Research Area: Software Defined Networks
Distributed in-network cloud architecture is a promising solution to efficiently host next generation internet-of-things (IoT) services. With the rapid increase of IoT devices and applications, the backhaul or backbone networks, which transmit IoT traffic to various in-network clouds, will experience a predicted explosion in the volume of carried traffic. To guarantee the QoS of IoT cloud services and improve the network performance, it is crucial for network operator to implement efficient routing optimization strategies for IoT traffic. As a promising networking paradigm, software-defined networking (SDN) has flexible and programmable control capability for fine-grained flows. The emergence of SDN paves a way for implementing high-performance routing optimization in networks. In SDN networks, the routing strategies are realized through flow rules, which are usually stored in TCAM with very limited capacity. However, the number of IoT flows are enormous. Thus, in this paper, we address the routing optimization problem in SDN-based IoT with TCAM capacity constraint. We first formulate the problem as a mixed integer linear programming problem and prove the problem is NP-hard. Then to solve the problem efficiently, we propose several approximate algorithms, which solve the problem in two stages. In the first stage, the algorithms calculate the routing strategies for flows without considering the TCAM capacity constraint. To meet the TCAM capacity constraint, the algorithms using different strategies to adjust the paths of some flows in the second stage. Extensive simulations are conducted on both real ISP and synthetic topologies to evaluate the performance of the algorithms. The simulation results verify that the algorithms can achieve promising load balancing performance in SDN-based IoT, where the capacity of TCAM in SDN switches is very limited.
Keywords:
Internet of things
routing optimization
software-defined networking
ternary content-addressable memory
Author(s) Name: Shizhong Xu; Xiong Wang; Guangxu Yang; Jing Ren; Sheng Wang
Journal name: Journal of Communications and Networks
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/JCN.2020.000006
Volume Information: Volume: 22, Issue: 2, April 2020, Page(s): 145 - 158
