In accordance with one or more embodiments, aspects of the disclosure may provide efficient, effective, and convenient ways of managing network devices. In particular, a client router may connect to an upstream virtual gateway. The virtual gateway may manage a large number of client devices. Each client router may be represented virtually within the gateway as a virtual router. The virtual gateways may be distributed regionally, in order to manage large numbers of client routers and/or to reduce transmission delays. The virtual gateways may be managed by a gateway controller. The gateway controller may be centralized, and perform various configuration functions, such as configurations for hardware, logical networking, or content access policies. In some instances, messages sent between the gateway controller using a first protocol and the client router using a second protocol may be translated by a protocol agent.

In accordance with an embodiment, described herein is a system and method for use of virtual reality and/or augmented reality with data center operations and cloud infrastructure services. The approach leverages virtual reality and/or augmented reality, and insights from various sources of data describing the operation of the data center, including data center analytics, for facilitating in-situ diagnostics, operations, monitoring, maintenance, repair, health prognostics, and remote collaboration, toward enhancing the efficiency of managing and running data centers. In accordance with an embodiment, the system can operate with VR/AR devices that can be provided as VR/AR headsets or other devices, that include sensors that measure a data center operator's position, orientation, and movement within a cloud infrastructure or data center environment, and can display a visualization associated with the physical devices of the data center environment, including where appropriate information from other sources useful in performing data center operations.

A machine learning technology-based fault management system for network equipment that supports SDN OpenFlow protocol that includes an L2 switch or a router, which is network equipment connected to a client; and an Artificial Intelligence (AI)-based Software Defined Network (SDN) controller requested for management commands for each scenario when the L2 switch or the router, which is network equipment connected to the client, encounters a network fault so that a Simple Network Management System (SNMP) agent installed in the L2 switch and the router determines the type of fault occurred on a network and AI is employed to recover from a current fault through learning results from past data. An effect is achieved that not only service quality is improved through real-time fault management using an AI-based automatic response against a network fault but also a fault is precisely overcome by using the AI-based automatic response.

A machine learning technology-based fault management system for network equipment that supports SDN OpenFlow protocol that includes an L2 switch or a router, which is network equipment connected to a client; and an Artificial Intelligence (AI)-based Software Defined Network (SDN) controller requested for management commands for each scenario when the L2 switch or the router, which is network equipment connected to the client, encounters a network fault so that a Simple Network Management System (SNMP) agent installed in the L2 switch and the router determines the type of fault occurred on a network and AI is employed to recover from a current fault through learning results from past data. An effect is achieved that not only service quality is improved through real-time fault management using an AI-based automatic response against a network fault but also a fault is precisely overcome by using the AI-based automatic response.

A computer-implemented method, system and computer program product for performing real-time monitoring of machine learning models. Real-time model state data and metadata (e.g., operating dataset) of the machine learning models located within an orchestration plane of a network are collected by agents located within the machine learning models. The portion of the collected real-time model state data and metadata that is to be provided to the user by the service orchestrator (configured to monitor the machine learning models in the service orchestration plane via the use of agents in the machine learning models) is selected and marked. The marked collected real-time model state data and metadata are then provided to the user by the service orchestrator. In this manner, real-time monitoring of the machine learning models in the orchestration plane, such as the service orchestration plane, of a broadband cellular network (e.g., fifth generation broadband cellular network) is achieved.

Systems and methods of automatically creating and operating a Maintenance End Point (MEP) include, at a slave/reactive network device, receiving an Operations, Administration, and Maintenance (OAM) Protocol Data Unit (PDU) with a destination Media Access Control (MAC) address equal to an interface address of the slave/reactive network device; automatically creating the MEP based on the received OAM PDU and attributes contained in a header of the OAM PDU, wherein the MEP is with a master/active network device; and operating an OAM session with the master/active network device including exchanging Continuity Check Messages (CCMs) with an interval learned from received CCMs from the master/active network device. The systems and methods can further include automatically deleting the MEP responsive to failing to receive any OAM PDUs from the master/active network device during the operating for a predetermined time.

Systems and methods of automatically creating and operating a Maintenance End Point (MEP) include, at a slave/reactive network device, receiving an Operations, Administration, and Maintenance (OAM) Protocol Data Unit (PDU) with a destination Media Access Control (MAC) address equal to an interface address of the slave/reactive network device; automatically creating the MEP based on the received OAM PDU and attributes contained in a header of the OAM PDU, wherein the MEP is with a master/active network device; and operating an OAM session with the master/active network device including exchanging Continuity Check Messages (CCMs) with an interval learned from received CCMs from the master/active network device. The systems and methods can further include automatically deleting the MEP responsive to failing to receive any OAM PDUs from the master/active network device during the operating for a predetermined time.

Described are various embodiments of a system and method in which device-identifying data can be used to uniquely recognize and optionally track and report on device activity at one or more hotspot and/or Wi-Fi locations by way of the creation and management of a device and/or visit profile uniquely associated with such devices and stored in a network accessible knowledge base.

A method for communicating between apparatuses, comprises: in a first apparatus, generating a second packet according to a second protocol, the second packet including a first packet according to a first protocol; in the first apparatus, sending the generated second packet to a second apparatus; in the second apparatus, receiving the second packet; in the second apparatus, determining whether a response to the first packet included in the second packet is possible; and in the second apparatus, in a case where it is determined that a response to the first packet is impossible, including status information corresponding to a cause for the impossibility of the response in a response packet corresponding to the second packet and sending the response packet to the first apparatus.

Methods, techniques, computer program products, apparatus, devices, etc., used in connection with DSL Management Interfaces, significantly improve the management capabilities of a DSL network and/or improve testing relating to DSL equipment and services by permitting better control and operation of a DSL system, including implementation of timestamping for more accurate measurement, monitoring and control of a system. Timestamping further allows customized data collection techniques, where a DSL line can be measured or monitored at intervals whose frequency depends on the line's stability. Moreover, data parameter read and control parameter write operations are presented in conjunction with the use of timestamping. Also, control and operation of a DSL system is enhanced by implementing bit-loading that minimizes, eliminates or otherwise mitigates the amount by which the SNR margin per tone exceeds a maximum SNR margin quantity, where such bit-loading can be selected through an appropriate interface.