Paper Info
Title
A Large-Scale And Decentralized Infrastructure For Multiple Queries Optimization And Aggregation
Abstract
Leveraging DHTs (Distributed Hash Table), we propose a novel architecture, which applies multiple query optimization technique to efficiently aggregate queries in large scale distributed networks. We target applications that continuously query the data sources and manipulate a large amount of query results. Our goal is to implement system components to aggregate distributed queries in the network, so as to (1) reduce the overhead (CPU cycles, disk I/O etc.) on the data source nodes for query evaluation; (2) save the overall network bandwidth cost for delivering queries and the query results. To deal with the skewed load distribution, we also provide load balancing mechanisms to ensure that no node in the system is unduly loaded.The simulation results show that the proposed architecture can efficiently distribute the query processing in the network and significantly reduce the number of queries evaluated on the data source nodes. The network bandwidth consumption is largely reduced by eliminating the transmission of common data items.
Year
DOI
Venue
2008
10.1109/MASCOT.2008.4770584
2008 IEEE INTERNATIONAL SYMPOSIUM ON MODELING, ANALYSIS & SIMULATION OF COMPUTER AND TELECOMMUNICATION SYSTEMS (MASCOTS)
Keywords
Field
DocType
distributed processing,databases,load balance,query optimization,resource allocation,data mining,bandwidth,load distribution,distributed hash table,distributed databases
Load management,Query optimization,Load balancing (computing),Computer science,Sargable,Distributed database,Online aggregation,Instruction cycle,Distributed computing,Distributed hash table
Conference
ISSN
Citations 
PageRank 
1526-7539
0
0.34
References 
Authors
15
2
Name
Order
Citations
PageRank
Xiaoyu Yang1555.21
Yiming Hu263944.91