English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Eliminating duplicate data in primary storage of clouds increases thecost-efficiency of cloud service providers as well as reduces the cost of usersfor using cloud services. Existing primary deduplication techniques either useinline caching to exploit locality in primary workloads or use post-processingdeduplication running in system idle time to avoid the negative impact on I/Operformance. However, neither of them works well in the cloud servers runningmultiple services or applications for the following two reasons: Firstly, thetemporal locality of duplicate data writes may not exist in some primarystorage workloads thus inline caching often fails to achieve good deduplicationratio. Secondly, the post-processing deduplication allows duplicate data to bewritten into disks, therefore does not provide the benefit of I/O deduplicationand requires high peak storage capacity. This paper presents HPDedup, a HybridPrioritized data Deduplication mechanism to deal with the storage system sharedby applications running in co-located virtual machines or containers by fusingan inline and a post-processing process for exact deduplication. In the inlinededuplication phase, HPDedup gives a fingerprint caching mechanism thatestimates the temporal locality of duplicates in data streams from differentVMs or applications and prioritizes the cache allocation for these streamsbased on the estimation. HPDedup also allows different deduplication thresholdfor streams based on their spatial locality to reduce the disk fragmentation.The post-processing phase removes duplicates whose fingerprints are not able tobe cached due to the weak temporal locality from disks. Our experimentalresults show that HPDedup clearly outperforms the state-of-the-art primarystorage deduplication techniques in terms of inline cache efficiency andprimary deduplication efficiency.

HPDedup: A Hybrid Prioritized Data Deduplication Mechanism for Primary Storage in the Cloud