In some implementations, a first network device may receive an advertisement from a second network device. The advertisement may be associated with indicating that the second network device is configured to support a particular flex-algorithm. The first network device may identify, in the advertisement, an address of the second network device. The first network device may configure a routing table of the first network device to indicate that the second network device is capable of receiving traffic associated with the particular flex-algorithm based on the address. The first network device may perform, using the routing table, an action associated with routing the traffic associated with the particular flex-algorithm.

In some implementations, a first network device may receive an advertisement from a second network device. The advertisement may be associated with indicating that the second network device is configured to support a particular flex-algorithm. The first network device may identify, in the advertisement, an address of the second network device. The first network device may configure a routing table of the first network device to indicate that the second network device is capable of receiving traffic associated with the particular flex-algorithm based on the address. The first network device may perform, using the routing table, an action associated with routing the traffic associated with the particular flex-algorithm.

Examples can include an optimizer that dynamically determines where to place virtual network functions for a slice in a distributed Telco cloud network. The optimizer can determine a slice path that complies with a service level agreement and balances network load. The virtual network functions of the slice can be provisioned at clouds identified by the optimal slice path. In one example, performance metrics are normalized, and tenant-selected weights can be applied. This can allow the optimizer to prioritize particular SLA attributes in choosing an optimal slice path.

Examples can include an optimizer that dynamically determines where to place virtual network functions for a slice in a distributed Telco cloud network. The optimizer can determine a slice path that complies with a service level agreement and balances network load. The virtual network functions of the slice can be provisioned at clouds identified by the optimal slice path. In one example, performance metrics are normalized, and tenant-selected weights can be applied. This can allow the optimizer to prioritize particular SLA attributes in choosing an optimal slice path.

A traffic planning platform may receive information related to a traffic flow including a traffic bandwidth to transport through a network with various network devices interconnected by links. The traffic planning platform may generate a traffic plan by assigning the traffic flow to a set of the links that includes network resources connecting a source of the traffic flow to a destination of the traffic flow. The traffic planning platform may render a visualization of the traffic plan, wherein the visualization includes a user interface (e.g., a diagram, an animation, and/or the like) in which geometric shapes that represent the source, the peer link, and the destination are connected by bands that represent the tunnel and the external route and further in which the geometric shapes and the bands each have a first visual property and a second visual property based on the traffic bandwidth of the traffic flow.

A traffic planning platform may receive information related to a traffic flow including a traffic bandwidth to transport through a network with various network devices interconnected by links. The traffic planning platform may generate a traffic plan by assigning the traffic flow to a set of the links that includes network resources connecting a source of the traffic flow to a destination of the traffic flow. The traffic planning platform may render a visualization of the traffic plan, wherein the visualization includes a user interface (e.g., a diagram, an animation, and/or the like) in which geometric shapes that represent the source, the peer link, and the destination are connected by bands that represent the tunnel and the external route and further in which the geometric shapes and the bands each have a first visual property and a second visual property based on the traffic bandwidth of the traffic flow.

A quarantine system could be disposed between an outer firewall and an inner firewall. The quarantine system may include persistent storage containing mappings between computing devices disposed within the inner firewall and data sources disposed outside the outer firewall. The quarantine system may include one or more processors configured to perform operations that include requesting and receiving, based on the mappings, a software-related update from a data source, the software-related update being targeted for deployment on the computing devices. The operations may also include assigning the software-related update for review by a group of one or more agents authorized to approve or reject the software-related update. The operations may also receiving an indication that the software-related update has been approved by the one or more agents and, responsive to receiving the indication, transmitting, based on the mappings, the software-related update to a recipient device within the inner firewall.

Methods, computer readable media, and systems service a queue, comprising a plurality of jobs, by identifying nodes satisfying a hardware requirement for at least a subset of jobs in the queue. Each job indicates when it was submitted to the queue and one or more node resource requirements. A current availability score for each node class in a plurality of node classes is determined and nodes of a first node class in the plurality of node classes are reserved when a demand score for the class satisfies the current availability score for the first node class by a first threshold amount. Reserved nodes are permitted to draw jobs from the queue in accordance with satisfaction by such nodes of the node resource requirements of the jobs but are terminated, without completing the jobs, when the current availability score for their node class exceeds a second threshold amount.

A system is provided for optimizing deployment of a processing function in a media production workflow. The system includes a media production workflow generator that builds the media production workflow that includes the processing function and determines deployment criteria that includes an input dataset for the processing function and an atomic compute function for executing the processing function. Moreover, a deployment topology generator generates a topologies of the resources available in a cloud computing network and based on the determined deployment criteria, with the generated topologies indicating different configurations of resources for executing the processing function and a processor for executing the atomic compute function of the processing function. Furthermore, a deployment optimizer selects an optimal topology to deploy the processing function within the cloud computing network, with the optimal topology selected to include the processor for optimizing accessibility of electronic memory to execute the atomic compute function.

A system and method to adapt the grouping between a plurality of nodes in a data processing system. In one embodiment, a first leader node (320) in a data processing system (300) is configured to determine (815) a cost map within a first group of nodes (310), calculate (820) costs between the first leader node (320) and first member nodes (330) based on the cost map, determine (830) a candidate member node (330A) from the first member nodes (330), query (840) other leader nodes (350, 380) to accept a transfer of the candidate member node (330A), receive (850) a first transfer response from a second leader node (350) to accept the transfer to a second group of nodes (340), initiate (860) a transfer of and receive (865) an acknowledgement for the transfer of the candidate member node (330A) to the second group of nodes (340).