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

作者：Li, Ying^*; Zhang, Jing; Chen, XiaoJun; Li, JunHuai; Ding, JuLan

来源：Cluster Computing, 2018, 21(2): 1395-1410.

DOI：10.1007/s10586-017-1344-z

• Cloud computing
• Virtualization
• Load monitoring
• Load assessment
• Resource adjustment
• Resource scheduling

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 showthat 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.

• 出版日期2018-6
• 单位西安理工大学