MIST: An Efficient Approach for Software-Defined Multicast in Wireless Mesh Networks
Rupei Xu, Yuming Jiang, Jason P. Jue
TL;DR
This work addresses interference-aware software-defined multicast in wireless mesh networks by formulating Minimum Interference Steiner Tree (MIST), a bicriteria optimization balancing total latency and interference. It develops a rigorous algorithmic framework leveraging monotone submodularity and vicinal preorder properties to cast MIST as a submodular minimization under Steiner constraints, culminating in the Two-Stage Submodular Relaxation (TSSR) approach. The authors prove submodularity properties, derive structural insights, and provide approximation guarantees, complemented by simulations showing identical tree lengths to baseline Steiner approaches with notably reduced interference. The results offer a principled path to scalable, interference-conscious multicast in SDN-enabled WMNs with NFV-placed functions, suitable for 5G/B5G contexts and beyond. Future work includes relaxing the single-request assumption and exploring geographic proximity rules and multi-orchestrations.
Abstract
Multicasting is a vital information dissemination technique in Software-Defined Networking (SDN). With SDN, a multicast service can incorporate network functions implemented at different nodes, which is referred to as software-defined multicast. Emerging ubiquitous wireless networks for 5G and Beyond (B5G) inherently support multicast. However, the broadcast nature of wireless channels, especially in dense deployments, leads to neighborhood interference as a primary system degradation factor, which introduces a new challenge for software-defined multicast in wireless mesh networks. To tackle this, this paper introduces an innovative approach, based on the idea of minimizing both the total length cost of the multicast tree and the interference at the same time. Accordingly, a novel bicriteria optimization problem is formulated--\emph{Minimum Interference Steiner Tree (MIST)}, which is the edge-weighted variant of the vertex-weighted secluded Steiner tree problem \cite{chechik2013secluded}. To solve the bicriteria problem, instead of resorting to heuristics, this paper employs an innovative approach that is an approximate algorithm for MIST but with guaranteed performance. Specifically, the approach exploits the monotone submodularity property of the interference metric and identifies Pareto optimal solutions for MIST, then converts the problem into the submodular minimization under Steiner tree constraints, and designs a two-stage relaxation algorithm. Simulation results demonstrate and validate the performance of the proposed algorithm.