Abstract: Described herein are devices, systems, methods, and processes for benchmarking sustainability in information technology (IT) infrastructure. A data collection module may gather sustainability-related telemetry metrics from various sources, such as, but not limited to, network elements, internet of things (IoT) endpoints, and/or building management systems. A computation module can establish sustainability benchmarks based on the collected metrics and one or more network sustainability indexes. The telemetry metrics may include power usage per device, per device component, per device family, and/or per network. The telemetry metrics can also encompass power saved due to switching devices to the power saving mode or due to device configuration. The system may normalize the benchmark to eliminate bias towards smaller networks.

Abstract: Described herein are devices, systems, methods, and processes for benchmarking sustainability in information technology (IT) infrastructure. A data collection module may gather sustainability-related telemetry metrics from various sources, such as, but not limited to, network elements, internet of things (IoT) endpoints, and/or building management systems. A computation module can establish sustainability benchmarks based on the collected metrics and one or more network sustainability indexes. The telemetry metrics may include power usage per device, per device component, per device family, and/or per network. The telemetry metrics can also encompass power saved due to switching devices to the power saving mode or due to device configuration. The system may normalize the benchmark to eliminate bias towards smaller networks.

Abstract: Described herein are devices, systems, methods, and processes for benchmarking sustainability in information technology (IT) infrastructure. A data collection module may gather sustainability-related telemetry metrics from various sources, such as, but not limited to, network elements, internet of things (IoT) endpoints, and/or building management systems. A computation module can establish sustainability benchmarks based on the collected metrics and one or more network sustainability indexes. The telemetry metrics may include power usage per device, per device component, per device family, and/or per network. The telemetry metrics can also encompass power saved due to switching devices to the power saving mode or due to device configuration. The system may normalize the benchmark to eliminate bias towards smaller networks.

Abstract: Described herein are devices and methods for calculating a network sustainability index (NSI) and providing specific remediation/action plans to achieve better sustainability in network infrastructures. The NSI is calculated based on one or more of three main parameters: a reporting parameter, an optimization parameter, and/or a device energy efficiency rating. The reporting parameter may evaluate the capability of each device to report its power consumption, considering factors such as granularity, component-level reporting, and reporting frequency. The optimization parameter may assess whether a device participates in network energy optimization. The device energy efficiency rating may evaluate the energy efficiency of each device. A recommendation portal offers actionable insights and remediation plans based on the calculated NSI.

Abstract: Devices, networks, systems, methods, and processes for optimizing the energy consumption of a communication network are described herein. A network optimizer can group multiple network devices in the communication network into one or more energy management groups. The network optimizer may optimize an energy consumption of the one or more energy management groups by generating and transmitting one or more energy optimization configurations. The network optimizer can generate a device replacement list for an energy management group to suggest one or more replacement devices for every replaceable device that is not capable of implementing an energy optimization function. The network optimizer may provide an interactive dashboard and may further facilitate a network operator to modify the energy management groups through the interactive dashboard.

Abstract: Described herein are devices and methods for calculating a network sustainability index (NSI) and providing specific remediation/action plans to achieve better sustainability in network infrastructures. The NSI is calculated based on one or more of three main parameters: a reporting parameter, an optimization parameter, and/or a device energy efficiency rating. The reporting parameter may evaluate the capability of each device to report its power consumption, considering factors such as granularity, component-level reporting, and reporting frequency. The optimization parameter may assess whether a device participates in network energy optimization. The device energy efficiency rating may evaluate the energy efficiency of each device. A recommendation portal offers actionable insights and remediation plans based on the calculated NSI.

Abstract: The present technology addresses a need in the art for an automated tool that allows users to create network-based custom workflows for networks and associated management applications. The users do not need to have in-depth network knowledge to work with the tool or even write any code/script. The tool provides the users with a flexible graphical user interface for automated troubleshooting, network provisioning, and closed-loop automation. Further, the tool uses a domain-independent semantic machine reasoning engine as an underlying engine and a mock data engine to test and validate network-based workflows created by the users.

Abstract: Described herein are devices and methods for calculating a network sustainability index (NSI) and providing specific remediation/action plans to achieve better sustainability in network infrastructures. The NSI is calculated based on one or more of three main parameters: a reporting parameter, an optimization parameter, and/or a device energy efficiency rating. The reporting parameter may evaluate the capability of each device to report its power consumption, considering factors such as granularity, component-level reporting, and reporting frequency. The optimization parameter may assess whether a device participates in network energy optimization. The device energy efficiency rating may evaluate the energy efficiency of each device. A recommendation portal offers actionable insights and remediation plans based on the calculated NSI.

Abstract: Presented herein are methodologies for implementing a system and apparatus to estimate a network disruption index and undertake a mitigation action accordingly. A method includes calculating a network disruption index based on at least a disruption score associated with a service request measure, an end-of-life measure, a security incident response measure and a return material authorization measure for respective hardware devices in a network, comparing the network disruption index to a predetermined threshold, and when the network disruption index is above the predetermined threshold, identifying one or more of the hardware devices in the network for a mitigation action and implementing the mitigation action.

