Virtual Machine Scheduling Strategy for Reliable Service Resource Supply in Cloud Environment

Virtual Machine Scheduling Strategy for Reliable Service Resource Supply in Cloud Environment

Dan Liu Xin Sui Yan Zeng Guangcai Cui Xu Di Li Li*

Changchun University of Science and Technology, College of Computer Science and Technology, Changchun 130022, China

Jilin Provincial Institute of Education, Jilin 130022, China

Corresponding Author Email: 
ll@cust.edu.cn
Page: 
450-462
|
DOI: 
https://doi.org/10.18280/ama_b.600214
Received: 
8 June 2017
|
Accepted: 
15 June 2017
|
Published: 
30 June 2017
| Citation

OPEN ACCESS

Abstract: 

Nowadays, most researchers carry out such unilateral research on virtual machine scheduling strategy, energy consumption. And there are few jobs which less concern about the impact of multiple factors. Due to heterogeneous cloud environment is made up of thousands of resource nodes. The resource utilization, virtual machine scheduling and other indicators must be related with uncertainties arising from node failure events. The paper studies the virtual machine scheduling strategy under the supply of reliable resources. And a dynamic-reliable resource supply FR strategy is proposed. In this paper, the Cloud Sim simulation cloud platform was used to simulate the experiment and compare with classical IQR/LR scheduling algorithm during simulation experiment. And experimental results show that FR strategy can effectively improve CPU utilization and is conducive to data center energy consumption reduction.

Keywords: 

Heterogeneous environment, Service quality, Dynamic supply strategy, Resource utilization

1. Introduction
2. Overview of Cloud Platform
3. Failure Pattern of Cloud Service System
4. Reliability Improvement of Cloud Service System
5. VM Placement Optimization
6. Strategy of Reliable Resource Supply
7. Simulation Experiments
8. Conclusion
Acknowledgment
  References

[1] W.B. Zhao, P. Melliar-Smith, and L. Moser, Fault tolerance middleware for cloud computing, 2010, Proceedings of IEEE 3rd Int’l Conf, Cloud Computing.

[2] A. Khajeh-Hosseini, I. Sommerville, I. Sriram, Research challenges for enterprise cloud computing, 2010.

[3] China Information and Communication Research Institute, Cloud computing white paper 2016, 2016, China Information and Communication Research Institute.

[4] J.K. Dong, X. Jin, H.B. Wang, Y.Y. Li, P. Zhang, S.D. Cheng, Energy-saving virtual machine placement in cloud data centers, 2013, IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[5] X.F. Liao, H. Jin, H.K. Liu, Towards a green cluster through dynamic remappingof virtual machines, 2012, Future Generation Computer Systems.

[6] R.K. Sahoo, A. Sivasubramaniam, M.S. Squillante, Y.Y. zhang, Failure data analysis of a large-scale hetcrogencous server environment, 2004, Proceedings of the DSN 2004.

[7] B. Schroeder, G.A. Gibson, A large-scale study of failures in high-performance computing systems//Proceedings of DSN2006.2006.

[8] T. Heath, R.P. Martin, T.D. Nguyen, Improving cluster availability using workstation validation, 2002, Proceedings of the ACM SIGMETRICS, Marina Del Rey.

[9] X.B. Fan, W Weber, L.A. Barroso, Power Provisioning for a Warehouse-sized Computer, 2007, ACM SIGARCH Computer Architecture News.

[10] A. Beloglazov, J. Abawajy, R. Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing, 2012, Future Generation Computer Systems.

[11] L.G. He, D.Q. Zou, Z. Zhang, H. Jin, K. Yang, S.A. Jarvis, Optimizing resource consumptions in clouds, 2011, In: Proc, of the 12th IEEE/ACM Int’l Conf. on Grid Computing.