Abstract: A network function (NF) entity in a communication network receives session request data associated with a User Equipment (UE), which includes blockchain authentication data. The NF entity selects a Blockchain Authentication Function (BAF) entity based on the session request data, and exchanges at least a portion of the blockchain authentication data with the BAF entity over a blockchain network interface. The NF entity further receives authentication confirmation data from the BAF entity over the blockchain network interface, and establishes a data session associated with the UE based on the authentication confirmation data.

Abstract: Presented herein are techniques for automatically creating communities of network-connected devices, i.e., Internet of Thing (IoT) devices. One or more of a plurality of network-connected devices are identified based on one or more policies that define one or more communities of network-connected devices. A community of network-connected devices includes network-connected devices that share common functional, physical or relational attributes. Information is stored that indicates the one or more communities of which each of the one or more of the plurality of network-connected devices is a member based on the policies that define the one or more communities and functional, physical or relational attributes of the one or more of the plurality of network-connected devices.

Abstract: A plurality of data sets are compiled in a memory. A first data set comprises a location mapping of an item of interest within a predetermined environment, and a second set comprises energy usage data within the predetermined environment. The first data set is correlated, via a processor, with the second data set to locate a subset of energy usage data in the second data set associated with the item of interest. A third data set is generated by combining the first data set with the subset of energy usage data in the second data set to increase an accuracy of the location mapping of the item of interest based upon the subset of energy usage data. The third data set is applied to a computing resource.

Abstract: In one embodiment, a processor receives data indicative of a plurality of conversations involving a primary user. The processor identifies a subset of the plurality of conversations that are regarding a particular topic. The processor adds a conversation to the subset based on a match between one or more keywords in the conversation matching a list of keywords associated with the particular topic. The processor uses a machine learning-based model to identify one or more context characteristics of the conversations in the identified subset. The processor updates the subset of conversations by adding at least one of the conversations to the subset based on the at least one conversation having at least one context characteristic identified by the machine learning-based model. The processor provides data indicative of the updated subset of conversations to a user interface for review by the primary user.

Abstract: A network slice manager receives a workload mobility request to add network resources to a domain in the communication network, and authenticates a virtual network function (VNF) with a blockchain authentication function (BAF) over a blockchain network interface based on the workload mobility request. The network slice manager further receives an indication of a successful authentication from the BAF, and instantiates the VNF in the domain of the communication network based on the indication of the successful authentication. Notably, these authentication processes may be readily adapted to instantiate new network resources or migrate existing network resources between domains.

Abstract: A network function (NF) entity in a communication network receives authentication data associated with a User Equipment (UE), determines the UE supports a blockchain registration procedure based on the authentication data, exchanges authentication messages with a Blockchain Roaming Broker (BRB) entity over a blockchain network interface, receives a blockchain authentication confirmation from the BRB entity, and registers the UE with the core network based on the blockchain authentication confirmation.

Abstract: Techniques are provided for predictively activating physical servers. Embodiments determine a plurality of physical servers that are available within one or more data centers. A plurality of virtual workload deployment requests that were processed within the one or more data centers during a historical window of time is determined. Embodiments classify each of the plurality of virtual workload deployment requests into one of a plurality of categories. A respective neural network prediction model is generated for each of the plurality of categories, based on the virtual workload deployment requests classified into the respective category. Embodiments then determine a number of physical servers to have active at a future moment in time, using the generated neural network prediction models.

Abstract: Techniques are presented herein to monitor a plurality of big data sources in order to dynamically identify keywords. The big data sources are analyzed to classify the keywords as related to either a technical problem or to a solution to the technical problem. In addition, data associated with the keywords is weighted based on one or more attributes of the data and stored in a database in a problem-solution format.

Abstract: Techniques are provided for predictively activating physical servers. Embodiments determine a plurality of physical servers that are available within one or more data centers. A plurality of virtual workload deployment requests that were processed within the one or more data centers during a historical window of time is determined. Embodiments classify each of the plurality of virtual workload deployment requests into one of a plurality of categories. A respective neural network prediction model is generated for each of the plurality of categories, based on the virtual workload deployment requests classified into the respective category. Embodiments then determine a number of physical servers to have active at a future moment in time, using the generated neural network prediction models.

