Burstable Instances for Clouds: Performance Modeling, Equilibrium Analysis, and Revenue Maximization

Yuxuan Jiang, Mohammad Shahrad, David Wentzlaff, Danny H.K. Tsang, Carlee Joe-Wong

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Leading cloud providers recently introduced a new instance type named burstable instances to better match the time-varying workloads of tenants and further reduce their costs. In the research community, however, little has been done to understand burstable instances from a theoretical perspective. This paper presents the first unified framework to model, analyze, and optimize the operation of burstable instances. Specifically, we model the resource provisioning of burstable instances, identify key performance metrics, and derive the analytical performance given the resource provisioning decisions. We then characterize the equilibrium behind tenants' responses to the prices offered for different burstable instance service classes, taking into account the impact of tenants' actions on the performance achieved by each service class. In addition, we investigate how a cloud provider can leverage knowledge of this equilibrium to find the prices that maximize its total revenue. Finally, we validate our framework on real traces and demonstrate its usage to price burstable offerings in a public cloud.

Original languageEnglish (US)
Article number9178492
Pages (from-to)2489-2502
Number of pages14
JournalIEEE/ACM Transactions on Networking
Issue number6
StatePublished - Dec 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


  • Cloud
  • burstable instances
  • equilibrium
  • revenue maximization


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