Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing

Vladimir Podolskiy, Michael Mayo, Abigail Koey, Michael Gerndt, Panos Patros

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

18 Scopus citations

Abstract

With changing workloads, cloud service providers can leverage vertical container scaling (adding/removing resources) so that Service Level Objective (SLO) violations are minimized and spare resources are maximized. In this paper, we investigate a solution to the self-adaptive problem of vertical elasticity for co-located containerized applications. First, the system learns performance models that relate SLOs to workload, resource limits and service level indicators. Second, it derives limits that meet SLOs and minimize resource consumption via a combination of optimization and restricted brute-force search. Third, it vertically scales containers based on the derived limits. We evaluated our technique on a Kubernetes private cloud of 8 nodes with three deployed applications. The results registered two SLO violations out of 16 validation tests; acceptably low derivation times facilitate realistic deployment. Violations are primarily attributed to application specifics, such as garbage collection, which require further research to be circumvented.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019
PublisherIEEE Computer Society
Pages72-81
Number of pages10
ISBN (Electronic)9781728127316
DOIs
StatePublished - Jun 2019
Event13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019 - Umea, Sweden
Duration: 16 Jun 201920 Jun 2019

Publication series

NameInternational Conference on Self-Adaptive and Self-Organizing Systems, SASO
Volume2019-June
ISSN (Print)1949-3673
ISSN (Electronic)1949-3681

Conference

Conference13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019
Country/TerritorySweden
CityUmea
Period16/06/1920/06/19

Keywords

  • Autoscaling
  • Cloud Computing
  • Data Driven Adaptation
  • Performance Interference
  • Resource Sharing
  • Self Adaptive Systems

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