Capacity-Driven Scaling Schedules Derivation for Coordinated Elasticity of Containers and Virtual Machines

Yesika M. Ramirez, Vladimir Podolskiy, Michael Gerndt

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

11 Scopus citations

Abstract

With the growing complexity of microservice applications and proliferation of containers, scaling of cloud applications became challenging. Containers enabled the adaptation of the application capacity to the changing workload on the finer level of granularity than it was possible only with virtual machines. The common way to automate the adaptation of a cloud application is via autoscaling. Autoscaling is provided both on the level of virtual machines and containers. Its accuracy on dynamic workloads suffers significantly from the reactive nature of the available autoscaling solutions. The aim of the paper is to explore potential improvements of autoscaling by designing and evaluating several predictive-based autoscaling policies. These policies are naive (used as a baseline), best resource pair, only-Delta-load, always-resize, resize when beneficial. The scaling policies were implemented in Scaling Policy Derivation Tool (SPDT). SPDT takes the long-term forecast of the workload and the capacity model of microservices as input to produce the sequence of scaling actions scheduled for the execution in future with the aims to meet the service level objectives and minimize the costs. Policies implemented in SPDT were evaluated for three microservice applications and several workload patterns. The tests demonstrate that the combination of horizontal and vertical scaling enables more flexibility and reduces costs. Schedule derivation according to some policies might be compute-intensive, therefore careful consideration of the optimization objective (e.g. cost minimization or timeliness of the scaling policy) is required from the user of SPDT.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Autonomic Computing, ICAC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages177-186
Number of pages10
ISBN (Electronic)9781728124117
DOIs
StatePublished - Jun 2019
Event16th IEEE International Conference on Autonomic Computing, ICAC 2019 - Umea, Sweden
Duration: 16 Jun 201920 Jun 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Autonomic Computing, ICAC 2019

Conference

Conference16th IEEE International Conference on Autonomic Computing, ICAC 2019
Country/TerritorySweden
CityUmea
Period16/06/1920/06/19

Keywords

  • Cloud Computing
  • Containers
  • Microservices
  • Predictive Autoscaling
  • Scaling Policies
  • Self-Adaptive Cloud

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