Mobileflow: Applying SDN to Mobility in Wireless Networks
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Wireless technology has become an increasingly popular way for network access. Wireless networks provide efficient, reliable service; supporting a broad range of emerging applications including multimedia streaming and video conferencing. Currently, there are two dominant technologies for providing wireless network access: cellular broadband networks and wireless local area networks (Wi-Fi). Cellular networks offer ubiquitous coverage, high reliability, and support mobility; yet such networks require expensive specialized equipment and expensive spectrum bands. In contrast, Wi-Fi networks utilize unlicensed frequency bands; relying on commodity equipment. As a result, Wi-Fi infrastructure operational costs are lower than cellular network costs. Wi-Fi networks however, have limited coverage, do not support mobility, and are less reliable than cellular networks. Recently, software-defined-networking architectures are gaining interest. The Software-Defined Networking (SDN) approach separates control (forwarding decisions) and data plane (packet processing). This approach provides an abstraction of a network switch and an interface for manipulating this abstraction with clear semantics. The SDN approach enables applications to control underlying network services without knowing the low-level details of specific network equipment. Thus, this approach allows network programming by modifying the behavior of the routers and switches to meet network application requirements. This thesis introduces a reference architecture that supports user mobility through integration of the SDN technology into Wi-Fi networks. This project then implements a mobility manager application on top of an SDN controller to handle clients’ handoff between access points. It proposes an algorithm for mobility prediction, allowing the network operator to minimize packet loss and delays during handoffs. Algorithm validation uses real data traces from the Texas A&M University network. Trace analysis was conducted to extract mobility patterns to build a prediction model which was implemented as an application in the SDN controller. The approach was tested by measuring packet loss that was decreased by approximately nine times. Collected mobility traces were used to analyze our prediction model performance, whose accuracy reached 65% and 95% when selecting five users with Last-in-First-out scheme with a high- and low-load access point, respectively. This research lays out groundwork for enhancing the functionality of WiFi networks, including mobility support, while maintaining their advantages in terms of lower cost, flexibility, and user of off-the-shelf components.
Al-Shaikhli, Raghdah (2014). Mobileflow: Applying SDN to Mobility in Wireless Networks. Master's thesis, Texas A & M University. Available electronically from