Alexandria: A Proof-of-concept Implementation and Evaluation of Generalised Data Deduplication

Lars Nielsen, Rasmus Vestergaard, Niloofar Yazdani, Siva Rama Krishna Prasad Talasila, Daniel Enrique Lucani Rötter, Marton Sipos

    Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

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    The amount of data generated worldwide is expected to grow from 33 to 175 ZB by 2025 in part driven by the growth of Internet of Things (IoT) and cyber-physical systems (CPS). To cope with this enormous amount of data, new cloud storage techniques must be developed. Generalised Data Deduplication (GDD) is a new paradigm for reducing the cost of storage by systematically identifying near identical data chunks, storing their common component once, and a compact representation of the deviation to the original chunk for each chunk. This paper presents a system architecture for GDD and a proof-of-concept implementation. We evaluated the compression gain of Generalised Data Deduplication using three data sets of varying size and content and compared to the performance of the EXT4 and ZFS file systems, where the latter employs classic deduplication. We show that Generalised Data Deduplication provide up to 16.75% compression gain compared to both EXT4 and ZFS with data sets with less than 5 GB of data.
    Original languageEnglish
    Title of host publication2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings
    Publication date2019
    Article number9024368
    ISBN (Electronic)978-1-7281-0960-2
    Publication statusPublished - 2019
    Event2019 IEEE Globecom Workshops -
    Duration: 9 Dec 201913 Dec 2019


    Conference2019 IEEE Globecom Workshops


    • Deduplication
    • Edge computing
    • Edge storage


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