Described embodiments provide for routing remote application data. A device can receive a request to access an application. The application can be provided by data centers and accessible via service providers. The device can select a data center from the plurality of data centers and a service provider based at least on a metric indicative of a connection between the data center and the service provider. The device can query a database including one or more connection metrics using the application identified in the request and a location of a router transmitting the request. The device can determine the location of the router based on an internet protocol (IP) address of a client communicably coupled to the router. The device can transmit a response to the request identifying the selected data center and the selected service provider.

Replicas are selected in a large distributed network, and the roles for these replicas are identified. In one example, a leader is selected from among candidate computing dusters. To make this selection, an activity monitor predicts or monitors the workload of one or more clients. Different activities of the workload are given corresponding weights. The delay in performing requested activities, modified by these weights is found, and the candidate leader with the lowest weighted delay is selected as the leader.

Replicas are selected in a large distributed network, and the roles for these replicas are identified. In one example, a leader is selected from among candidate computing dusters. To make this selection, an activity monitor predicts or monitors the workload of one or more clients. Different activities of the workload are given corresponding weights. The delay in performing requested activities, modified by these weights is found, and the candidate leader with the lowest weighted delay is selected as the leader.

Systems and methods for establishing a connection with an edge application server are provided. A user equipment (UE) in a wireless communication network establishes a connection with an edge application server to offload the data processing of an application executing on the UE to the edge application server. The UE communicates key performance indicators (KPIs) associated with the application to the edge data network. The KPIs indicate the resources that application uses to process the data. In response, the UE receives edge application server parameters from multiple servers in the edge data network that meet or exceed the KPIs. The parameters include compute, graphical compute, memory and storage parameters with various levels of specificity. The UE selects one of the edge application servers to process the data on behalf of the application based on the parameters.

Embodiments of the present disclosure provide a method, system and device for content delivery network (CDN) scheduling, and a storage medium. The method includes: acquiring CDN data in real time from a CDN node device to generate a CDN index system; acquiring metropolitan area network, MAN, data in real time from a MAN to generate a MAN index system; generating a CDN node load intelligent image based on the CDN index system, and generating an intra-region scheduling algorithm through artificial intelligence, AI, training and algorithm optimization; generating a CDN region load intelligent image based on the CDN index system and the MAN index system, and generating an inter-region scheduling algorithm through the AI training and the algorithm optimization; and determining a CDN scheduling policy according to the intra-region scheduling algorithm and the inter-region scheduling algorithm, and executing the CDN scheduling policy.

Embodiments of the present disclosure provide a method, system and device for content delivery network (CDN) scheduling, and a storage medium. The method includes: acquiring CDN data in real time from a CDN node device to generate a CDN index system; acquiring metropolitan area network, MAN, data in real time from a MAN to generate a MAN index system; generating a CDN node load intelligent image based on the CDN index system, and generating an intra-region scheduling algorithm through artificial intelligence, AI, training and algorithm optimization; generating a CDN region load intelligent image based on the CDN index system and the MAN index system, and generating an inter-region scheduling algorithm through the AI training and the algorithm optimization; and determining a CDN scheduling policy according to the intra-region scheduling algorithm and the inter-region scheduling algorithm, and executing the CDN scheduling policy.

Described embodiments provide for routing remote application data. A device can receive a request to access an application. The application can be provided by data centers and accessible via service providers. The device can select a data center from the plurality of data centers and a service provider based at least on a metric indicative of a connection between the data center and the service provider. The device can query a database including one or more connection metrics using the application identified in the request and a location of a router transmitting the request. The device can determine the location of the router based on an internet protocol (IP) address of a client communicably coupled to the router. The device can transmit a response to the request identifying the selected data center and the selected service provider.

Systems, computer program products, and methods are described herein for implementing hybrid cloud computation engines using infrastructure optimization. The present invention may be configured to identify, from a group of available infrastructures, an infrastructure to perform a computing request, where the group of available infrastructures includes an enterprise infrastructure and one or more cloud provider infrastructures. The present invention may be further configured to provide, to the identified infrastructure and via a blockchain distributed ledger, the computing request and input data for the computing request, where the computing request includes a request to generate, using a recurrent neural network, based on data in the blockchain distributed ledger, and based on the input data, output data. The present invention may be further configured to receive, from the identified infrastructure and via the blockchain distributed ledger, the output data.

Systems, computer program products, and methods are described herein for implementing hybrid cloud computation engines using infrastructure optimization. The present invention may be configured to identify, from a group of available infrastructures, an infrastructure to perform a computing request, where the group of available infrastructures includes an enterprise infrastructure and one or more cloud provider infrastructures. The present invention may be further configured to provide, to the identified infrastructure and via a blockchain distributed ledger, the computing request and input data for the computing request, where the computing request includes a request to generate, using a recurrent neural network, based on data in the blockchain distributed ledger, and based on the input data, output data. The present invention may be further configured to receive, from the identified infrastructure and via the blockchain distributed ledger, the output data.

Techniques of network virtualization of containers in cloud-based system are disclosed herein. In one embodiment, a method includes receiving a selection of a host in the computer system to instantiate a container in response to a request from a user. In response to the received selection, the method includes identifying parameters of network operations on the selected host to instantiate the requested container and assigning a network address to the container to be instantiated on the selected host in the computer system, the assigned network address is addressable from outside of the selected host without network name translation. The method can then include transmitting an instruction to the selected host to instantiate the requested container based on the assigned network address.