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    On the Power of In-Network Caching in the Hadoop Distributed File System

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    Name:
    Hadoop_ICN.pdf
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    741.9Kb
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    Description:
    Final Accepted Manuscript
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    Author
    Newberry, Eric
    Zhang, Beichuan
    Affiliation
    Univ Arizona, Dept Comp Sci
    Issue Date
    2019-09-24
    Keywords
    Caching
    Spark
    HDFS
    Big data
    Named data networking
    NDN
    Information-centric networking
    ICN
    
    Metadata
    Show full item record
    Publisher
    ASSOC COMPUTING MACHINERY
    Citation
    Eric Newberry and Beichuan Zhang. 2019. On the Power of In-Network Caching in the Hadoop Distributed File System. In 6th ACM Conference on Information-Centric Networking (ICN ’19), September 24–26, 2019, Macao, China. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3357150. 3357392
    Journal
    PROCEEDINGS OF THE 2019 CONFERENCE ON INFORMATION-CENTRIC NETWORKING (ICN '19)
    Rights
    © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    The Hadoop Distributed File System (HDFS) is a network file system used to support multiple widely-used big data frameworks that can scale to run on large clusters. In this paper, we evaluate the effectiveness of using in-network caching on switches in HDFSsupported clusters in order to reduce per-link bandwidth usage in the network. We discovered that some applications featured large amounts of data requested by multiple clients and that, by caching read data in the network, the average per-link bandwidth usage of read operations in these applications could be reduced by more than half. We also found that the choice of cache replacement policy could have a significant impact on caching effectiveness in this environment, with LIRS and ARC generally performing the best for larger and smaller cache sizes, respectively. Moreover, given the structure of HDFS write operations, we developed a mechanism to reduce the total per-link bandwidth usage of HDFS write operations by replacing write pipelining with multicast. In order to evaluate in-network caching potential, we developed a simulator to replay real traces through a fat tree network simulating the caching architecture used in the Named Data Networking (NDN) information-centric networking (ICN) architecture. Our results suggest that ICN-style in-network caching can provide significant benefits to HDFS-supported big data clusters, justifying future work to apply ICN architectures to cluster environments.
    DOI
    10.1145/3357150.3357392
    Version
    Final accepted manuscript
    Sponsors
    National Science Foundation
    ae974a485f413a2113503eed53cd6c53
    10.1145/3357150.3357392
    Scopus Count
    Collections
    UA Faculty Publications

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