Online Virtual Machine Allocation with Lifetime and Load Predictions

Niv Buchbinder, Yaron Fairstein, Konstantina Mellou, Ishai Menache, Joseph (seffi) Naor

Research output: Contribution to journalArticlepeer-review


The cloud computing industry has grown rapidly over the last decade, and with this growth there is a significant increase in demand for compute resources. Demand is manifested in the form of Virtual Machine (VM) requests, which need to be assigned to physical machines in a way that minimizes resource fragmentation and efficiently utilizes the available machines. This problem can be modeled as a dynamic version of the bin packing problem with the objective of minimizing the total usage time of the bins (physical machines). Motivated by advances in Machine Learning that provide good estimates of workload characteristics, this paper studies the effect of having extra information about future (total) demand. We show that the competitive factor can be dramatically improved with this additional information; in some cases, we achieve constant competitiveness, or even a competitive factor that approaches 1. Along the way, we design new offline algorithms with improved approximation ratios for the dynamic bin-packing problem.

Original languageEnglish
Pages (from-to)9-10
Number of pages2
JournalPerformance Evaluation Review
Issue number1
StatePublished - Jun 2021


  • cloud computing
  • dynamic bin packing
  • virtual machine scheduling

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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