Cloud Resource Management with Turnaround Time Driven Auto-Scaling

Xiao Long Liu, Shyan-Ming Yuan*, Guo Heng Luo, Hao Yu Huang, Paolo Bellavista

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Cloud resource management research and techniques have received relevant attention in the last years. In particular, recently numerous studies have focused on determining the relationship between server-side system information and performance experience for reducing resource wastage. However, the genuine experiences of clients cannot be readily understood only by using the collected server-side information. In this paper, a cloud resource management framework with two novel turnaround time driven auto-scaling mechanisms is proposed for ensuring the stability of service performance. In the first mechanism, turnaround time monitors are deployed in the client-side instead of the more traditional server-side, and the information collected outside the server is used for driving a dynamic auto-scaling operation. In the second mechanism, a schedule-based auto scaling preconfiguration maker is designed to test and identify the amount of resources required in the cloud. The reported experimental results demonstrate that using our original framework for cloud resource management, stable service quality can be ensured and, moreover, a certain amount of quality variation can be handled in order to allow the stability of the service performance to be increased.

Original languageEnglish
Article number7935490
Pages (from-to)9831-9841
Number of pages11
JournalIEEE Access
StatePublished - 29 May 2017


  • big data
  • Network
  • resource management
  • service management
  • turnaround time


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