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Rapid Testing of IaaS Resource Management Algorithms via Cloud Middleware Simulation
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2018 (English)In: Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering, ACM Digital Library, 2018, p. 184-191Conference paper, Published paper (Refereed)
Abstract [en]

Infrastructure as a Service (IaaS) Cloud services allow users to deploy distributed applications in a virtualized environment without having to customize their applications to a specific Platform as a Service (PaaS) stack. It is common practice to host multiple Virtual Machines (VMs) on the same server to save resources. Traditionally, IaaS data center management required manual effort for optimization, e.g. by consolidating VM placement based on changes in usage patterns. Many resource management algorithms and frameworks have been developed to automate this process. Resource management algorithms are typically tested via experimentation or using simulation. The main drawback of both approaches is the high effort required to conduct the testing. Existing Cloud or IaaS simulators require the algorithm engineer to reimplement their algorithm against the simulator's API. Furthermore, the engineer manually needs to define the workload model used for algorithm testing. We propose an approach for the simulative analysis of IaaS Cloud infrastructure that allows algorithm engineers and data center operators to evaluate optimization algorithms without investing additional effort to reimplement them in a simulation environment. By leveraging runtime monitoring data, we automatically construct the simulation models used to test the algorithms. Our validation shows that algorithm tests conducted using our IaaS Cloud simulator match the measured behavior on actual hardware.

Place, publisher, year, edition, pages
ACM Digital Library, 2018. p. 184-191
Keywords [en]
IaaS middleware simulation, cloud simulation, performance model extraction, performance simulation, power consumption prediction, simulation-based testing of resource management algorithms
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-146655DOI: 10.1145/3184407.3184428OAI: oai:DiVA.org:umu-146655DiVA, id: diva2:1197958
Conference
9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018), Berlin, Germany, April 9–13, 2018
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-06-09Bibliographically approved

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Krzywda, Jakub

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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