About the Project
Mobile operators are spending substantial capital on improving their network infrastructure in order to meet this fast-growing data traffic demand. A particularly important question facing mobile operators in this context is how to allocate their network resource between fixed and cloud network infrastructures.
Fixed network infrastructure (e.g. hardware-based firewalls) is typically associated with good performance (high throughput, short response time, etc.) but suffers from the fact that mobile data traffic usually has peaks during the day. Since hardware-based legacy equipment is designed for a single purpose only it typically lacks flexibility, which implies low network utilisation at other times (e.g. during the night). In consequence, to guarantee the quality of network users’ experience, mobile operators usually install spare capacity to tackle peak traffic when deploying fixed network equipment.
Cloud network infrastructure enables mobile operators to manage their network equipment in a more fine-grained and efficient way. Thanks to the development of cloud computing technology, network function virtualization (NFV) has become one of the most important features to be introduced into 5G mobile networks. NFV enables mobile operators to virtualise hardware and software resources as virtual network function (VNF) instances. VNF instances can be turned on/off to adjust computing and network capabilities to save energy and resources. Although the idea is just being applied to cellular networks, it has already been used in the community of cloud computing. However, on the negative side, when network equipment is virtualised as software solutions, i.e., VNF instances, they usually have lower performance compared with fixed network equipment that are designed and optimized for their particular purpose.
This PhD project will tackle the research questions of how to best allocate a limited budget between different types of (e.g., fixed, cloud, private cloud) network infrastructure to balance cost-performance tradeoff.
Project Start Date: Oct 2019
Mode of Study: Full-time
Acceptable First Degree: Computer Science, Information Communication Technology, Mathematics, Engineering or other numerate discipline.
Standard minimum entry requirement is 2:1, but it is desirable that applicants will have a 1st Class Honours Degree in a related subject and hopefully a Masters with experience of communications networking protocols, mathematical modelling and simulation.
Early application is encouraged.
Funding Notes
This PhD project is in a Faculty of Science competition for funded studentships. These studentships are funded for 3 years and comprise home/EU fees, an annual stipend of £14,777 and £1,000 per annum to support research training. Overseas applicants may apply but they are required to fund the difference between home/EU and overseas tuition fees (which for 2018-19 are detailed on the University’s fees pages at https://portal.uea.ac.uk/planningoffice/tuition-fees . Please note tuition fees are subject to an annual increase).
References
[1] P. Rost et al., “Network Slicing to Enable Scalability and Flexibility in 5G Mobile Networks,” IEEE Commun. Mag., vol. 55, no. 5, pp. 72–79, 2017.
[2] X. Li et al., “Network Slicing for 5G: Challenges and Opportunities,” IEEE Internet Comput., vol. 21, no. 5, pp. 20–27, 2017.
[3] X. Foukas, G. Patounas, A. Elmokashfi, and M. K. Marina, “Network Slicing in 5G: Survey and Challenges,” IEEE Commun. Mag., vol. 55, no. 5, pp. 94–100, 2017.
[4] T. Phung-Duc, Y. Ren, et al., “Design and analysis of Deadline and Budget Constrained Autoscaling (DBCA) algorithm for 5G mobile networks," in Proc. 8th IEEE Int'l Conf. Cloud Computing Technology and Science (CloudCom '16), Luxembourg, Dec., 2016.
[5] Y. Ren, et al., "Dynamic Auto Scaling Algorithm (DASA) for 5G mobile networks," in Proc. IEEE Global Telecommunications Conf. (GLOBECOM '16), Washington, DC, USA, Dec. 2016.
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