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The proliferation of smart mobile devices and user applications has continued to contribute to the tremendous volume of data traffic in cellular networks. Moreover, with the feature of heterogeneous connectivity interfaces of these smart devices, it becomes more complex for managing the traffic volume in the context of mobility. To surmount this challenge, service and resource providers are looking for alternative mechanisms that can successfully facilitate managing network resources and mobility in a more dynamic, predictive and distributed manner. New concepts of network architectures such as Software-Defined Network (SDN) and Network Function Virtualization (NFV) have paved the way to move from static to flexible networks. They make networks more flexible (i.e., network providers capable of on-demand provisioning), easily customizable and cost effective. In this regard, network slicing is emerging as a new technology built on the concepts of SDN and NFV. It splits a network infrastructure into isolated virtual networks and allows them to manage network resources based on their requirements and characteristics. Most of the existing solutions for network slicing are facing challenges in terms of resource and mobility management. Regarding resource management, it creates challenges in terms of provisioning network throughput, end-to-end delay, and fairness resources allocation for each slice, whereas, in the case of mobility management, due to the rapid change of user mobility the network slice operator would like to hold the mobility controlling over its clients across different access networks, rather than the network operator, to ensure better services and user experience. In this thesis, we propose two novel architectural solutions to solve the challenges identified above. The first proposed solution introduces a Network Slicing Resource Management (NSRM) mechanism that assigns the required resources for each slice, taking into consideration resource isolation between different slices. The second proposed v solution provides a Mobility Management architecture-based Network Slicing (MMNS) where each slice manages its users across heterogeneous radio access technologies such as WiFi, LTE and 5G networks. In MMNS architecture, each slice has different mobility demands (e.g,. latency, speed and interference) and these demands are governed by a network slice configuration and service characteristics. In addition, NSRM ensures isolating, customizing and fair sharing of distributed bandwidths between various network slices and users belonging to the same slice depending on different requirements of each one. Whereas, MMNS is a logical platform that unifies different Radio Access Technologies (RATs) and allows all slices to share them in order to satisfy different slice mobility demands. We considered two software simulations, namely OPNET Modeler and OMNET++, to validate the performance evaluation of the thesis contributions. The simulation results for both proposed architectures show that, in case of NSRM, the resource blocking is approximately 35% less compared to the legacy LTE network, which it allows to accommodate more users. The NSRM also successfully maintains the isolation for both the inter and intra network slices. Moreover, the results show that the NSRM is able to run different scheduling mechanisms where each network slice guarantee perform its own scheduling mechanism and simultaneously with other slices. Regarding the MMNS, the results show the advantages of the proposed architecture that are the reduction of the tunnelling overhead and the minimization of the handover latency. The MMNS results show the packets delivery cost is optimal by reducing the number of hops that the packets transit between a source node and destination. Additionally, seamless session continues of a user IP-flow between different access networks interfaces has been successfully achieved.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: 5G network; LTE network; WiFi networks; Software Define Networking (SDN); Network Function Virtualization (NFV); Mobility management; Resource Allocation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Date Deposited: 01 Nov 2018 09:43
Last Modified: 03 Jan 2023 15:21
DOI or ID number: 10.24377/researchonline.ljmu.ac.uk.00009587
Supervisors: Lee, GM, Pereira, R and Shamsa, TB
URI: https://researchonline.ljmu.ac.uk/id/eprint/9587
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