面向QoS需求的分簇自组织网络路由算法

基于分布式分簇的网络管理架构，网络节点可以被划分成多个管理域，并由相应区域的簇首进行协同管理。为实现分布式网络场景中，业务差异化的服务质量（QoS）需求与多维度网络资源之间的高效按需匹配，提出了一种基于强化学习的路由调度算法，以降低端到端的时延和防止网络拥塞为目标，优化调度路径。所提算法可以通过簇首集中式和节点分布式2种方式实现，可以解决分布式环境下全局资源信息不完备的问题，有效保证跳变环境下网络的健壮性。将100个节点划分为4个管理域进行仿真验证。仿真结果表明，所提算法可以有效地降低业务的平均时延，并且在业务拒绝率、网络资源利用率方面均优于传统方法。

QoS Routing Algorithm in Clustered Self-Organizing Networks

Based on the distributed and clustering network architecture, network nodes can be divided into multiple clusters, which can be managed by their corresponding cluster heads in a collaborative manner. In order to achieve on-demand, efficient matches between the differentiated quality of service (QoS) requirements and the multi-dimensional network resources, a reinforcement learning-based routing algorithm is proposed. The proposed algorithm aims to reduce end-to-end delay and prevent congestion by optimizing the routing path, which can be implemented in both centralized cluster heads and distributed nodes, so as to guarantee the robustness in a dynamic environment. The performance is evaluated by numerical simulations in a network with 100 nodes divided into four regions. The simulation results illustrate that the proposed algorithm can reduce average latency significantly. Besides, the algorithm proposed is superior to the minimum-hop method in terms of rejection rate and resource utilization.