Forwarding Rule Multiplexing for Scalable SDN-Based Internet of Things

Abstract

Internet of Things (IoT) provides a vast number of devices with heterogeneous characteristics connected to the Internet. As a promising networking paradigm that decouples control plane from data plane, software-defined networking (SDN) is an appropriate architecture for IoT. The SDN paradigm supports deploying traffic flows dynamically by a centralized controller to SDN switches. In particular, the controller configures forwarding rules of SDN switches to steer traffic. However, forwarding rules are usually stored in expensive and power hungry ternary content addressable memory (TCAM), which is very limited in quantity for SDN switches. Thus, the shortage of TCAM becomes a fatal bottleneck for scalable flow management for SDN-based IoT. To this end, we propose a method of forwarding rule multiplexing (FRM) to minimize the total number of forwarding rules in SDN-based IoT. We multiplex different traffic flows traversing through the same path into an aggregated flow with the label of VLAN ID. As a result, multiple forwarding rules could be merged into one multiplexed rule. We also extend the method to SDN protection against link failure, and reduce backup path forwarding rules. We formulate the FRM problem as an integer linear programming model. Since the problem is NP-hard, we design a polynomial algorithm using the Markov approximation technique. Theoretical analysis indicates that the polynomial algorithm generates near-optimal solution. The extensive emulation results show that the proposed Markov approximation-based algorithm reduces the number of forwarding rules by 15.73% in average compared with the benchmark algorithms.