Keeping a connection continuity during the movement of a mobile node (MN) between access points without any suspension of provided services is one of the most pressing issues should be solved. Long handover processing causes interruptions in session connection, high rate of data loss, and long end-to-end delay time. Smart virtualization means the cooperation of different virtualization technologies with novel ideas. In this paper, we proposed a mobile network architecture compatible with cloud computing of 5G and beyond networks. We invented a new idea to create a tag to be used as an MN's identity, which consists of the standard E.164 numbering and MAC address. Based on the uniqueness of E.164 numbering and MAC which are processed together to generate the MN tag (T H ). T H is used to handle the packets inside the mobile networks. The software-defined networking (SDN) provides a capability of separating the control plane from the data plane. This decoupling is a suitable candidate to exploit it in our proposed system that uses the SDN and other virtualization technologies. The requirements of the 5G and beyond for future mobile communications encouraged us to think in a novel packet forwarding during the handover to keep real-time connection continuity for an MN. Our proposed system has been simulated and performed by MATLAB and Mininet platforms. The results showed that the packet loss rate decreased to 4% of the date that was lost during the handover delay time or packets re-direction mechanism. At the same time, the MN could receive 96.4% of the data that were lost during the handover process.INDEX TERMS 5G, control plane, communication networks, SDN, NFV.

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Recently, the increasing demand to transfer data through the Internet has pushed the Internet infrastructure to the final edge of the ability of these networks. This high demand causes a deficiency of rapid response to emergencies and disasters to control or reduce the devastating effects of these disasters. As one of the main cornerstones to address the data traffic forwarding issue, the Internet networks need to impose the highest priority on the special networks: Security, Health, and Emergency (SHE) data traffic. These networks work in closed and private domains to serve a group of users for specific tasks. Our novel proposed network flow priority management based on ML and SDN fulfills high control to give the required flow priority to SHE data traffic. The proposal relies on selected header bits from the traffic class field of a packet using the ML to prioritize traffic flows according to the precedence levels by governing the Differentiated Services Code Point (DSCP) bits in keeping with network administrator policies. The proposed network has been evaluated and performed utilizing the MATLAB platform and the Mininet simulator. The results of extensive testing show enhancement by applying our forcing priority algorithm obtained an efficient reduction in queuing delay and lost packets. The average waiting time in queue was reduced by around 61%, and the lost packets hit 0.005% when adopting the SDN-based ML network traffic priority management.

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational characteristics of traffic flow types; by considering only the position of the selected bits from the packet header. The proposal a learning approach based on deep packet inspection which integrates both feature extraction and classification phases into one system. The results show that the FDPHI works very well on the applications of feature learning. Also, it presents powerful adequate traffic classification results in terms of energy consumption (70% less power CPU utilization around 48% less), and processing time (310% for IPv4 and 595% for IPv6).

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