Parallel database systems: the future of high performance database systems
Communications of the ACM
LH: Linear Hashing for distributed files
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Lazy updates for distributed search structure
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Distributing a search tree among a growing number of processors
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Extendible hashing—a fast access method for dynamic files
ACM Transactions on Database Systems (TODS)
Design and Implementation of DDH: A Distributed Dynamic Hashing Algorithm
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Linear hashing: a new tool for file and table addressing
VLDB '80 Proceedings of the sixth international conference on Very Large Data Bases - Volume 6
Full-speed scalability of the pDomus platform for DHTs
PDCN'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: parallel and distributed computing and networks
VECPAR'02 Proceedings of the 5th international conference on High performance computing for computational science
EH*RS: a high-availability scalable distributed data structure
ICA3PP'07 Proceedings of the 7th international conference on Algorithms and architectures for parallel processing
Domus – an architecture for cluster-oriented distributed hash tables
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
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In today's world of computers, dealing with huge amounts of data is not unusual. The need to distribute this data in order to increase its availability and increase the performance of accessing it is more urgent than ever. For these reasons it is necessary to develop scalable distributed data structures. In this paper we propose EH*, a distributed variant of the Extendible Hashing data structure. It consists of buckets of data that are spread across multiple servers and autonomous clients that can access these buckets in parallel. EH* is scalable in the sense that it grows gracefully, one bucket at a time, to a large number of servers. The communication overhead is relatively independent of the number of servers and clients in the system. EH* offers a high query efficiency and good storage space utilization. The simulation results reveal that the method is comparable to the LH* introduced by Witold Litwin.