Research Article
Multi-service Routing with Guaranteed Load Balancing for LEO Satellite Networks
@INPROCEEDINGS{10.1007/978-3-030-41114-5_22, author={Cui-Qin Dai and Guangyan Liao and P. Takis Mathiopoulos and Qianbin Chen}, title={Multi-service Routing with Guaranteed Load Balancing for LEO Satellite Networks}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part I}, proceedings_a={CHINACOM}, year={2020}, month={2}, keywords={LEO satellite network Routing Multi-service Load balancing}, doi={10.1007/978-3-030-41114-5_22} }- Cui-Qin Dai
Guangyan Liao
P. Takis Mathiopoulos
Qianbin Chen
Year: 2020
Multi-service Routing with Guaranteed Load Balancing for LEO Satellite Networks
CHINACOM
Springer
DOI: 10.1007/978-3-030-41114-5_22
Abstract
Low Earth Orbit (LEO) Satellite Networks (SN) offers communication services with low delay, low overhead, and flexible networking. As service types and traffic demands increase, the multi-service routing algorithms play an important role in ensuring users’ Quality of Service (QoS) requirements in LEO-SN. However, the multi-service routing algorithm only considers the link QoS information, ignoring the uneven distribution of ground users, causing satellite link or node congestion, increasing the packet transmission delay, and packet loss rate. In order to solve the above problems, we propose a Multi-Service Routing with Guaranteed Load Balancing (MSR-GLB) algorithm which balances the network load while satisfying multi-service QoS requirements. Firstly, the Geographic Location Information Factors (GLIF) are defined to balance the network load by scheduling the ISLs with lower loads. Then, the optimization objective function is constructed by considering delay, remaining bandwidth, packet loss rate, and GLIF in order to characterize the routing problems caused by multi-service and load balancing. Following this, we propose an MSR-GLB algorithm that includes the state transition rule and the pheromone update rule. Among them, the state transition rule is based on QoS information and link GLIF, and the pheromone update rule has the characteristics of positive and negative feedback mechanism. The simulation results show that the MSR-GLB algorithm can well meet the QoS requirements of different services, balance the network load compared to Cross-layer design and Ant-colony optimization based Load-balancing routing algorithm in LEO Satellite Network (CAL-LSN) and Multi-service On-demand Routing (MOR) algorithm.
