Online VM Auto-Scaling Algorithms for Application Hosting in a Cloud

Yang Guo, Alexander L. Stolyar, Anwar Walid

Research output: Contribution to journalArticlepeer-review


We consider the auto-scaling problem for application hosting in a cloud, where applications are elastic and the number of requests changes over time. The application requests are serviced by Virtual Machines (VMs), which reside on Physical Machines (PMs) in a cloud. We aim to minimize the number of hosting PMs by intelligently packing VMs into PMs, while the VMs are auto-scaled, i.e., dynamically acquired and released, to accommodate varying application needs. We consider a shadow routing based approach for this problem. The proposed shadow algorithm employs a specially constructed virtual queueing system to dynamically produce an optimal solution that guides the VM auto-scaling and the VM-To-PM packing. The proposed algorithm runs continuously without the need to re-solve the underlying optimization problem 'from scratch', and adapts automatically to the changes in the application demands. We prove the asymptotic optimality of the shadow algorithm. The simulation experiments further demonstrate the algorithm's good performance and high adaptivity.

Original languageEnglish (US)
Article number8351912
Pages (from-to)889-898
Number of pages10
JournalIEEE Transactions on Cloud Computing
Issue number3
StatePublished - Jul 1 2020


  • Cloud computing
  • auto-scaling
  • data centers
  • dynamic stochastic bin packing
  • online algorithms
  • virtual machine

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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