mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
An Experimental Performance Evaluation of Autoscaling Algorithms for Complex Workflows
Delft University of Technology, Netherlands.
Umeå University, Sweden.
University of Würzburg, Germany.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1364-8127
Show others and affiliations
2017 (English)In: ICPE '17 Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, 2017, 75-86 p.Conference paper (Refereed)
Abstract [en]

Simplifying the task of resource management and scheduling for customers, while still delivering complex Quality-of-Service (QoS), is key to cloud computing. Many autoscaling policies have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application utilizing cloud elasticity features. However, in prior work, when a new policy is proposed, it is seldom compared to the state-of-the-art, and is often compared only to static provisioning using a predefined QoS target. This reduces the ability of cloud customers and of cloud operators to choose and deploy an autoscaling policy. In our work, we conduct an experimentalperformance evaluation of autoscaling policies, using as application model workflows, a commonly used formalism for automating resource management for applications with well-defined yet complex structure. We present a detailed comparative study of general state-of-the-art autoscaling policies, along with two new workflow-specific policies. To understand the performance differences between the 7 policies, we conduct various forms of pairwise and group comparisons. We report both individual and aggregated metrics. Our results highlight the trade-offs between the suggested policies, and thus enable a better understanding of the current state-of-the-art.

Place, publisher, year, edition, pages
2017. 75-86 p.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:mdh:diva-35688DOI: 10.1145/3030207.3030214ISBN: 978-1-4503-4404-3 OAI: oai:DiVA.org:mdh-35688DiVA: diva2:1108306
Conference
ICPE '17 Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, L'Aquila, Italy — April 22-26, 2017
Projects
Future factories in the Cloud
Available from: 2017-06-12 Created: 2017-06-12 Last updated: 2017-06-12Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Papadopoulos, Alessandro
By organisation
Embedded Systems
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 1 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf