Abstract: Systems and methods provide a MEC policy optimization service. A network device applies, in a first edge cluster of the application service layer network, a policy for an application service that supports a customer application; receives network performance data related to execution of the application service; identifies, based on the network performance data, an improved policy to optimize the first policy for the customer application in the first edge cluster; and sends a model of the improved policy to a central network device for the application service layer network.

Abstract: A device receives, from a first compute instance, a Remote Latency Call, that includes a target latency measure, a function, and one or more arguments for the function. The device clusters a plurality of compute instances into multiple clusters of compute instances based on a respective latency measure associated with each compute instance of the plurality of compute instances, and determines a first cluster of compute instances from the multiple clusters of compute instances that satisfies the target latency measure. The device causes a second compute instance within the first cluster of compute instances to execute the function, using the one or more arguments, and return one or more results.

Abstract: A system comprises a Network Exposure Function (NEF) and a Session Management Function (SMF). The MF may be configured to: receive, from the NEF, a first request to subscribe to a notification service offered by the SMF; send a create session request to a User Plane Function (UPF); receive a first response from the UPF to the create session request, wherein the first response includes binding information that pertains to an association between a User Equipment device (UE) identifier (ID) and Internet Protocol (IP) addresses; and send, as part of the notification service, a message that includes the binding information to the NEF.

Abstract: A device may include a memory storing instructions and processor configured to execute the instructions to receive, by a first blockchain node and via a first base station, a message from a first Internet of Things (IoT) device to a second IoT device, wherein the device corresponds to a first multi-access edge computing (MEC) device located in a first MEC network associated with the first base station. The processor may be further configured to authenticate, by the first blockchain node, the first IoT device using a blockchain associated with a group of IoT devices and send, by the first blockchain node, the message to a second blockchain node in a second MEC device in a second MEC network associated with a second base station servicing the second IoT device, in response to authenticating the first IoT device using the blockchain associated with the group of IoT devices.

Abstract: A device receives, from a first compute instance, a Remote Latency Call, that includes a target round trip time (RTT), a function, and one or more arguments for the function. The device determines, responsive to the Remote Latency Call, a first cluster of compute instances that satisfies the target RTT. The device migrates code associated with the function to a second compute instance within the first cluster of compute instances, and passes the arguments to the second compute instance for execution of the function.

Abstract: A device may include a memory storing instructions and processor configured to execute the instructions to receive, by a first blockchain node and via a first base station, a message from a first Internet of Things (IoT) device to a second IoT device, wherein the device corresponds to a first multi-access edge computing (MEC) device located in a first MEC network associated with the first base station. The processor may be further configured to authenticate, by the first blockchain node, the first IoT device using a blockchain associated with a group of IoT devices and send, by the first blockchain node, the message to a second blockchain node in a second MEC device in a second MEC network associated with a second base station servicing the second IoT device, in response to authenticating the first IoT device using the blockchain associated with the group of IoT devices.

Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based resource management service is provided. A network device obtains network and end device information, and uses machine learning to determine whether to adjust an auto-scaling rule pertaining to the provisioning of an application service. The network device generates a modified auto-scaling rule based on the analysis.

Abstract: An illustrative latency service system identifies, in response to a request by a user equipment (UE) device for performance of a distributed computing service, a latency performance level to which the UE device is subscribed. The system obtains real-time latency performance data for a distributed computing network that is to perform the distributed computing service for the UE device in response to the request, and, based on the real-time latency performance data, determines a characteristic of a geographic zone in which the UE device is located. The characteristic is associated with latency performance that the distributed computing network is capable of providing in the geographic zone. Based on the latency performance level and the characteristic of the geographic zone, the system arranges for the performance of the distributed computing service for the UE device by the distributed computing network. Corresponding methods and systems are also disclosed.

Abstract: A method, a device, and a non-transitory storage medium are described in which a edge content distribution service is provided. The edge content distribution service may include the distribution of static content among multi-access edge computing networks. The edge content distribution service may include tiered overlay networks that are correlated to performance metrics associated with application services, static contents of the application services, and/or the acquisition and delivery of the static contents. The edge content distribution service also consider available network resources at the multi-access edge computer networks and the overlay networks of the tiered architecture.

Abstract: An illustrative multi-access server orchestration system determines a subset of a set of multi-access servers associated with a communication network. The subset corresponds to a service area of a network anchor point through which network traffic on the communication network is routed to a client device. The multi-access server orchestration system orders the multi-access servers included within the subset to form an ordered listing of multi-access servers from a most-preferred to a least-preferred multi-access server for fulfilling a service request for the client device. The ordered listing designates a first multi-access server included in the subset and located more proximate to the network anchor point than to the client device to be more highly preferred for fulfilling the service request than a second multi-access server included in the subset and located more proximate to the client device than to the network anchor point. Corresponding methods and systems are also disclosed.

