained using a set of discriminative features extracted from training source code and corresponding measurements of metrics of the service level agreement requirements obtained by executing the training source code on a plurality of the resources of the shared computing environment.

Techniques for tenant-driven dynamic resource allocation in network functions virtualization infrastructure (NFVI). In one example, an orchestration system is operated by a data center provider for a data center and that orchestration system comprises processing circuitry coupled to a memory; logic stored in the memory and configured for execution by the processing circuitry, wherein the logic is operative to: compute an aggregate bandwidth for a plurality of flows associated with a tenant of the data center provider and processed by a virtual network function, assigned to the tenant, executing on a server of the data center; and modify, based on the aggregate bandwidth, an allocation of compute resources of the server executing the virtual network function.

Described herein are systems, methods, and software to manage resources in a gateway shared by multiple tenants. In one example, a system may monitor usage of resources by a tenant of the gateway and compare the usage with usage limits associated with the resources. The system may further determine when the usage of a resource exceeds a usage limit associated with the resource and, when the usage of the resource exceeds the usage limit, identify an operation associated with causing the usage limit to be exceeded and blocking the operation.

Methods and apparatus for predicting communications resources which will be needed at a venue and then controlling the amount of available resources dynamically are described. In various embodiments real time or near real time video of areas of the venue are used to predict the number of people in a portion of a venue and/or the direction of movement. Along with other information such as the type of event and/or event schedule collected information is supplied to a set of trained resource requirement models which are used to predict future resource needs at a venue, e.g., while an event is ongoing. Commands are sent to dynamically vary the amount of communications resources provided to one or more portions of the venue. Resources which can be varied included but are not limited to fixed wired WAN bandwidth, WiFi bandwidth, cellular bandwidth, network based on-demand services, transcoding services, firewall services, etc.

A user equipment (UE) configured to receive an uplink (UL) grant comprising a UL grant size, determine a current UL buffer size, compare the current UL buffer size to the UL grant size and determining an amount of padding to fill the UL grant and determine whether to transmit on the UL grant based on the amount of padding to fill the UL grant.