SRVC: An Efficient Scheduler for Concurrent Virtual Machines over the Xen Hypervisor

Po-Jen Chuang; Chih-Yuan Chou

doi:10.6180/jase.2017.20.3.10

SRVC: An Efficient Scheduler for Concurrent Virtual Machines over the Xen Hypervisor

Electrical Engineering Computer Science and Information Engineering

Po-Jen Chuang This email address is being protected from spambots. You need JavaScript enabled to view it.¹ and Chih-Yuan Chou¹

¹Department of Electrical and Computer Engineering, Tamkang University, Tamsui, Taiwan 25137, R.O.C.

Received: June 12, 2016
Accepted: April 28, 2017
Publication Date: September 1, 2017

Download Citation: ||https://doi.org/10.6180/jase.2017.20.3.10

ABSTRACT

A CPU scheduler can support virtualization practices by distributing physical resources to form virtual machines (VMs). To facilitate concurrent applications in virtualization platforms, this paper presents an SRVC scheduler which is a Scheduler involving the R-B Tree structure, the Virtual runtime of the CFS mechanism and a Concurrent waiting queue. The SRVC scheduler can practically cut down the overall runtime by using the R-B Tree structure to shorten the virtual CPU (VCPU) lookup time and attain efficient task scheduling. It meanwhile applies the CFS mechanism to reduce the gap between concurrent and non-concurrent VMs and obtain balanced CPU time distribution and resource utilization. To promote synchronization on concurrent VCPUs and scheduling, SRVC takes in a concurrent waiting queue to handily pick up marked VCPUs for arranged execution. As simulation results demonstrate, SRVC needs constantly less runtime than existing Credit and Turbo schedulers due to its ability to reduce the runtime in both concurrent and non-concurrent applications.

Keywords: Virtualization, The Xen Hypervisor, Concurrent Virtual Machines, Scheduling, Performance Evaluation

REFERENCES

[1] Virtualization, http://en.wikipedia.org/wiki/Virtualization.
[2] Virtualization Spectrum, http://wiki.xen.org/wiki/ Virtualization_Spectrum
[3] Chisnall, D., The Definitive Guide to the Xen Hypervisor, Prentice Hall, Nov. (2007).
[4] VMware Virtualization Software, http://www.vmware. com/tw.
[5] Kernel Based Virtual Machine, http://www.linuxkvm.org.
[6] Credit Scheduler, http://wiki.xensource.com/wiki/ Credit_Scheduler.
[7] Tseng, C. Y., Lin, S. C., Chen, L. C. and Chung, H. K., “The Performance Improvement and Evaluation of an Enhanced CPU Scheduler in Virtualized Environment,” Proc. 2011 Int’l Conf. on e-Commerce, e-Administration, e-Society, e-Education, and e-Technology, pp. 31073123, Jan. (2011).
[8] Tseng, C. Y. and Liu, K. Y., “A Modified Priority Based CPU Scheduling Scheme for Virtualized Environment,” International Journal of Hybrid Information Technology, Vol. 6, No. 2 (2013).
[9] Haiyang, Z. and Qiaoyu, L., “Red-black Tree Used for Arranging Virtual Memory Area of Linux,” Proc. 2010 Int’l Conf. On Management and Service Science, pp. 13, Aug. (2010). doi: 10.1109/ICMSS.2010.5575666
[10] Hoong, P. K. and Shyang, O. Y., “Red-black Tree Architecture for P2P Media Streaming,” Proc. 2013 IEEE Region 10 Conference, pp. 14, Oct. (2013). doi: 10.1109/TENCON.2013.6719004
[11] Molnar, I., Scheduler Core and Completely Fair Scheduler (CFS), http://lwn.net/Articles/230501/.
[12] Cherkasova, L., Gupta, D. and Vahdat, A., “Comparison of the Three CPU Schedulers in Xen,” ACM SIGMETRICS Performance Evaluation Review, pp. 4251, Sep (2007).
[13] Coffman, E. G. and Graham, R. L., “Optimal Preemptive Scheduling on Two-processor Systems,” IEEE Trans. on Computers, Vol. 18, No. 11, pp. 10141020 (1969). doi: 10.1109/T-C.1969.222573
[14] NAS Parallel Benchmarks, http://www.nas.nasa.gov/ publications/npb.html. doi: 10.1007/978-0-387-09766- 4_133
[15] Jin, H., Frumkin, M. and Yan, J., “The Open MP Implementation of NAS Parallel Benchmarks and Its Performance,” NAS Technical Report, NAS-99-011, Oct. (1999).
[16] Cheng, K., Bai, Y., Wang, R. and Ma, Y., “Optimizing Soft Real-time Scheduling Performance for Virtual Machines with SRT-Xen,” Proc. 2015 15th IEEE/ ACM Int’l Symp. on Cluster, Cloud and Grid Computing, May, pp. 169178 (2015). doi: 10.1109/CCGrid. 2015.52
[17] Liu, X. and Wang, M. “A Workload-aware Resources Scheduling Method for Virtual Machine,” International Journal of Grid and Distributed Computing, Vol. 8, No. 1, pp. 247258 (2015). doi: 10.14257/ ijgdc.2015.8.1.23
[18] Tan, H., Li, C., He, Z., Li, K. and Hwang, K., “VMCD: a Virtual Multi-Channel Disk I/O Scheduling Method for Virtual Machines,” IEEE Trans. on Services Computing, Vol. 9, No. 6 (2016). doi: 10.1109/TSC.2015. 2436388
[19] Zhang, W. Z., Xie, H. C. and Hsu, C. H., “Automatic Memory Control of Multiple Virtual Machines on a Consolidated Server,” IEEE Trans. on Cloud Computing, Vol. 5, No. 1 (2017). doi: 10.1109/TCC.2014. 2378794 M