A computer network optimization methodology is disclosed. In a computer-implemented method, components of a computing environment are automatically monitored, and have a feature selection analysis performed thereon. Provided the feature selection analysis determines that features of the components are in frequent communication and generating network latency. Provided the feature selection analysis determines that features of the components are not well defined, a similarity analysis of the features is performed. Results of the feature selection methodology are generated, and the components involved in the network traffic latency are reassigned to migrate the latency.

A computer system is provided. The computer system includes a memory and a processor. The processor is configured to scan user interface (UI) data representative of a plurality of UI controls; detect a portion of the UI data associated with private information, the portion corresponding to a UI control of the plurality of UI controls; record first session data comprising an obfuscated version of the UI control and unobfuscated versions of other UI controls of the plurality of UI controls; record second session data comprising an unobfuscated version of the UI control; encrypt the second session data to generate encrypted session data; and store the encrypted session data in association with the first session data.

A method of deploying a network service (NS) across multiple data centers includes identifying virtual network functions (VNFs) associated with the NS in response to a request for or relating to the NS, generating commands to deploy VNFs based on VNF descriptors, and issuing the commands to the data centers to deploy VNFs. The data centers each have a cloud management server in which cloud computing management software is run to provision virtual infrastructure resources thereof for a plurality of tenants. The cloud computing management software of a first data center is different from the cloud computing management software of a second data center, and the commands issued to the first and second data centers are each a generic command that is not in a command format of the cloud computing management software of either the first data center or the second data center.

A data complementing method implemented by a computer, the method includes: calculating degree of correlation of a data item corresponding to a missing data value with another data item, in a case where the missing data value exists in a plurality of data records including data values corresponding to a plurality of data items, respectively; performing complementation of the missing data value by a recursive method based on a data item value of the other data item, in a case where the degree of correlation is larger than a predetermined correlation threshold; and performing complementation of the missing data value by a statistical method based on a data value other than the missing data value of a data item corresponding to the missing data value, in a case where the degree of correlation is not larger than the correlation threshold.

A virtual network system for a computer network is provided. The system includes a first host executing a virtual network function manager. The system also includes a second host executing a management virtual machine. The management virtual machine is in communication with the virtual network function manager and with one or more virtual network function component instantiations. The management virtual machine is programmed to route messages between the one or more virtual network function component instantiations and the virtual network function manager.

A method of deploying a network service across a plurality of data centers, includes the steps of: in response to a request for or relating to a network service, identifying virtual network functions associated with the network service and determining network connectivity requirements of the virtual network functions, issuing commands to provision a first virtual link between at least two of the data centers in which the virtual network functions are to be deployed, and issuing commands to provision a second virtual link to one of the data centers in which the virtual network functions are to be deployed.

In an approach for optimally grouping containers, a processor passively monitors a set of parameters for a set of containers within a network. A processor records the set of parameters for each container of the set of containers. A processor deploys a k-nearest neighbor neural network (KNN) to determine a first set of groupings of the set of containers based on the set of parameters. A processor simulates the network having grouped containers based on the first set of groupings of the set of containers output by the KNN. A processor simulates an action on the set of containers. A processor updates a reward function based on the action. A processor determines whether a maximum value of the reward function is reached.

Methods and apparatus, including computer program products, are provided for supporting network functions. In some example embodiments, there may be provided a method that includes selecting, by a network node, a network function instance and/or a group of network function instances, the selecting based on information indicating membership of the network function instance in the group of network functions instances and the information including session information for a request associated with a session of a user equipment, the session information indicating whether the network function instance and/or the group of network function instances is assigned to the user equipment; and forwarding, by the network node, the request to the selected network function instance and/or the selected group. Related systems, methods, and articles of manufacture are also described.

A computer of a resource monitoring system is configured to execute an application for analyzing an operation of an industrial device. A use status acquisition module is configured to acquire a use status of a resource of the computer by the application. A display control module is configured to display the use status on a resource monitoring screen.

Provided is a server. The server comprises a communicator configured to receive or transmit data from or to an external network and a processor configured to receive, through the communicator, service data of an electronic device, device data of the electronic device, and weather data for the electronic device and to calculate emotion index data based on the service data, the device data, and the weather data. Accordingly, customer's emotion index data may be efficiently calculated.