Goodput Aware Load Distribution for Real-time Traffic over Multipath Networks Using Spectrum Sensing in Cognitive Radio

Load distribution is a key research issue in deploying the limited network resources available to support traffic transmissions. Developing an effective solution is critical for enhancing traffic performance and network utilization. In this paper, we investigate the problem of load distribution for real-time traffic over multipath
networks. Due to the path diversity and unreliability in
heterogeneous overlay networks, large end-to-end delay and
consecutive packet losses can significantly degrade the traffic flow’s goodput, whereas existing studies mainly focus on the delay or throughput performance. To address the challenging problems, we propose a Goodput Aware Load distribution (GALTON) model that includes three phases: (1) path status estimation to accurately sense the quality of each transport link, (2) flow rate assignment to optimize
the aggregate goodput of input traffic, and (3) deadline-constrained packet interleaving to mitigate consecutive losses.