Cluster Computing

, Volume 21, Issue 2, pp 1395–1410 | Cite as

An accurate resource scheduling system for virtual machines based on CPU load monitoring and assessment

  • Ying LiEmail author
  • Jing Zhang
  • XiaoJun Chen
  • JunHuai Li
  • JuLan Ding


An accurate resource scheduling system (RSS) for virtual machines based on CPU monitoring and load assessment is presented to solve the shortcoming of resource scheduling in cloud computing systems. A new architecture is designed to improve Credit scheduler, including three core components: CPU load monitoring component (CLMA), CPU load assessment component (CLAA), and the resource adjustment component (RSA). On the basis of the prototype design, we make an evaluation between Credit scheduler and our system with a typical example in Xen platform. The experimental results show that the proposed system could satisfy the personalized resources requirements from users with higher tasks resource utilization and lower system resource utilization when compared with Credit scheduler. RSS has a strong sensitivity to meet the requirements of cloud computing systems, since it can accelerate the executions of applications via dynamic resource scheduling.


Cloud computing Virtualization Load monitoring Load assessment Resource adjustment Resource scheduling 


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© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Automation and Information EngineeringXi’an University of TechnologyXi’anChina
  2. 2.School of Computer Science and EngineeringXi’an University of TechnologyXi’anChina

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