EH/sup */-extendible hashing in a distributed environment

Hilford, V.; Bastani, Farokh B.; Čukić, Bojan

doi:10.1109/cmpsac.1997.624796

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…Victoria Hilford et. al proposed a variation of extendible hashing method for distributed environments [9]. The buckets were spread across different servers and clients could access the data in parallel from different systems.…”

Section: Related Workmentioning

confidence: 99%

BLEH: Bit-Less Extendible Hashing for DBMS and Hard Disk Drives

Sridevi*,

Ashoka

2019

IJITEE

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Indexing techniques such as extendible hashing and B-trees are widely used to store, retrieve and search for data on files in most file systems. These techniques have been comprehensively explored to enhance the data structure for increasing the faster access to file contents. Extendible hashing is a dynamic hashing technique which handles dynamic files that keep changing in size. Traditional extendible hashing uses bit addresses to hash the data to buckets and restricts the directory size to be a power of 2 which has corresponding complications in implementation. Restriction on directory size also results in uneven distribution of data which increases the possibility of overflows. This in turn increases the cost of index maintenance. In this paper, an efficient and simpler to implement variation of Extendible hashing method named Bit-Less Extendible Hashing (BLEH) for dynamic files is proposed. The proposed method eliminates the need for binary representation of the hash address which reduces the complexity of implementation. Furthermore, it eliminates the need for the directory size to be a power of 2 providing flexibility in the choice of initial directory size. The experimental results show that the proposed method provides better performance in terms of split count upon insertion when compared to the traditional extendible hashing method with good space utilization.

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Section: Related Workmentioning

confidence: 99%

BLEH: Bit-Less Extendible Hashing for DBMS and Hard Disk Drives

Sridevi*,

Ashoka

2019

IJITEE

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“…Tables (DHTs) are often used to implement distributed dictionaries. Research in DHTs started with models tailored to small/medium-scale networks of workstations (NOWs) [10,8,9], and continued with more recent contributions focused in large-scale/internet-wide peer-to-peer (P2P) scenarios [12,18,14]; the later demand support for i) efficient routing, ii) wide-area intermittent network connections, iii) continuous arrival/departure of nodes, iv) security/anonymity, etc; this requisites may be relaxed in a typical cluster, a tightly integrated hardware/software environment, of a much lower scale, running on private (and often very high-bandwidth) local networks.…”

Section: Introductionmentioning

confidence: 99%

pDomus: a prototype for Cluster-oriented Distributed Hash Tables

Rufino¹,

Alves²,

Exposto³

et al. 2007

15th EUROMICRO International Conference on Parallel, Distributed and Network-Based Processing (PDP'07)

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“…Distributed Hash Tables (DHTs) are one of the most popular approaches to distributed dictionaries. Research in this domain has been prolific, ranging from 1st generation models, oriented to the cluster environment [1,2,3,4], to numerous recent contributions, most exclusively focused in the area of P2P systems [5,6].…”

Section: Introductionmentioning

confidence: 99%

Domus – An Architecture for Cluster-Oriented Distributed Hash Tables

Rufino

Pina

Alves

et al. 2006

Parallel Processing and Applied Mathematics

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This paper presents a high level description of Domus, an architecture for cluster-oriented Distributed Hash Tables. As a data management layer, Domus supports the concurrent execution of multiple and heterogeneous DHTs, that may be simultaneously accessed by different distributed/parallel client applications. At system level, a load balancement mechanism allows for the (re)distribution of each DHT over cluster nodes, based on the monitoring of their resources, including CPUs, memory, storage and network. Two basic units of balancement are supported: vnodes, a coarse-grain unit, and partitions, a fine-grain unit. The design also takes advantage of the strict separation of object lookup and storage, at each cluster node, and for each DHT. Lookup follows a distributed strategy that benefits from the joint analysis of multiple partition-specific routing information, to shorten routing paths. Storage is accomplished through different kinds of data repositories, according to the specificity and requirements of each DHT.

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EH/sup */-extendible hashing in a distributed environment

Cited by 6 publications

References 8 publications

BLEH: Bit-Less Extendible Hashing for DBMS and Hard Disk Drives

BLEH: Bit-Less Extendible Hashing for DBMS and Hard Disk Drives

pDomus: a prototype for Cluster-oriented Distributed Hash Tables

Domus – An Architecture for Cluster-Oriented Distributed Hash Tables

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