Abstract: Systems and methods are provided for reducing a number of performance metrics generated by network functions to a number of reduced dimension metrics, which can be used to detect anomalous behavior and generate a warning signal of the detected anomalous behavior. The disclosed systems and methods transform raw performance metrics in a high dimensionality space to a reduced number of metrics in a lower dimensionality space through dimensionality reduction techniques. Anomalous behavior in network performance is detected in the high dimensionality space using the reduced dimension metrics. The systems and methods disclosed herein convert the reduced dimension metrics back to the high dimensionality space, such that the performance metrics from network functions can be utilized to understand and address potential problems in the network.

Abstract: The present disclosure provides techniques for optimizing power consumption of Massive-Internet of Things (IoT) devices comprising a plurality of IoT devices. According to some examples, a power optimizer system may obtain one or more operational parameters of a plurality of IoT devices. The power optimizer system may estimate a Power Cost Function (PCF) based on one or more operational parameters to determine power consumption of each IoT device. The power optimizer system may determine a variation in the PCF of one or more IoT devices out of the plurality of IoT devices due to variations in uplink and downlink operations of each IoT device. The power optimizer system may identify one or more optimal operational parameters and then, configure the one or more IoT devices of the plurality of IoT devices with the one or more optimal operational parameters.

Abstract: Systems and methods are provided for predicting system or network failures, such as a degradation in the services provided by a service provider system or network, a system or network outage, etc. In a discovery phase, failure scenarios that can be accurately predicted based on monitored system events are identified. In an operationalization phase, those failure scenarios can be used to design production run-time machines that can be used to predict, in real-time, a future failure scenario. An early warning signal(s) can be sent in advance of the failure scenario occurring.

Abstract: The present disclosure provides techniques for optimizing power consumption of Massive-Internet of Things (IoT) devices comprising a plurality of IoT devices. According to some examples, a power optimizer system may obtain one or more operational parameters of a plurality of IoT devices. The power optimizer system may estimate a Power Cost Function (PCF) based on one or more operational parameters to determine power consumption of each IoT device. The power optimizer system may determine a variation in the PCF of one or more IoT devices out of the plurality of IoT devices due to variations in uplink and downlink operations of each IoT device. The power optimizer system may identify one or more optimal operational parameters and then, configure the one or more IoT devices of the plurality of IoT devices with the one or more optimal operational parameters.

Abstract: The present disclosure relates to a method for handling an event message in a communications system. The method comprises receiving the event message and processing the event message using a pre-processing module of the communications system to determine if the event message is relevant for a management module of the communications system. Responsive to determining that the event message is relevant, the event message is tagged to generate a tagged event message, and the tagged event message is transmitted to the management module. Tagging the event comprises adding, to the event message, a model or pipeline label identifying a model or pipeline applied to the event message by the pre-processing module, a status identifier indicating a status of the event message following a process or set of processes performed by the model or pipeline, and an input identifier for identifying features or variables of the event message that were used as inputs to the model or pipeline.

Abstract: Systems and methods are provided for predicting system or network failures, such as a degradation in the services provided by a service provider system or network, a system or network outage, etc. In a discovery phase, failure scenarios that can be accurately predicted based on monitored system events are identified. In an operationalization phase, those failure scenarios can be used to design production run-time machines that can be used to predict, in real-time, a future failure scenario. An early warning signal(s) can be sent in advance of the failure scenario occurring.

Abstract: Systems and methods are provided for predicting system or network failures, such as a degradation in the services provided by a service provider system or network, a system or network outage, etc. In a discovery phase, failure scenarios that can be accurately predicted based on monitored system events are identified. In an operationalization phase, those failure scenarios can be used to design production run-time machines that can be used to predict, in real-time, a future failure scenario. An early warning signal(s) can be sent in advance of the failure scenario occurring.

Abstract: Various aspects of the subject technology relate to methods, systems, and machine-readable media for self-correlating network operations. The method includes receiving a stream of network messages, the stream of network messages comprising a variety of network events for various network devices. The method also includes identifying patterns within the stream of network messages, the patterns comprising groupings of the variety of network events. The method also includes determining for each pattern an appropriate operationalization scenario. The method also includes operationalizing the patterns as correlation rules in a correlation engine to automatically detect or predict network alarms from input provided by a fault management system.

Abstract: The present disclosure relates to a method for handling an event message in a communications system. The method comprises receiving the event message and processing the event message using a pre-processing module of the communications system to determine if the event message is relevant for a management module of the communications system. Responsive to determining that the event message is relevant, the event message is tagged to generate a tagged event message, and the tagged event message is transmitted to the management module. Tagging the event comprises adding, to the event message, a model or pipeline label identifying a model or pipeline applied to the event message by the pre-processing module, a status identifier indicating a status of the event message following a process or set of processes performed by the model or pipeline, and an input identifier for identifying features or variables of the event message that were used as inputs to the model or pipeline.

Abstract: Systems and methods are provided for predicting system or network failures, such as a degradation in the services provided by a service provider system or network, a system or network outage, etc. In a discovery phase, failure scenarios that can be accurately predicted based on monitored system events are identified. In an operationalization phase, those failure scenarios can be used to design production run-time machines that can be used to predict, in real-time, a future failure scenario. An early warning signal(s) can be sent in advance of the failure scenario occurring.

Abstract: Various aspects of the subject technology relate to methods, systems, and machine-readable media for self-correlating network operations. The method includes receiving a stream of network messages, the stream of network messages comprising a variety of network events for various network devices. The method also includes identifying patterns within the stream of network messages, the patterns comprising groupings of the variety of network events. The method also includes determining for each pattern an appropriate operationalization scenario. The method also includes operationalizing the patterns as correlation rules in a correlation engine to automatically detect or predict network alarms from input provided by a fault management system.