Paper Info
Title
Large-Scale Analysis of Docker Images and Performance Implications for Container Storage Systems
Abstract
Docker containers have become a prominent solution for supporting modern enterprise applications due to the highly desirable features of isolation, low overhead, and efficient packaging of the application’s execution environment. Containers are created from images which are shared between users via a registry. The amount of data registries store is massive. For example, Docker Hub, a popular public registry, stores at least half a million public images. In this article, we analyze over 167 TB of uncompressed Docker Hub images, characterize them using multiple metrics and evaluate the potential of file-level deduplication. Our analysis helps to make conscious decisions when designing storage for containers in general and Docker registries in particular. For example, only 3 percent of the files in images are unique while others are redundant file copies, which means file-level deduplication has a great potential to save storage space. Furthermore, we carry out a comprehensive analysis of both small I/O request performance and copy-on-write performance for multiple popular container storage drivers. Our findings can motivate and help improve the design of data reduction and caching methods for images, pulling optimizations for registries, and storage drivers.
Year
DOI
Venue
2021
10.1109/TPDS.2020.3034517
IEEE Transactions on Parallel and Distributed Systems
Keywords
DocType
Volume
Containers,Docker,container images,container registry,deduplication,Docker hub,container storage drivers
Journal
32
Issue
ISSN
Citations 
4
1045-9219
3
PageRank 
References 
Authors
0.44
0
9
Name
Order
Citations
PageRank
Nannan Zhao152.22
Vasily Tarasov219918.98
Hadeel Albahar330.44
Ali Anwar411314.83
Lukas Rupprecht56010.88
Dimitrios Skourtis672.30
Arnab Kumar Paul7122.73
Keren Chen830.44
Ali R Butt921017.36