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Difficulty-Aware Hybrid Search in Peer-to-Peer Networks

机译：对等网络中的难度感知混合搜索

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摘要

By combining an unstructured protocol with a DHT-based index, hybrid Peer-to-Peer (P2P) improves search efficiency in terms of query recall and response time. The key challenge in hybrid search is to estimate the number of peers that can answer a given query. Existing approaches assume that such a number can be directly obtained by computing item popularity. In this work, we show that such an assumption is not always valid, and previous designs cannot distinguish whether items related to a query are distributed in many peers or are in a few peers. To address this issue, we propose QRank, a difficulty-aware hybrid search, which ranks queries by weighting keywords based on term frequency. Using rank values, QRank selects proper search strategies for queries. We conduct comprehensive trace-driven simulations to evaluate this design. Results show that QRank significantly improves the search quality as well as reducing system traffic cost compared with existing approaches.
机译：通过将非结构化协议与基于DHT的索引相结合，混合对等（P2P）可以提高查询回想率和响应时间的搜索效率。混合搜索中的关键挑战是估计可以回答给定查询的对等体的数量。现有方法假定可以通过计算项目受欢迎程度来直接获得这样的数量。在这项工作中，我们证明了这种假设并不总是有效的，并且以前的设计无法区分与查询相关的项目是分布在多个对等实体中还是在几个对等实体中。为了解决这个问题，我们提出了QRank，一种可感知困难的混合搜索，可通过根据词频对关键字加权来对查询进行排名。使用排名值，QRank选择适当的搜索策略进行查询。我们进行全面的跟踪驱动模拟，以评估该设计。结果表明，与现有方法相比，QRank显着提高了搜索质量并降低了系统流量成本。

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