Abstract: A plurality of data sets are compiled in a memory. A first data set comprises a location mapping of an item of interest within a predetermined environment, and a second set comprises energy usage data within the predetermined environment. The first data set is correlated, via a processor, with the second data set to locate a subset of energy usage data in the second data set associated with the item of interest. A third data set is generated by combining the first data set with the subset of energy usage data in the second data set to increase an accuracy of the location mapping of the item of interest based upon the subset of energy usage data. The third data set is applied to a computing resource.

Abstract: Techniques are presented herein to monitor a plurality of big data sources in order to dynamically identify keywords. The big data sources are analyzed to classify the keywords as related to either a technical problem or to a solution to the technical problem. In addition, data associated with the keywords is weighted based on one or more attributes of the data and stored in a database in a problem-solution format.

Abstract: Presented herein are techniques for automatically creating communities of network-connected devices, i.e., Internet of Thing (IoT) devices. One or more of a plurality of network-connected devices are identified based on one or more policies that define one or more communities of network-connected devices. A community of network-connected devices includes network-connected devices that share common functional, physical or relational attributes. Information is stored that indicates the one or more communities of which each of the one or more of the plurality of network-connected devices is a member based on the policies that define the one or more communities and functional, physical or relational attributes of the one or more of the plurality of network-connected devices.

Abstract: A system for streamlining a network management system (NMS) includes scanning, via a mobile device, a code on a hardware or software component; and determining, via the mobile device, whether the hardware or software component is a network component (NC), based on the scanned code. The hardware or software component may be a call manager, server, client device, interface card, repeater, hub, bridge, switch, router, or firewall, for example.

Abstract: In one embodiment, a central service request management system for managing service requests across a plurality of ticketing systems. The central service request management system includes translation logic for receiving service request messages from the plurality of ticketing systems and translating the service request messages to a standardized format, where each service request message is associated with a respective service request. Additionally, the central service request management system includes data storage logic for maintaining a status of service requests based on the translated service request messages. The central service request management system further includes routing logic for identifying one of the plurality of ticketing systems to transmit each translated service request message to based on the maintained status of the service request associated with the service request message, and for transmitting the translated service request message to the identified ticketing system.

Abstract: Techniques are disclosed for hiding sensitive information from a provider of support services. In one embodiment, a first network device determines that network device information includes non-sensitive data and sensitive data. In response to the determining, the first network device generates mapping data that maps dummy information to the sensitive data. The first network device generates output data that comprises the non-sensitive data and the dummy data and sends the output data to a second network device. In other embodiments, the user may select the network parameters that are sensitive. The first network device may also receive first report data from the second network device that identifies a network problem and includes the dummy data and generate second report data by using the mapping data to replace the dummy information with the sensitive data.

Abstract: A system for streamlining a network management system (NMS) includes scanning, via a mobile device, a code on a hardware or software component; and determining, via the mobile device, whether the hardware or software component is a network component (NC), based on the scanned code. The hardware or software component may be a call manager, server, client device, interface card, repeater, hub, bridge, switch, router, or firewall, for example.

Abstract: In one embodiment, a method for securely transferring entitled data from one or more devices in a customer's network to a vendor's network via a public network is described. The data is obtained from a collection module communicatively coupled to the devices. The obtained data is transformed into a format that is recognized by a backend server present in the vendor's network. The transformed data is then assorted by associating the transformed data with corresponding one or more devices. Finally, the assorted data is then encrypted and sent to the backend server securely via the public network along with entitlement attributes corresponding to the one or more devices.

Abstract: A system for streamlining a network management system (NMS) includes scanning, via a mobile device, a code on a hardware or software component; and determining, via the mobile device, whether the hardware or software component is a network component (NC), based on the scanned code. The hardware or software component may be a call manager, server, client device, interface card, repeater, hub, bridge, switch, router, or firewall, for example.

Abstract: A system for streamlining a network management system (NMS) includes scanning, via a mobile device, a code on a hardware or software component; and determining, via the mobile device, whether the hardware or software component is a network component (NC), based on the scanned code. The hardware or software component may be a call manager, server, client device, interface card, repeater, hub, bridge, switch, router, or firewall, for example.

Abstract: A method is provided in one example and includes verifying a storage capacity of a network element coupled to an end device over a network connection. The method also includes executing script provided in the network element, which is configured for exchanging packets in a network environment. The script initiates a collection of data being retrieved from the end device. The data can be Fault, Configuration, Accounting, Performance, and Security (FCAPS) data associated with the end device. The data collected from the end device is communicated to a next destination.