基于DHT的拓扑感知节点聚集算法

Topology-Aware Node Rendezvous Algorithm Based on DHT

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.

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 bui wh t on top of existing substrate network, random joining of nodes will lead to topology mismatching, ch 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.