Systems and methods implemented by a workflow engine include obtaining data from a plurality of interfaces and network functions, NF, in one or more datacenters associated with a 5G core, 5GC, network, wherein the data relates to lifecycle management in the 5GC network; responsive to analyzing the data with a set of key performance indicators, KPI, and rules associated therewith that are formulated to detect anomalies that impact the lifecycle management, generating an alarm based on the analyzing based on detection of an anomaly that impacts the lifecycle management; and creating a workflow based on the alarm and pushing the workflow to the 5GC network via an orchestrator.

The present invention provides a Learn. Map, Measure and Assure (LEMMA) framework deployed as multi-cloud platform. The LEMMA framework comprises a learn module configured to receive a service level agreement, convert the service level agreement to a service level objective, refine the service level objective and store the service level objective in machine-readable record, and a map module configured to determine a list of available resources via a service broker. The service broker is configured to select a best priced resources from a list of available resources that matches with the machine-readable service level objective. A measure module configured to generate monitored data by continuously monitoring the allocated best priced resources. An assure module configured to perform iterative refinement of the machine-readable service level objective in response to comparison of the machine-readable service level objective with the monitored data and transmit the refined service level objective to the service broker.

A virtual function performance analysis system and an analysis method thereof are disclosed. The virtual function performance analysis method includes: monitoring performance of at least one virtual function of a virtual network function application on a virtual platform having at least one physical resource and at least one virtual resource; monitoring and recording an actual value of each of performance indicators of each of physical resources, each of virtual resources and each of virtual functions; comparing the actual value of each of performance indicators with the associated expected value and/or threshold value to obtain a comparison result; and analyzing system performance according to the comparison result.

Embodiments of a system and method for providing DRX enhancements in LTE systems are generally described herein. In some embodiments, a system control module is provided for controlling communications via a communications interface. A processor is coupled to the system control module and is arranged to implement an inactivity timer and an on-duration timer for determining an active time for monitoring subframes on the physical downlink control channel for control signals, the processor further monitoring subframes after the active time.

Systems and methods for analyzing the root cause of service failures and service degradation in a telecommunications network are provided. A method, according to one implementation, includes a step of receiving any of Performance Monitoring (PM) data, standard path alarms, service PM data, standard service alarms, network topology information, and configuration logs from equipment configured to provide services in a network. The method also includes a step of automatically detecting a root cause of a service failure or signal degradation from the available PM data, standard path alarms, service PM data, standard service alarms, network topology information, and configuration logs.

A cloud gateway outage risk detector can receive, by an event listener module, user session data associated with a plurality of user sessions over a cloud gateway. The event listener module can store the data in a database. A run-time collection module can obtain at least a portion of the data. The run-time collection module can provide the portion of the data to a run-time risk criteria evaluation module that can determine, based upon the portion of the data, a run-time outage risk criteria for the cloud gateway. A baseline risk criteria evaluation module can obtain historical data from the database. The baseline risk criteria evaluation module can determine, based upon the data, a baseline outage risk criteria for the cloud gateway. The run-time risk criteria evaluation module can determine whether the run-time outage risk criteria meets or exceeds an outage risk threshold.

In one aspect, a computerized method includes the step of providing process monitor in a Gateway. The method includes the step of, with the process monitor, launching a Gateway Daemon (GWD). The GWD runs a GWD process that implements a Network Address Translation (NAT) process. The NAT process includes receiving a set of data packets from one or more Edge devices and forwarding the set of data packets to a public Internet. The method includes the step of receiving another set of data packets from the public Internet and forwarding the other set of data packets to the one or more Edge devices. The method includes the step of launching a Network Address Translation daemon (NATD). The method includes the step of detecting that the GWD process is interrupted; moving the NAT process to the NATD.

Techniques are described that detect areas with insufficient radio frequency (RF) coverage in a wireless network. A network management system (NMS) determines one or more service level expectation (SLE) metrics for each client device in a wireless network. The SLE metrics are aggregated to each access point (AP) in the wireless network, and each AP is assigned an AP score based on the aggregated SLE metrics. To identify potential coverage holes, the NMS groups APs having poor AP scores. If a root cause of the poor AP scores cannot be automatically resolved and if the poor AP scores persist for a predetermined period of time, the group of APs is determined to represent a true coverage hole. The NMS may generate a notification regarding recommended corrective actions to the customer and/or IT personnel.

Methods and systems disclosed herein relate generally to systems and methods for configuring an agent application provisioned within a private information technology infrastructure. The agent application provisioned in a private information technology infrastructure receives a payload comprising a set of parameters that include information that identifies that an update of the agent application is available. The agent application parses the set of parameters to determine that the update of the agent application is available. The agent application receives an updated component for modifying one or more functions of the agent application. The agent application deactivates a component of the agent application that corresponds to the updated component. The agent application replaces the deactivated component of the agent application with the updated component, in which one or more other components of the agent application continue their respective operations.

A method comprising discovering workload attributes and identify dependencies, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)'s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP's cloud services including cloud workload cost.