Evaluation of cloud autoscaling strategies under different incoming workload patterns

Daniele Tessera, Luisa Massari, Maria Carla Calzarossa

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Cloud computing provides cost-effective solutions for deploying services and applications. Although resources can be provisioned on demand, they need to adapt quickly and in a seamless way to the workload intensity and characteristics and satisfy at the same time the desired performance levels. In this paper, we evaluate the effects exercised by different incoming workload patterns on cloud autoscaling strategies. More specifically, we focus on workloads characterized by periodic, continuously growing, diurnal and unpredictable arrival patterns. To test these workloads, we simulate a realistic cloud infrastructure using customized extensions of the CloudSim simulation toolkit. The simulation experiments allow us to evaluate the cloud performance under different workload conditions and assess the benefits of autoscaling policies as well as the effects of their configuration settings.
Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalCONCURRENCY AND COMPUTATION
DOIs
Publication statusPublished - 2020

Keywords

  • CloudSim
  • autoscaling policies
  • cloud computing
  • resource management
  • workload characterization
  • workload patterns

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