Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 109Cloud Computing and Resource ManagementSoftware System Performance and ReliabilityDistributed systems and fault tolerance

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Container Orchestration Engines: A Thorough Functional and Performance Comparison

Jawarneh, Isam Mashhour Al, Bellavista, Paolo, Bosi, Filippo, Foschini, Luca, Martuscelli, Giuseppe, Montanari, Rebecca, Palopoli, Amedeo·2019·ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

Abstract

In the last decade, novel software architectural patterns, such as microservices, have emerged to improve application modularity and to streamline their development, testing, scaling, and component replacement. To support these new trends, new practices as DevOps methodologies and tools, promoting better cooperation between software development and operations teams, have emerged to support automation and monitoring throughout the whole software construction lifecycle. That affected positively several IT companies, but also helped the transition to the softwarization of complex telco infrastructures in the last years. Container-based technologies played a crucial role by enabling microservice fast deployment and their scalability at low overhead; however, modern container-based applications may easily consist of hundreds of microservices services with complex interdependencies and call for advanced orchestration capabilities. While there are several emerging container orchestration engines, such as Docker Swarm, Kubernetes, Apache Mesos, and Cattle, a thorough functional and performance assessment to help IT managers in the selection of the most appropriate orchestration solution is still missing. This paper aims to fill that gap. Collected experimental results show that Kubernetes outperforms its counterparts for very complex application deployments, while other engines can be a better choice for simpler deployments.

Overview

Integrity signals

Registry

Crossref · 10.1109/icc.2019.8762053

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 11 references

Research works printed but not linked: 2

  1. isenberg, 0, Container Orchestration Wars, Velocity 2016
  2. rouse, 2016, What is Docker Swarm?, TechTarget

Cited by

See all 109

Metadata

Authors
Jawarneh, I. M. A., Bellavista, P., Bosi, F., Foschini, L., Martuscelli, G., Montanari, R., & Palopoli, A.
Affiliations
University of Bologna (IT)
Venue
ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
Publisher
IEEE
Published
2019-05
Pages
1-6
ISSN
1550-3607 (ISSN-L)
OpenAlex
W2960287963
Cited by
109
References
11
Citations by year
2026: 3 · 2025: 13 · 2024: 14 · 2023: 25 · 2022: 27 · 2021: 15 · 2020: 12
FWCI
10.75
Type
conference paper
Language
en
Open access
closed
Locations
  • International Conference on Communications (publishedVersion)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
Keywords
container orchestration, Kubernetes, Docker Swarm, Apache Mesos, cattle, microservices, DevOps, performance comparison, software-defined networking

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review