Systems and methods for providing a guaranteed batch pool are described, including receiving a job request for execution on the pool of resources; determining an amount of time to be utilized for executing the job request based on available resources from the pool of resources and historical resource usage of the pool of resources; determining a resource allocation from the pool of resources, wherein the resource allocation spreads the job request over the amount of time; determining that the job request is capable of being executed for the amount of time; and executing the job request over the amount of time, according to the resource allocation.

Systems and methods for providing a guaranteed batch pool are described, including receiving a job request for execution on the pool of resources; determining an amount of time to be utilized for executing the job request based on available resources from the pool of resources and historical resource usage of the pool of resources; determining a resource allocation from the pool of resources, wherein the resource allocation spreads the job request over the amount of time; determining that the job request is capable of being executed for the amount of time; and executing the job request over the amount of time, according to the resource allocation.

The subject matter described herein includes methods, systems, and computer readable media for proactive network testing. One method for proactive network testing includes receiving, by a test controller and via a network tap, at least one metric associated with live network traffic; determining, by the test controller and using the at least one metric and a threshold value associated with the at least one metric, that a network test is to be performed; configuring, by the test controller, a first test agent to execute the network test; and executing, by the first test agent, the network test.

The subject matter described herein includes methods, systems, and computer readable media for proactive network testing. One method for proactive network testing includes receiving, by a test controller and via a network tap, at least one metric associated with live network traffic; determining, by the test controller and using the at least one metric and a threshold value associated with the at least one metric, that a network test is to be performed; configuring, by the test controller, a first test agent to execute the network test; and executing, by the first test agent, the network test.

Presented herein are techniques performed in a network comprising a plurality of network nodes each configured to apply one or more service functions to traffic that passes the respective network nodes in a service path. At a network node, an indication is received of a failure or degradation of one or more service functions or applications applied to traffic at the network node. Data descriptive of the failure or degradation is generated. A previous service hop network node at which a service function or application was applied to traffic in the service path is determined. The data descriptive of the failure or degradation is communicated to the previous service hop network node.

A method includes polling, via a service specific manager operating on a software container in a cloud infrastructure, usage of different application resources and parameters for each service of a plurality of services provided in the cloud infrastructure to yield respective polled data for each service, collating, at the service specific manager, the respective polled data for each service to yield a collation, and based on the collation, deriving a respective weight for each service which a container manager can use to create multiple instances of a new service. The method further includes communicating the respective weight for each service to the container manager and determining, via the container manager, whether to scale up or scale down container services based on the respective weight for each service.

Disclosed herein are system, method, and apparatus for account inheritance. The method performed at an application server includes receiving, via an application programming interface (API), data information received by each authorization gateway of a plurality of authorization gateways. The method includes performing calculation of an overall health of each authorization gateway of the plurality of authorization gateways based on a predetermined value of a weight assigned to each server of the plurality of servers and the value of service level corresponding to each server of the plurality of servers. The method includes balancing load of each authorization gateway of the plurality of authorization gateways by routing at least one client request message to a set of one or more authorization gateways of the plurality of authorization gateways based on the calculated overall health of each authorization gateway of the plurality of authorization gateways that is above a specific threshold value.

A system receives, from one or more subsystems, one or more predicted outcomes associated with a device. The system provides provide at least a subset of the predicted outcomes as input to a machine learning model trained to identify a set of resolution actions. The system receives, from the machine learning model, the set of resolution actions for the subset of the predicted outcomes, wherein each resolution action in the set of resolution actions is associated with a probability of resolving at least one of the predicted outcomes in the subset of predicted outcomes. The system identifies a first resolution action from the set of resolution actions, wherein the first resolution action has a highest probability of resolving the at least one of the predicted outcomes in the subset of predicted outcomes. The system provides a first instruction to execute the first resolution action.

A system receives, from one or more subsystems, one or more predicted outcomes associated with a device. The system provides provide at least a subset of the predicted outcomes as input to a machine learning model trained to identify a set of resolution actions. The system receives, from the machine learning model, the set of resolution actions for the subset of the predicted outcomes, wherein each resolution action in the set of resolution actions is associated with a probability of resolving at least one of the predicted outcomes in the subset of predicted outcomes. The system identifies a first resolution action from the set of resolution actions, wherein the first resolution action has a highest probability of resolving the at least one of the predicted outcomes in the subset of predicted outcomes. The system provides a first instruction to execute the first resolution action.

A method for subscriber tier plan adjustment including: monitoring traffic flow for one or more subscriber of a plurality of subscribers on an operator’s network; determining a bandwidth requirement for each of the one or more subscribers; determining a recommended tier plan for each subscriber based on the subscribers’ bandwidth requirement; and providing the recommended tier plan for each of the subscribers to a network operator. A system for subscriber tier plan adjustment including: a learning module configured to monitor traffic flow for one or more subscribers of a plurality of subscribers on a network and determine a bandwidth requirement of each of the one or more subscribers; an analysis module configured to determine a recommended tier plan for each of the subscribers based on each subscriber’s bandwidth requirement; and a notification module configured to provide the recommended tier plan for each subscriber.