Patent classifications
H04L41/5054
Managing service user discovery and service launch object placement on a device
Methods and apparatuses to manage service user discovery and service launch object placement on a device. A method comprising: obtaining information to assist in identifying a portion of a user interface of a wireless device, the wireless device communicatively coupled to a network system over a wireless access network; determining a differentiating attribute of the identified portion of the user interface; obtaining one or more service launch objects for placement in the identified portion of the user interface; and sending configuration information to the wireless device over the wireless access network to assist the wireless device in placing the one or more service launch objects in the identified portion of the user interface.
Shim layer for extracting and prioritizing underlying rules for modeling network intents
Systems, methods, and computer-readable media for receiving one or more models of network intents, comprising a plurality of contracts between providers and consumers, each contract containing entries with priority values. Each contract is flattened into a listing of rules and a new priority value is calculated. The listing of rules encodes the implementation of the contract between the providers and the consumers. Each entry is iterated over and added to a listing of entries if it is not already present. For each rule, the one or more entries associated with the contract from which the rule was flattened are identified, and for each given entry a flat rule comprising the combination of the rule and the entry is generated, wherein a flattened priority is calculated based at least in part on the priority value of the given one of given entry and the priority value of the rule.
Shim layer for extracting and prioritizing underlying rules for modeling network intents
Systems, methods, and computer-readable media for receiving one or more models of network intents, comprising a plurality of contracts between providers and consumers, each contract containing entries with priority values. Each contract is flattened into a listing of rules and a new priority value is calculated. The listing of rules encodes the implementation of the contract between the providers and the consumers. Each entry is iterated over and added to a listing of entries if it is not already present. For each rule, the one or more entries associated with the contract from which the rule was flattened are identified, and for each given entry a flat rule comprising the combination of the rule and the entry is generated, wherein a flattened priority is calculated based at least in part on the priority value of the given one of given entry and the priority value of the rule.
Determining optimum software update transmission parameters
Optimum software update transmission parameters are determined and used for transmitting a software update from a host to servers of a computer network. The software update is transmitted while the servers are live and required to meet certain quality of service requirements for tenants of the computer network. Transmission parameters for transmitting the software update are adjusted and updated based on service performance data. Based on iterative adjustments, optimum transmission parameters may be determined. Additionally or alternatively, machine learning is used to generate a model that determines predicted optimum transmission parameters. The predicted optimum transmission parameters may be initially used for transmitting a software update, while the transmission parameters continue to be adjusted throughout transmission.
Determining optimum software update transmission parameters
Optimum software update transmission parameters are determined and used for transmitting a software update from a host to servers of a computer network. The software update is transmitted while the servers are live and required to meet certain quality of service requirements for tenants of the computer network. Transmission parameters for transmitting the software update are adjusted and updated based on service performance data. Based on iterative adjustments, optimum transmission parameters may be determined. Additionally or alternatively, machine learning is used to generate a model that determines predicted optimum transmission parameters. The predicted optimum transmission parameters may be initially used for transmitting a software update, while the transmission parameters continue to be adjusted throughout transmission.
Fault-tolerant and highly available configuration of distributed services
Fault-tolerant and highly available configuration of distributed services including a computer-implemented method for role-based configuration discovery comprising receiving a request comprising an identifier of a role; identifying a first key, in a replica of a distributed configuration store, comprising a first value that matches the role identifier; identifying one or more other key-value pairs associated in the replica with the first key; and returning a response to an entity that sent the request comprising the value of at least one key-value pair that is specific to the role the service has. Also disclosed are techniques for log forwarding.
Distributing service function chain data and service function instance data in a network
In some examples, a computing device comprises a first service function instance to apply a service function and a service function forwarder to: receive a first layer 3 routing protocol route advertisement that includes service function instance data for a second service function instance, the service function instance data indicating a service function type and a service identifier for the service function instance; receive a second layer 3 routing protocol route advertisement that includes service function chain data for a service function chain, the service function chain data indicating a service path identifier and one or more service function items; and send, to the second service function instance and based at least on determining a service function item of the one or more service function items indicates the second service function instance, a packet classified to the service function chain.
Deploying services to multiple public cloud environments using cloud-specific encapsulated communication logic
Techniques are provided for deploying services to multiple public clouds using cloud-specific encapsulated communication logic. One method comprises performing, in response to a request associated with a given public cloud of multiple public clouds having corresponding encapsulated communication logic for communicating with the respective public cloud: obtaining an image for the encapsulated communication logic for the given public cloud; instantiating an orchestration service for the given public cloud using the image for the encapsulated communication logic for the given public cloud, wherein the instantiated orchestration service for the given public cloud makes a connection to an endpoint of the given public cloud using the encapsulated communication logic for the given public cloud; and processing a request to create a service in the given public cloud using the instantiated orchestration service for the given public cloud as a connection gateway to the given public cloud.
Resource lifecycle automation
Policies can be applied to, and enforced for, specific resources by applying a corresponding tag to those resources. An entity, such as a customer of a resource provider, can generate one or more policies to be applied to a set of resources, where those policies can relate to data retention, backup, lifecycle events, and other such aspects. Each policy can be associated with a particular tag, which may comprise a key-value pair to be applied to various resources. A policy enforcement manager can determine the tagged resources and ensure that the relevant policies are applied. The policies can include logic or intelligence for performing a variety of tasks with respect to resources, groups of resources, or types of resources, as identified using the tags.
Resource lifecycle automation
Policies can be applied to, and enforced for, specific resources by applying a corresponding tag to those resources. An entity, such as a customer of a resource provider, can generate one or more policies to be applied to a set of resources, where those policies can relate to data retention, backup, lifecycle events, and other such aspects. Each policy can be associated with a particular tag, which may comprise a key-value pair to be applied to various resources. A policy enforcement manager can determine the tagged resources and ensure that the relevant policies are applied. The policies can include logic or intelligence for performing a variety of tasks with respect to resources, groups of resources, or types of resources, as identified using the tags.