Systems and methods are disclosed for managing workload among server clusters is disclosed. According to certain embodiments, the system may include a memory storing instructions and a processor. The processor may be configured to execute the instructions to determine historical behaviors of the server clusters in processing a workload. The processor may also be configured to execute the instructions to construct cost models for the server clusters based at least in part on the historical behaviors. The cost model is configured to predict a processor utilization demand of a workload. The processor may further be configured to execute the instructions to receive a workload and determine efficiencies of processing the workload by the server clusters based at least in part on at least one of the cost models or an execution plan of the workload.

A device receives, from a cloud computing environment, cloud instance information associated with cloud instances in the cloud computing environment, and processes the cloud instance information, with a machine learning model, to determine containers for one or more of the cloud instances and whether cloud instances should be removed from the cloud computing environment. The device causes a first subset of the cloud instances to be removed from the cloud computing environment, based on determining which of the cloud instances should be removed, and causes the containers to be created for a second subset of the cloud instances based on determining the containers. The device receives, from the cloud computing environment, cloud container information associated with the containers created in the cloud computing environment, and causes one or more of the containers to be scaled based on the cloud container information.

The disclosure relates to a method and a state controller running in a Kubernetes system. The state controller being operative to assign labels to pods, the labels indicating services to which the pods are assigned and high-availability states of the pods; detect a failed pod having a label indicating a high-availability state of not ready; and reassign the label indicating the high-availability state of the failed pod to a healthy pod, thereby changing endpoints of services provided and service flows from the failed pod to the healthy pod.

State of the art networking solutions are tightly coupled and proprietary in nature due to multiple vendors in the networking domain. Embodiments of the present disclosure provide a method and system for management and orchestration of heterogeneous network environment using dynamic, robust and network aware microservices. The method enables a platform for automatically and dynamically identifying appropriate group of microservices in accordance with network type and service type specified by the user, thus providing a solution that generates network aware microservices for each network in the heterogeneous network landscape. Furthermore, the system manages the identified microservices for each of the network by managing the life cycle of these microservices. The right life cycle management and co-ordination of the microservices for the network is in-line with desired goals/business logic, in a reliable and scalable manner, in heterogeneous network environments.

Systems and methods for detecting, e.g., that a Network Function (NF) service consumer in a core network of a cellular communications system has restarted are disclosed. In some embodiments, a method of operation of a NF service consumer in a core network of a cellular communications system comprises sending, to a NF service producer, a message comprising information related to a unit of the NF service consumer.

Disclosed is a network management system that provides an interface to enable diagnostics and troubleshoot of a remotely managed multi-site network. Some embodiments provide a natural language interface, while other embodiments provide a chatbot type interface that communicates with a technician via traditional text information on a display screen. The diagnostic and troubleshooting capabilities search a central data store that receives device property information from each site of the multi-site network. Based on devices or users that match portions of the entity, queries to the data store are initiated to obtain additional data on the devices. A response to the query is then provided based on the properties of the devices.

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.

Methods, apparatuses, or computer program products provide for generating a predictive causal probability score data object. A complex federated service network may be monitored to identify a service incident data object associated with a service incident. A predictive causal machine learning model may generate a predictive causal probability score data object based at least in part on a service incident time associated with the service incident data object. The predictive causal probability score data object may be output.

Described are computer-based methods and apparatuses, including computer program products, for leveraging available compute resources from a plurality of computing devices using containers. Each computing device from the plurality of computing devices executes a container that virtualizes a portion of an operating system executing on the computing device such that the container can execute one or more secondary applications in isolation from any incumbent applications being executed by the operating system on the computing device that have priority over the one or more secondary applications.

A computerized-method for providing an indication as to an availability of a communication-channel type that is used during an interaction with a customer, via a web app is provided herein. The computerized-method includes operating a communication-channel-type availability module that includes: receiving collected data of an interaction of an agent during an interaction with a customer, via a communication-channel-type from a communication manager module; operating one or more analyses on the collected data to yield a corresponding score of each analysis of the one or more analyses; calculating an availability-score of the communication-channel-type during the interaction, based on the score for each analysis of the one or more analyses; storing the calculated availability-score, in a data storage, as an availability-score of the agent, after the interaction ends; and displaying the availability-score as an indication to an availability of a communication-channel type, on a display unit, associated with the computerized system.