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OpenFlow rule placement in carrier backhaul networks for multicast applications

  • Amado, Rafael George
Publication Date
Apr 04, 2022
Espace ÉTS
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Today, mobile consumers increasingly use multicast applications (e.g., online gaming, Virtual/ Augmented Reality (VR/AR), social media) to stream video through carrier networks. Thanks to its flexibility, OpenFlow-enabled Software-defined networking (SDN) allows the enforcement of high-level policies and support end-to-end network-slicing, which are substantial for these applications’ requirements. A higher abstraction level hides the complexity of the network devices and exposes a simple interface to operators. However, this flexibility brings the complex task of allocating the low-level rules in the actual network, which requires handling constraints such as limited available memory on the switches and the capacity of the links. Due to legacy switch models, prior work focuses only on Flow Table entries that cannot efficiently support multicast traffic from one device (or server) to many devices across the network. Our approach is to leverage the use of Group Tables, which is recently introduced in the OpenFlow 1.1 specification, to support multicast flows and save switch memory. Traffic to multiple destinations can be aggregated to match a single Flow Table entry per switch, saving significant link resources. The controller has the challenging task of calculating where to efficiently install Flow and Group Tables entries (rules) in the network. In this thesis, we optimize rule placement in resource-constrained Openflow networks for both unicast and multicast flows. We formulate our model as an Integer Nonlinear Programming (INLP) problem, considering the network’s limitations, such as the memory capacity of both Flow and Group Tables and the available link bandwidth. The objective is to maximize the traffic delivered to the destinations under resource constraints. To solve this model, we use a solver (Gurobi) to obtain the optimal allocation and propose two algorithms to calculate the rule allocation in polynomial time: a greedy approach named OpenFlow Multicast Allocation Algorithm (OFMAA) and an improved version based on Steiner-tree (ST-OFMAA). Our experimental results on three different topologies show that Group Table is a key factor in reducing memory usage across the network. Our solution can support a higher number of flows than the solutions proposed by prior work, which do not consider Group Tables, by reducing both the link usage by up to 30% and the number of flow entries needed to deliver the traffic to destinations by 22%.

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