基于DHT的拓扑感知节点聚集算法

在文件共享、流媒体和协作计算等P2P应用模型中,节点间采用单播通信并构建出对应的覆盖网络.由于覆盖网络通常建立在已有的底层网络之上,节点随机加入系统将导致上下层网络拓扑不匹配,不仅增加了节点间通信延时而且给底层网络带来较大的带宽压力.当前的拓扑匹配算法尚存在可扩展性低、节点聚集时延长等问题.在网络坐标算法和DHT算法基础之上,提出一种分布式的拓扑感知节点聚集算法TANRA,利用等距同心圆簇对节点二维网络坐标平面进行等面积划分,并根据节点所处区域进行多层命名空间中区间的一一映射.由于保留了节点之间的邻近关系,从而可使用DHT基本的"发布"和"搜索"原语进行相邻节点聚集.仿真结果表明,TANRA算法在大规模节点数时能有效保证网络拓扑匹配,并且具有较低的加入延时.

Topology-Aware Node Rendezvous Algorithm Based on DHT

In the peer-to-peer application models such as file sharing,media streaming and cooperative computing,nodes communicate by unicast and construct overlay networks.As overlay networks is usually built on top of existing substrate network,random joining of nodes will lead to topology mismatching,which will not only increase the communication delay between end nodes but also make a heavy bandwidth pressure on the substrate network.Current topology-matching algorithms suffer low scalability and long clustering delays.Proposed in this paper is a novel distributed topology-aware node rendezvous algorithm(TANRA)based on network coordinates and distributed hash table(DHT).In TANRA,firstly,2-dimension network coordinates space of nodes is partitioned into many sectors which have the same area by means of using a cluster of concentric circles.Secondly,every sector is mapped to a unique region in the multi-layer name space of DHT based on the geographic information in 2-dimension network coordinates space.Finally,the nodes can be clustered with its near neighbors in substrate network by calling two primitives of DHT,Put()?Get(),as the proximity between nodes is kept.The simulation results show that TANRA can efficiently provide the topology matching for upper applications with lower joining delay under a large number of end nodes.