A Routing Model Based on Multiple-User Requirements and the Optimal Solution

Abstract

To promote the development of high-bandwidth IP networks of nongeostationary Earth orbit (NGEO) satellites, this paper designs a new benefit measurement model. Moreover, we propose the class-A QoS benefit criterion (QABC), which is established based on a mathematical model involving the remaining queue length and QoS. The benefit model includes three maximum and minimum conflict subgoals and is designed to avoid the problem that the ant colony algorithm easily falls to local optima. This new model is solved with the wolf colony algorithm, which provides both next-hop selection and bottleneck bandwidth reservation mechanisms. Additionally, many burst flows can occur in satellite networks, and they lead to a slow convergence speed and unnecessary overhead. In this paper, the Kalman filter algorithm is used to address these issues. Considering the long-term correlation of satellite traffic, it is feasible to smooth the queue length with a Kalman filter. Finally, the Kalman filter wolf colony algorithm (KFWCA) can solve the conflict problem in the new benefit measurement model. The algorithm is applied in the network simulator NS2.35, and the results verify the effectiveness of load balancing and QoS maximization in a satellite network. The results indicate that the KFWCA yields better performance than other algorithms based on traffic allocation, the average delay, the packet loss rate and other factors, especially under high-traffic conditions. INDEX TERMS benefit measurement model; KFWCA; QoS support; load balancing I. INTRODUCTION

BeiDou satellites cross-regional communication path assignment model and resource management

“…QoS includes congestion avoidance, load balancing, QoS guarantees, and other technologies. 16 Li et al 17 established a low earth orbit (LEO) satellite model and designed a QoS routing (QSR) strategy with the aim of minimizing delay and packet loss. In the study by Wu et al, 18 a global-view–based intelligent QSR (IQR) algorithm and controller placement strategy were designed to adapt to varying QoS requirements.…”