Scalable Steiner Tree for Multicast Communications in Software-Defined Networking

TL;DR

This paper introduces a new multicast tree structure for SDN called Branch-aware Steiner Tree (BST), addressing scalability issues by jointly minimizing edges and branch nodes, with an efficient approximation algorithm demonstrated through simulations.

Contribution

Proposes the BST model for multicast in SDN and develops the BAERA algorithm, which is scalable and bandwidth-efficient for large networks.

Findings

BAERA produces more bandwidth-efficient trees than traditional methods.

The proposed approach is computationally efficient for large-scale networks.

BST is NP-Hard and inapproximable within k, highlighting its complexity.

Abstract

Software-Defined Networking (SDN) enables flexible network resource allocations for traffic engineering, but at the same time the scalability problem becomes more serious since traffic is more difficult to be aggregated. Those crucial issues in SDN have been studied for unicast but have not been explored for multicast traffic, and addressing those issues for multicast is more challenging since the identities and the number of members in a multicast group can be arbitrary. In this paper, therefore, we propose a new multicast tree for SDN, named Branch-aware Steiner Tree (BST). The BST problem is difficult since it needs to jointly minimize the numbers of the edges and the branch nodes in a tree, and we prove that it is NP-Hard and inapproximable within $k$, which denotes the number of group members. We further design an approximation algorithm, called Branch Aware Edge Reduction Algorithm (BAERA), to solve the problem. Simulation results demonstrate that the trees obtained by BAERA are more bandwidth-efficient and scalable than the shortest-path trees and traditional Steiner trees. Most importantly, BAERA is computation-efficient to be deployed in SDN since it can generate a tree on massive networks in small time.