Query Processing to Efficient Search in Ubiquitous Computing
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
A New Caching Technique to Support Conjunctive Queries in P2P DHT
IEICE - Transactions on Information and Systems
PAIS: A Proximity-Aware Interest-Clustered P2P File Sharing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Distance-based bloom filter for an efficient search in mobile ad hoc networks
Proceedings of the 2007 conference on Human interface: Part I
Keyword search in DHT-based peer-to-peer networks
ICA3PP'07 Proceedings of the 7th international conference on Algorithms and architectures for parallel processing
Efficient search technique for agent-based P2P information retrieval
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
Fuzzy-based load self-configuration in mobile P2P services
Computer Networks: The International Journal of Computer and Telecommunications Networking
Clustering peers based on contents for efficient similarity search
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
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Peer-to-peer (P2P) computing has become a popular distributed computing paradigm thanks to abundant computing power of modern desktop workstations and widely available network connectivity via the Internet. Although P2P file sharing provides a scalable alternative to conventional server-based approaches, providing efficient file search in a large scale dynamic P2P system remains a challenging problem. In this paper, we propose a set of mechanisms to provide a scalable keyword-based file search in distributed hash table (DHT)-based P2P systems. In particular, we address the problem induced by common keywords that are associated with a large number of files and thus require excessive storage consumptions from the hosting peers. Our proposed architecture, called keyword fusion, adaptively unburdens the peers overloaded with excessive storage consumptions due to common keywords and reduces network bandwidth consumption by transforming users' queries to contain more focused search terms. Through trace-driven simulations, we show that keyword fusion can reduces the storage consumption of the top 5% most loaded nodes by 50% and decrease the search traffic by up to 68% even in the modest scenarios of combining two keywords.