Abstract: A system and method for dynamically creating distributed, self-adjusting and optimizing core network with machine learning is disclosed. The method includes receiving a request to access one or more services and establishing a secure real time communication session with one or more client devices and a set of service layers based on the received request. The method further includes determining one or more service parameters based on the received request and sending one or more handshake messages to each of the set of service layers. Further, the method includes determining one or more environmental parameters and determining best possible service layer capable of processing the received request by using a trained service based ML model. The method includes processing the request at the determined best possible service layer and terminating or transferring the secure real time communication session after the request is processed.

Abstract: A system and method for autonomous data and signalling traffic management in a distributed infrastructure is disclosed. The method includes a distributed multi-cloud computing system with machine learning based intelligence across heterogenous computing platforms hosting mobile network functions, capable of leveraging AI based distribution across all the resources to creates autonomous network operations and intelligently work around any impairments. The method includes determining one or more service nodes by using a trained traffic management based ML model and establishing one or more cloud mesh links between the one or more service nodes at multiple levels of hierarchy based on the system, environment and network parameters and the current network demand. Further, the method includes processing the request by providing access of the one or more services hosted on the one or more external devices to the one or more electronic devices via the one or more cloud mesh links.

Abstract: The present disclosure describes solutions for dynamic network slicing including provisions to create, modify, and/or delete network slices in a de-centralized communication network including a plurality of central/regional/edge/far-edge locations across hybrid and multi-cloud environment referred to as edge server or edge location for providing service to the users. Network slicing enables multiple isolated and independent virtual (logical) networks to exist together. A plurality of virtual networks, i.e., slices, may be created using resources of the same physical network infrastructure.

Abstract: The present disclosure describes an artificial intelligence (AI)/machine learning (ML) based distributed, hybrid, and multi-cloud software fabric-based system that unifies the communication infrastructure across hybrid and multi clouds. This mobile connectivity software fabric allows operators to modernize their networks to bring significant operational savings while rolling out new mobile services. This fabric can enable small independent networks and allow them to seamlessly connect with public networks, and it can enable network of networks while keeping the underlying compute and heterogeneity unified.

Abstract: A system and method for dynamically creating distributed, self-adjusting and optimizing core network with machine learning is disclosed. The method includes receiving a request to access one or more services and establishing a secure real time communication session with one or more client devices and a set of service layers based on the received request. The method further includes determining one or more service parameters based on the received request and sending one or more handshake messages to each of the set of service layers. Further, the method includes determining one or more environmental parameters and determining best possible service layer capable of processing the received request by using a trained service based ML model. The method includes processing the request at the determined best possible service layer and terminating or transferring the secure real time communication session after the request is processed.