HaRD: a heterogeneity-aware replica deletion for HDFS

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Title: HaRD: a heterogeneity-aware replica deletion for HDFS
Authors: Ciritoglu, Hilmi EgemenMurphy, JohnThorpe, Christina
Permanent link: http://hdl.handle.net/10197/11328
Date: 21-Oct-2019
Online since: 2020-03-20T13:14:59Z
Abstract: The Hadoop distributed file system (HDFS) is responsible for storing very large data-sets reliably on clusters of commodity machines. The HDFS takes advantage of replication to serve data requested by clients with high throughput. Data replication is a trade-off between better data availability and higher disk usage. Recent studies propose different data replication management frameworks that alter the replication factor of files dynamically in response to the popularity of the data, keeping more replicas for in-demand data to enhance the overall performance of the system. When data gets less popular, these schemes reduce the replication factor, which changes the data distribution and leads to unbalanced data distribution. Such an unbalanced data distribution causes hot spots, low data locality and excessive network usage in the cluster. In this work, we first confirm that reducing the replication factor causes unbalanced data distribution when using Hadoop’s default replica deletion scheme. Then, we show that even keeping a balanced data distribution using WBRD (data-distribution-aware replica deletion scheme) that we proposed in previous work performs sub-optimally on heterogeneous clusters. In order to overcome this issue, we propose a heterogeneity-aware replica deletion scheme (HaRD). HaRD considers the nodes’ processing capabilities when deleting replicas; hence it stores more replicas on the more powerful nodes. We implemented HaRD on top of HDFS and conducted a performance evaluation on a 23-node dedicated heterogeneous cluster. Our results show that HaRD reduced execution time by up to 60%, and 17% when compared to Hadoop and WBRD, respectively.
Funding Details: European Commission - European Regional Development Fund
Science Foundation Ireland
Type of material: Journal Article
Publisher: Springer
Journal: Journal of Big Data
Volume: 6
Issue: 1
Copyright (published version): 2019 the Authors
Keywords: Hadoop distributed file system (HDFS)Replication factorReplica management frameworkSoftware performance
DOI: 10.1186/s40537-019-0256-6
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection

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