Improving the MXFT Scheduling Algorithm for a Cloud Computing Context

Paul Moggridge, Na Helian, Yi Sun, Mariana Lilley, Vito Veneziano, Martin Eaves

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In this paper, the Max-Min Fast Track (MXFT) scheduling algorithm is improved and compared against a selection of popular algorithms. The improved versions of MXFT are called Min-Min Max-Min Fast Track (MMMXFT) and Clustering Min-Min Max-Min Fast Track (CMMMXFT). The key difference is using Min-Min for the fast track. Experimentation revealed that despite Min-Min’s characteristic of prioritising small tasks at the expense of overall makespan, the overall makespan was not adversely affected and the benefits of prioritising small tasks were identified in MMMXFT. Experiments were conducted by using a simulator with the exception of one real-world experiment. The real-world experiment identified challenges faced by algorithms which rely on accurate execution time prediction.

Original languageEnglish
Pages (from-to)618 - 638
Number of pages21
JournalInternational Journal of Grid and Utility Computing (IJGUC)
Issue number6
Early online date9 Aug 2019
Publication statusE-pub ahead of print - 9 Aug 2019


  • Cloud computing
  • Max-min
  • Scheduling algorithms


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