Skip to main navigation Skip to search Skip to main content

A Comprehensive Study of Data Reduction Methods on Docker Container Images

  • Harbin Institute of Technology Shenzhen
  • Peng Cheng Laboratory

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The rapid growth of Docker images in cloud infrastructures has intensified storage and network demands, posing challenges to QoS for registries and deployments. This paper systematically evaluates four data reduction methods - file-level and block-level deduplication, delta compression, and local compression - on 18 representative images. We quantify their tradeoffs in compression, computation, I/O overhead, and restore performance, revealing that (1) filtering large files (>64 KB) preserves 80% of redundancy elimination at half the cost, (2) category-based reorganization reduces restore latency by 83%, and (3) fixed-size chunking with optimized blocks balances memory and compression. Based on these insights, we propose adaptive strategies for efficient container storage.

Original languageEnglish
Title of host publication2025 IEEE/ACM 33rd International Symposium on Quality of Service, IWQoS 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331549404
DOIs
StatePublished - 2025
Externally publishedYes
Event33rd IEEE/ACM International Symposium on Quality of Service, IWQoS 2025 - Gold Coast, Australia
Duration: 2 Jul 20254 Jul 2025

Publication series

NameIEEE International Workshop on Quality of Service, IWQoS
ISSN (Print)1548-615X

Conference

Conference33rd IEEE/ACM International Symposium on Quality of Service, IWQoS 2025
Country/TerritoryAustralia
CityGold Coast
Period2/07/254/07/25

Fingerprint

Dive into the research topics of 'A Comprehensive Study of Data Reduction Methods on Docker Container Images'. Together they form a unique fingerprint.

Cite this