Dynamic Hierarchical Cache Management for Cloud RAN and Multi- Access Edge Computing in 5G Networks

Cloud Radio Access Networks (CRAN) and Multi-Access Edge Computing (MEC) are two of the many emerging technologies that are proposed for 5G mobile networks. CRAN provides scalability, flexibility, and better resource utilization to support the dramatic increase of Internet of Things (IoT) and mobile devices. MEC aims to provide low latency, high bandwidth and real- time access to radio networks. Cloud architecture is built on top of traditional Radio Access Networks (RAN) to bring the idea of CRAN and in MEC, cloud computing services are brought near users to improve the user’s experiences. A cache is added in both CRAN and MEC architectures to speed up the mobile network services. This research focuses on cache management of CRAN and MEC because there is a necessity to manage and utilize this limited cache resource efficiently. First, a new cache management algorithm, H-EXD-AHP (Hierarchical Exponential Decay and Analytical Hierarchy Process), is proposed to improve the existing EXD-AHP algorithm. Next, this paper designs three dynamic cache management algorithms and they are implemented on the proposed algorithm: H-EXD-AHP and an existing algorithm: H-PBPS (Hierarchical Probability Based Popularity Scoring). In these proposed designs, cache sizes of the different Service Level Agreement (SLA) users are adjusted dynamically to meet the guaranteed cache hit rate set for their corresponding SLA users. The minimum guarantee of cache hit rate is for our setting. Net neutrality, prioritized treatment will be in common practice. Finally, performance evaluation results show that these designs achieve the guaranteed cache hit rate for differentiated users according to their SLA

Exploring intelligent service migration in a highly mobile network

Mobile services allow services to be migrated or replicated closer to users as they move around. This is now regarded as a viable mechanism to provide good Quality of Service to users in highly mobile environments such as vehicular networks. The vehicular environment is rapidly becoming a significant part of the internet and this presents various challenges that must be addressed; this is due to continuous handovers as mobile devices change their point of attachment to these networks resulting in a loss of service. Therefore, this explains the need to build a framework for intelligent service migration. This thesis addresses these issues. It starts by discussing the requirements for intelligent service migration. Then it investigates a low latency Quality of Service Aware Framework as well as an experimental transport protocol that would be favoured by vehicular networks. Furthermore, two analytical models are developed using the Zero-Server Markov Chain technique which is a way of analysing scenarios when the server is not continuously available to serve. Using the Zero-Server Markov Chain, the first analytical model looks at lost service due to continuous handovers and the communication dynamics of vehicular networks, while the second model analyses how service migration affects service delivery in these networks. Formulas are developed to yield the average number of packets in the system, the response time, the probability of blocking and a new parameter called the probability of lost service. These formulas are then applied to the Middlesex VANET Testbed to look at reactive and proactive service migration. These techniques are then incorporated into a new Service Management Framework to provide sustainable Quality of Service and Quality of Experience to mobile users in vehicular networks. This thesis also shows that this new approach is better than current approaches as it addresses key issues in intelligent service migration in such environments, and hence can play a significant part in the development of Intelligent Transport Systems for Smart Cities

A Case Study of Edge Computing Implementations: Multi-access Edge Computing, Fog Computing and Cloudlet

With the explosive growth of intelligent and mobile devices, the current centralized cloud computing paradigm is encountering difficult challenges. Since the primary requirements have shifted towards implementing real-time response and supporting context awareness and mobility, there is an urgent need to bring resources and functions of centralized clouds to the edge of networks, which has led to the emergence of the edge computing paradigm. Edge computing increases the responsibilities of network edges by hosting computation and services, therefore enhancing performances and improving quality of experience (QoE). Fog computing, multi-access edge computing (MEC), and cloudlet are three typical and promising implementations of edge computing. Fog computing aims to build a system that enables cloud-to-thing service connectivity and works in concert with clouds, MEC is seen as a key technology of the fifth generation (5G) system, and Cloudlet is a micro-data center deployed in close proximity. In terms of deployment scenarios, Fog computing focuses on the Internet of Things (IoT), MEC mainly provides mobile RAN application solutions for 5G systems, and cloudlet offloads computing power at the network edge. In this paper, we present a comprehensive case study on these three edge computing implementations, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge. We discuss the requirements, benefits, and mechanisms of typical co-deployment cases for each paradigm and identify challenges and future directions in edge computing

On the security of software-defined next-generation cellular networks

In the recent years, mobile cellular networks are ndergoing fundamental changes and many established concepts are being revisited. Future 5G network architectures will be designed to employ a wide range of new and emerging technologies such as Software Defined Networking (SDN) and Network Functions Virtualization (NFV). These create new virtual network elements each affecting the logic of the network management and operation, enabling the creation of new generation services with substantially higher data rates and lower delays. However, new security challenges and threats are also introduced. Current Long-Term Evolution (LTE) networks are not able to accommodate these new trends in a secure and reliable way. At the same time, novel 5G systems have proffered invaluable opportunities of developing novel solutions for attack prevention, management, and recovery. In this paper, first we discuss the main security threats and possible attack vectors in cellular networks. Second, driven by the emerging next-generation cellular networks, we discuss the architectural and functional requirements to enable appropriate levels of security