Abstract: An exemplary edge compute orchestration system that is communicatively coupled with a set of edge compute nodes in a communication network receives a task assignment request generated by a user equipment (“UE”) device coupled to the network. The request is associated with an edge compute task that is to be performed in furtherance of an application executing on the UE device. The system also accesses node characterization data for the set of nodes and manages a node selection policy configured to facilitate a balancing of node performance and node efficiency when assigning edge computing tasks to different nodes in the set. The system selects a node for performance of the edge compute task from the set of nodes in response to the request, based on the node characterization data, and in accordance with the selection policy. The system assigns the edge compute task to be performed by the selected node.

Abstract: A device receives, from a first compute instance, a Remote Latency Call, that includes a target round trip time (RTT), a function, and one or more arguments for the function. The device determines, responsive to the Remote Latency Call, a first cluster of compute instances that satisfies the target RTT. The device migrates code associated with the function to a second compute instance within the first cluster of compute instances, and passes the arguments to the second compute instance for execution of the function.

Abstract: An illustrative multi-access server orchestration system obtains, by way of a communication network, a service request for a client device. The service request indicates a client identity of the client device and represents a request for orchestration of service to be provided to the client device by a multi-access server of a set of multi-access servers associated with the communication network. In response to the service request and based on the client identity, the orchestration system identifies a network anchor point through which network traffic on the communication network is routed to the client device. The orchestration system also determines a subset of the set of multi-access servers. The subset may correspond to a service area of the identified network anchor point and may include one or more multi-access servers capable of fulfilling the service request for the client device. Corresponding methods and systems are also disclosed.

Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based resource management service is provided. A network device obtains network and end device information, and uses machine learning to determine whether to adjust an auto-scaling rule pertaining to the provisioning of an application service. The network device generates a modified auto-scaling rule based on the analysis.

Abstract: A device may include a memory storing instructions and processor configured to execute the instructions to receive, by a first blockchain node and via a first base station, a message from a first Internet of Things (IoT) device to a second IoT device, wherein the device corresponds to a first multi-access edge computing (MEC) device located in a first MEC network associated with the first base station. The processor may be further configured to authenticate, by the first blockchain node, the first IoT device using a blockchain associated with a group of IoT devices and send, by the first blockchain node, the message to a second blockchain node in a second MEC device in a second MEC network associated with a second base station servicing the second IoT device, in response to authenticating the first IoT device using the blockchain associated with the group of IoT devices.

Abstract: A method, a device, and a non-transitory storage medium are described in which a edge content distribution service is provided. The edge content distribution service may include the distribution of static content among multi-access edge computing networks. The edge content distribution service may include tiered overlay networks that are correlated to performance metrics associated with application services, static contents of the application services, and/or the acquisition and delivery of the static contents. The edge content distribution service also consider available network resources at the multi-access edge computer networks and the overlay networks of the tiered architecture.

Abstract: A computer device may include a memory storing instructions and processor configured to execute the instructions to provide a test configuration to a plurality of multi-access edge computing (MEC) devices; collect test results associated with the provided test configuration from the plurality of MEC devices; and obtain capability information associated with particular ones of the plurality of MEC devices. The computer device may be further configured to generate a test report that relates one or more parameters included in the test results and the obtained capability information to particular ones of the plurality of MEC devices; and use the generated test report to select a MEC device from the plurality of MEC devices for a user equipment (UE) device requesting an application session.

Abstract: Systems and methods provide a distributed runtime logging and transaction control service for federated edge and public cloud services. A network device in an application service layer network receives a request from a network function, logs a record of the request, and publishes the record of the request in a distributed ledger. The network device receives a validation decision for the request, wherein the validation decision is provided from a main transaction control system for the application service layer network. The network device initiates a positive response to the request by the first edge cluster when the validation decision indicates an approval of the request, and initiates a denial response to the request when the validation decision indicates a disapproval of the request.

Abstract: A method, a device, and a non-transitory storage medium are described in which a lifecycle management service is provided. A network device of an application service network may use the number of requests from end devices as a basis to determine whether to terminate a virtual network device. The network device may compare the number of requests to management information that includes a threshold number of requests value. The network device may also instantiate a virtual network device based on receipt of a domain name system request and radio network information.

Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based resource management service is provided. A network device obtains network and end device information, and uses machine learning to determine whether to adjust an auto-scaling rule pertaining to the provisioning of an application service. The network device generates a modified auto-scaling rule based on the analysis.