Performance of Generalized Deduplication Under Different Input Conditions

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

Abstract

In the era of exponential growth in data, compression of data is needed to limit the growth in the required storage space and supporting infrastructure. Recently, generalized deduplication has been proposed as a better way of performing data deduplication. We use Golomb Rice transform for providing generalized deduplication. With this setup, we evalute the performance of generalized deduplication for the input conditions modeled using binomial, geometric, poisson and uniform distributions. We derive the closed form expressions for pmf of the data after generalized deduplication. For all the input conditions, generalized deduplicaton transforms the input pmf into a new pmf that is highly suitable for deduplication, reduces size of the deduplication table, provides comparable compression gain for fewer number of input chunks.
Original languageEnglish
Title of host publicationEuropean Wireless Conference : 26th European Wireless Conference
Number of pages7
PublisherIEEE
Publication date2021
ISBN (Electronic)978-3-8007-5672-8
Publication statusPublished - 2021
EventEuropean Wireless 2021 - Verona , Italy
Duration: 10 Nov 202112 Nov 2021

Conference

ConferenceEuropean Wireless 2021
Country/TerritoryItaly
City Verona
Period10/11/202112/11/2021

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  • Scale-loT

    Lucani Rötter, D. E.

    01/01/201831/12/2022

    Project: Research

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