In-network softwarization, Network Slicing provides scalability and flexibility through various services such as Quality of Service (QoS) and Quality of Experience (QoE) to cover the network demands. For the QoS, a set of policies must be considered in real-time, accompanied by a group of functions and services to guarantee the end-user needs based on network demand. On the other hand, for the QoE, the service's performance needs to be improved to bring an efficient service to cover the demands of the end-user. The 3G Partnership Project (3GPP) defined the slice as a component of resources used to process a set of packets. These resources need to be flexible, which means the resources can be scaled up or down based on the demand. This survey discusses softwarization and virtualization techniques, considering how to implement the slices for future networks. Specifically, we discuss current advances concerning the functionality and architecture of the 5G network. Therefore, the paper critically evaluates recent research and systems related to mobility management as a service in real-time inter/intra slice control by considering the strengths and limitations of these contributions to identify the research gaps and possible research directions for emerging research and development opportunities. Moreover, we extend our review by considering the slice types and their numbers based on the 3GPP Technical Specification (3GPP TS). The study presented in this paper identifies open issues and research directions that reveal that mobility management at a service level with inter/intra slice management techniques has strong potential in future networks and requires further investigation from the research community to exploit its benefits fully.

End-to-End (E2E) virtual networks represent a key technology in future cellular networks. Generally, the E2E connection means each slice has an independent part of the RAN, User Plane Function (UPF) and the 5G Core. Within each slice, a subscriber may have one or more Quality of Service (QoS) flows. These flows only exist within the slices. According to the 3G Partnership Project (3GPP) Technical Specification (TS), it could be at most eight Single Network Slice Selection Assistance Informations (S-NSSAIs) in the Allowed list. Requested NSSAIs sent in signalling messages; registration request, accept and respectively; between the user and the network. These messages allow the network to select the serving Access and Mobility Management Function (AMF), network slices and Network Slice Instances (NSIs)for the user. The research idea is to improve the Quality of Service (QoS) and the Quality of Experience (QoE) for the user when connecting to different slices on the 5G systems. The slice performance for one slice should not be affected by other slice traffic. This paper evaluates the performance of E2E 5G slicing in terms of throughput, jitter, reliability, transmission rate and mobility under different circumstances. In the proposed system, the performance of the slice is checked when the user connects to eight slices or more at the same time. In addition, we propose a slice termination and connection algorithm that allows the user to register new slices. Moreover, the algorithm allows users who are already registered to be released after using slices, enabling more effective use of the network resources.

Due to the exponential increase in network traffic in the data centers, thousands of servers interconnected with high bandwidth switches are required. Field Programmable Gate Arrays (FPGAs) with Cloud ecosystem offer high performance in efficiency and energy, making them active resources, easy to program and reconfigure. This paper looks at FPGAs as reconfigurable accelerators for the cloud computing presents the main hardware accelerators that have been presented in various widely used cloud computing applications such as: MapReduce, Spark, Memcached, Databases.

The 3G Partnership Project (3GPP) defined network slicing as a set of resources that could be scaled up and down to cover users' requirements. Machine learning and network slicing will be used together to manage and optimize resources efficiently. Sharing resources across multiple operators, such as towers, spectrum and infrastructure, can reduce the cost of 5G resources. In the proposed prototype, the end-user is connected to more than eight inter and intra-slices according to the demands. A set of slices is implemented over the 5G networks to provide an efficient service to the end-user using softwarization and virtualization technologies. Traffic is generated over multiple scenarios then End-to-End slicing traffic was analyzed after generating realtime traffic over the 5G networks. Also, all the features extracted from the traffic based on the flow behaviours and a set of elements selected from the datasets according to machine learning behaviours. Multiple machine learning algorithms are applied to our datasets using MATLAB classification application. After that, the best model is chosen to train and predict the slices using less CPU and training time to reduce the computational power in future networks and build a sustainable environment. Furthermore, the regression application predicts the slice type on the third dataset with the minimum squared error.