Abstract: The present technology addresses a need in the art for an automated tool that allows users to create network-based custom workflows for networks and associated management applications. The users do not need to have in-depth network knowledge to work with the tool or even write any code/script. The tool provides the users with a flexible graphical user interface for automated troubleshooting, network provisioning, and closed-loop automation. Further, the tool uses a domain-independent semantic machine reasoning engine as an underlying engine and a mock data engine to test and validate network-based workflows created by the users.

Abstract: The present technology addresses a need in the art for a closed-loop automation system of a managed network that detects and fixes an issue in the managed network with minimal to no human intervention. The closed-loop automation system employs a domain-independent semantic machine reasoning engine as an underlying engine. After detecting the issue in the managed network, the system searches through a knowledge base to find a match for the detected issue. The knowledge base includes previously collected knowledge from subject matter experts and maintenance teams. The system then proposes a remedial action to a user of the managed network for approval. After approval by the user, the system implements the remedial action.

Abstract: The present technology addresses a need in the art for an automated tool that allows users to create network-based custom workflows for networks and associated management applications. The users do not need to have in-depth network knowledge to work with the tool or even write any code/script. The tool provides the users with a flexible graphical user interface for automated troubleshooting, network provisioning, and closed-loop automation. Further, the tool uses a domain-independent semantic machine reasoning engine as an underlying engine and a mock data engine to test and validate network-based workflows created by the users.

Abstract: The present technology addresses a need in the art for a closed-loop automation system of a managed network that detects and fixes an issue in the managed network with minimal to no human intervention. The closed-loop automation system employs a domain-independent semantic machine reasoning engine as an underlying engine. After detecting the issue in the managed network, the system searches through a knowledge base to find a match for the detected issue. The knowledge base includes previously collected knowledge from subject matter experts and maintenance teams. The system then proposes a remedial action to a user of the managed network for approval. After approval by the user, the system implements the remedial action.

Abstract: The present technology addresses a need in the art for an automated tool that allows users to create network-based custom workflows for networks and associated management applications. The users do not need to have in-depth network knowledge to work with the tool or even write any code/script. The tool provides the users with a flexible graphical user interface for automated troubleshooting, network provisioning, and closed-loop automation. Further, the tool uses a domain-independent semantic machine reasoning engine as an underlying engine and a mock data engine to test and validate network-based workflows created by the users.

Abstract: Presented herein are methodologies for implementing a system and apparatus to estimate a network disruption index and undertake a mitigation action accordingly. A method includes calculating a network disruption index based on at least a disruption score associated with a service request measure, an end-of-life measure, a security incident response measure and a return material authorization measure for respective hardware devices in a network, comparing the network disruption index to a predetermined threshold, and when the network disruption index is above the predetermined threshold, identifying one or more of the hardware devices in the network for a mitigation action and implementing the mitigation action.

Abstract: Presented herein are methodologies for implementing a system and apparatus to estimate a network disruption index and undertake a mitigation action accordingly. A method includes calculating a network disruption index based on at least a disruption score associated with a service request measure, an end-of-life measure, a security incident response measure and a return material authorization measure for respective hardware devices in a network, comparing the network disruption index to a predetermined threshold, and when the network disruption index is above the predetermined threshold, identifying one or more of the hardware devices in the network for a mitigation action and implementing the mitigation action.

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: A Network Function (NF) entity in a telecommunication network receives blockchain credentials associated with UE and selects a Blockchain Charging Function (BCF). The NF entity further generates a Charging Data Record (CDR) corresponding to network resources, and sends a charging request based on the CDR (and policy rules) to the BCF entity over a blockchain network interface. The BCF entity sends a confirmation of the charging request, and the NF entity, based on the confirmation, provisions the network resources to the UE.

Abstract: Presented herein are methodologies for implementing a system and apparatus to estimate a network disruption index and undertake a mitigation action accordingly. A method includes calculating a network disruption index based on at least a disruption score associated with a service request measure, an end-of-life measure, a security incident response measure and a return material authorization measure for respective hardware devices in a network, comparing the network disruption index to a predetermined threshold, and when the network disruption index is above the predetermined threshold, identifying one or more of the hardware devices in the network for a mitigation action and implementing the mitigation action.

Abstract: A Network Function (NF) entity in a telecommunication network receives blockchain credentials associated with UE and selects a Blockchain Charging Function (BCF). The NF entity further generates a Charging Data Record (CDR) corresponding to network resources, and sends a charging request based on the CDR (and policy rules) to the BCF entity over a blockchain network interface. The BCF entity sends a confirmation of the charging request, and the NF entity, based on the confirmation, provisions the network resources to the UE.