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

TY - GEN

T1 - Alexandria: A Proof-of-concept Implementation and Evaluation of Generalised Data Deduplication

AU - Nielsen, Lars

AU - Vestergaard, Rasmus

AU - Yazdani, Niloofar

AU - Talasila, Siva Rama Krishna Prasad

AU - Lucani Rötter, Daniel Enrique

AU - Sipos, Marton

PY - 2019/12

Y1 - 2019/12

N2 - 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.

AB - 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.

KW - Deduplication

KW - Edge computing

KW - Edge storage

UR - http://www.scopus.com/inward/record.url?scp=85082297952&partnerID=8YFLogxK

U2 - 10.1109/GCWkshps45667.2019.9024368

DO - 10.1109/GCWkshps45667.2019.9024368

M3 - Article in proceedings

BT - 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings

PB - IEEE

T2 - 2019 IEEE Globecom Workshops

Y2 - 9 December 2019 through 13 December 2019

ER -