A combination of DHTs and Peer Clustering for Distributed Information Retrieval

Distributed Hash Tables (DHTs) are very efficient for querying based on key lookups, if only a small number of keys has to be registered by each individual peer. However, building huge term indexes, as required for IR-style keyword search, are impractical with plain DHTs. Due to the large sizes of document term vocabularies, joining peers cause huge amounts of key inserts, and subsequently large numbers of index maintenance messages. Thus, the key to exploiting DHTs for distributed information retrieval is to reduce index maintenance. We show that this can be achieved by combining DHTs with peer clustering. Peers are first clustered into communities, each of the communities having a representative super-peer. Then all occurrences of a term in a community are published to the global DHT in a batch by the representative super-peer. Our evaluation shows that this reduces index maintenance cost by an order of magnitude, while still keeping a complete and correct term index for query processing.