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Towards the development of an optimal SDN controller placement framework to expedite SDN deployment in emerging markets
The challenge of poor broadband penetration in emerging markets is generally attributed to the high cost of deployment and operations for broadband infrastructure. Operators are more comfortable to rollout infrastructure in urban areas than in rural (i.e. remote, sparsely populated and low income) a...
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| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2020
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| Summary: | The challenge of poor broadband penetration in emerging markets is generally attributed to the high cost of deployment and operations for broadband infrastructure. Operators are more comfortable to rollout infrastructure in urban areas than in rural (i.e. remote, sparsely populated and low income) areas, due to the attractive profit margins they present. The repercussion of this is a wide “digital divide” between urban and rural areas, resulting in social and economic exclusion. The exclusion of rural areas stifles economic growth. In order to bridge this divide, a more cost effective telecommunication infrastructure is indispensable. This means adopting an architecture that minimizes both network deployment costs (CapEx) and operational costs (OpEx), while maintaining a high service quality level and ensuring business agility. There is a general consensus that a large portion of OpEx comes from the costs associated with the configuration and management of the telecommunication infrastructure. Software Defined Networking (SDN) has emerged as a promising solution to revolutionize network deployment, operations and economic growth. This paradigm aims to address management and configuration complexities in legacy networks so as to reduce the total cost associated with deploying and running telecommunication infrastructures. Conventionally, network control and data planes are tightly coupled and deployed within the same proprietary network device. SDN presents a shift in paradigm by decoupling the control plane from the data plane, abstracting lower level functionality of underlying hardware and enabling network programmability through a centralized controller. As the “brain” of the network, the controller must be able to process and respond to requests from the data plane promptly and proficiently. In order to optimize a controller’s operational efficiency, factors such as the number of controllers deployed, type of controller and controller placement are considered. During the network planning stage of an SDN deployment, the important questions that must be answered are: given a wide area network (WAN) topology, how many controllers are needed and where should they be placed to optimize SDN performance? Henceforth, this is referred to as the controller placement problem. This problem constitutes competing objectives such as load balancing, latency, reliability and CapEx, thus no single best placement solution is available. This study aims to address the controller placement problem by leveraging machine learning algorithms. Moreover, this study carries out a comparative performance evaluation of the most popular SDN controllers namely, Ryu, Floodlight, ONOS and OpenDayLight. The results from the performance evaluation are used to study the controller placement problem on an emulation orchestration platform. In order to contextualize the problem to emerging markets and maintain realism, a local national research and education wide area network called SANReN is used to test the proposed algorithms. This study can potentially be used by network operators as a guideline to start integrating SDN or plan a new SDN deployment, by helping them make quick automatic decisions regarding optimal controller placement. |
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