非结构化对等网络中的路由优化技术研究
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摘要
非结构化对等网络(P2P:Peer to Peer)是目前对等计算应用领域最重要的网络模型之一。由于在非结构化P2P中,即没有中心索引,也没有对网络拓扑和文件位置的精确控制,发起查询的请求结点(Requester),多采用泛洪式路由向其逻辑邻居结点(Neighbor),广播查询请求。邻居结点收到请求后,如果能够满足该请求,就成为响应结点(Responder),返回查询结果;如果不能响应该请求,就成为转发结点(Relayer),采用与请求结点相同的方法继续向自己的邻居结点广播请求。该过程直至请求得以满足或者发生某些错误为止。研究表明,在适度连接的P2P网络中,使用盲目泛洪查询(BFS:Blind Flooding Search)算法会产生大量的查询消息,且70%以上都是冗余的。这些冗余消息占用网络带宽、消耗结点的处理时间,却对扩展查询范围毫无益处。因此,如何减轻泛洪引起的网络负担,同时保证一定的查询命中率,已经成为一个研究的热点。基于对上述问题的考虑,本文提出了自适应泛洪路由算法(AFRA:Adaptive Flooding Routing Algorithm)和基于主动推荐机制的近似索引算法(ARMBDAI:Active Recommendation Mechanism Based Distributed Approximate Indexing)。
     本文提出的自适应泛洪路由算法结合了深度调整和广度调整。所谓深度调整,就是通过调整每条消息的生命期(TTL:Time to Live)来限制消息的传播范围。所谓广度调整,就是通过调整接收消息的邻结点数来减少消息的转发数量。现有文献虽然也提及广度调整和深度调整,但据我们所知,如何结合广度调整和深度调整,达到路由优化目的,还未见成熟方案。为此本文就相关问题进行了讨论,并详细阐述算法的原理和实现,最后通过仿真实验验证了算法的有效性。
     本文提出的基于主动推荐机制的近似索引算法以面向服务的主动推荐机制为基础,在保留泛洪查询动态性好、可维护性高和可靠性强等优点的前提下,构建了一种由信誉评估代理和信息索引代理组成的两级代理结构。该结构有效地结合了查询和信誉两种机制,提高了查询的准确性和系统的可靠性。仿真实验验证了算法的有效性和对网络拓扑的优化。
In unstructured P2P systems, it is hard to find the desired files without distributing queries widely, since there is neither a centralized directory nor any precise control over the network topology or file placement. Flooding-based routing and search approach is widely used in unstructured P2P systems. In blind flooding, each peer sends query message to all of its neighbor peers, which in turn continuously forwarded to all neighbors. The query is executed hop-to-hop through the unstructured network until success, failure or timeout. By flooding the whole P2P network, many messages are generated. It is shown that more than 70% of the generated messages are redundant for a flooding with a fixed TTL (Time-to-Live) in a moderately connected network. These redundant messages consume bandwidth and waste processing power, but it is useless to enlarge the search range and improve the searching coverage. To address the above problems, many researchers have been studying how to reduce flooding radius while maintaining the satisfactory search quality.
     In this dissertation, we propose a new routing and search algorithm, Adaptive Flooding Routing Algorithm (AFRA). AFRA combines depth adjustment and width adjustment. The depth adjustment is a method that reduces the number of routing messages by adjusting TTL value. The width adjustment means that a peer does not relay a query to all neighbors, but define the number of neighbors before forwarding the query. To our knowledge, although the existing documents have mentioned width adjustment and depth adjustment, there is no practical solution on how to combine width and depth adjustment to optimize search mechanisms. Experimental results show that our AFRA scheme can reduce flooding message by about 65% while maintaining the acceptable high search quality.
     To address the severe performance limitation of flooding search mechanism, we propose an Active Recommendation Mechanism Based Distributed Approximate Indexing (ARMBDAI) method. Our scheme can work in high dynamic P2P networks and enable users to share valuable information with others. By Active Recommendation Mechanism (ARM), we build a two-tier proxy architecture consisting of reputation evaluating proxies and content indexing proxies. Based on ARM and the new architecture, we purpose a Distributed Approximate Indexing (DAI) method to support efficient search without the need for a single centralized index host. The effectiveness of DAI is demonstrated through simulation studies. Our experimental results show that DAI solution can improve search efficiency and optimize network topology.
引文
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