Approximation and Online Algorithms for NFV-Enabled Multicasting in SDNs

• Published in: Proceedings of the International Conference on Distributed Computing Systems pp. 625 - 634
• Main Authors: Zichuan Xu, Weifa Liang, Meitian Huang, Mike Jia, Song Guo, Galis, Alex
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2017
• Subjects: Approximation algorithms; approximation and online algorithms; Bandwidth; Heuristic algorithms; Middleboxes; Multicast communication; multicasting; Network function virtualization; NFV-enabled multicasting; Optical switches; Servers; service chains; software-defined networks; virtualized network functions
• DOI: 10.1109/ICDCS.2017.43
• ISSN: 1063-6927

Multicasting is a fundamental functionality of networks for many applications including online conferencing, event monitoring, video streaming, and system monitoring in data centers. To ensure multicasting reliable, secure and scalable, a service chain consisting of network functions (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) usually is associated with each multicast request. Such a multicast request is referred to as an NFV-enabled multicast request. In this paper we study NFV-enabled multicasting in a Software-Defined Network (SDN) with the aims to minimize the implementation cost of each NFV-enabled multicast request or maximize the network throughput for a sequence of NFV-enabled requests, subject to network resource capacity constraints. We first formulate novel NFV-enabled multicasting and online NFV-enabled multicasting problems. We then devise the very first approximation algorithm with an approximation ratio of 2K for the NFV-enabled multicasting problem if the number of servers for implementing the network functions of each request is no more than a constant K (1). We also study dynamic admissions of NFV-enabled multicast requests without the knowledge of future request arrivals with the objective to maximize the network throughput, for which we propose an online algorithm with a competitive ratio of O(log n) when K = 1, where n is the number of nodes in the network. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms outperform other existing heuristics.