A multicore periodical preemption virtual machine scheduling scheme to improve the performance of computational tasks

  • Authors:

  • Chao Yu;Leihua Qin;Jingli Zhou

  • Affiliations:

  • Wuhan National Laboratory for Optoelectronics, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 430074;Wuhan National Laboratory for Optoelectronics, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 430074;Wuhan National Laboratory for Optoelectronics, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 430074

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2014

Quantified Score

Hi-index 0.00

Visualization

Abstract

In virtualized environments, the VMM (virtual machine monitor) scheduler is critical to overall performance, as it allocates the physical resources. However, traditional schedulers have poor I/O performance of mixed workloads. Although recent research significantly improves I/O performance, they degrade the performance of computational tasks by shortening time slices and reducing cache efficiency. In order to eliminate these problems while guaranteeing I/O performance, this paper presents a multicore periodical preemption scheduling scheme with three optimization techniques: (1)聽periodically coalescing and handling I/O events to reduce the preemption rate and scheduling latency, which guarantees I/O performance; (2)聽taking advantage of multicore environments and centrally handling I/O events on different cores in a Round-Robin manner to lengthen time slices, which improves the performance of computational tasks; (3)聽using a dedicated priority for I/O event handling to keep the CPU fairness. We implement a Xen-based prototype and evaluate the performance of I/O workloads and computation-intensive workloads. The experimental results demonstrate that our scheduling scheme efficiently lengthens time slices and improves the performance of computational tasks, achieving the same I/O performance as the existing approaches optimized for聽I/O.