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Open Access

A Multi-Objective Optimization Method of Initial Virtual Machine Fault-Tolerant Placement for Star Topological Data Centers of Cloud Systems

School of Software Engineering, Tongji University, Shanghai 201804, China.
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Abstract

Virtualization is the most important technology in the unified resource layer of cloud computing systems. Static placement and dynamic management are two types of Virtual Machine (VM) management methods. VM dynamic management is based on the structure of the initial VM placement, and this initial structure will affect the efficiency of VM dynamic management. When a VM fails, cloud applications deployed on the faulty VM will crash if fault tolerance is not considered. In this study, a model of initial VM fault-tolerant placement for star topological data centers of cloud systems is built on the basis of multiple factors, including the service-level agreement violation rate, resource remaining rate, power consumption rate, failure rate, and fault tolerance cost. Then, a heuristic ant colony algorithm is proposed to solve the model. The service-providing VMs are placed by the ant colony algorithms, and the redundant VMs are placed by the conventional heuristic algorithms. The experimental results obtained from the simulation, real cluster, and fault injection experiments show that the proposed method can achieve better VM fault-tolerant placement solution than that of the traditional first fit or best fit descending method.

Keywords

cloud computingvirtual machine placementfault tolerancemulti-objective optimizationheuristic ant colony algorithm

References

[1]
I. Foster, Y. Zhao, I. Raicu, and S. Y. Lu, Cloud computing and grid computing 360-degree compared, in Proc. 2008 Grid Computing Environments Workshop, Austin, TX, USA, 2008, pp. 1-10.
Crossref
[2]
H. L. Chen, A qualitative and quantitative study on availability of cloud computing, http://www.valleytalk.org/wp-content/uploads/2013/10/, 2013.
[3]
M. Nelson, B. H. Lim, and G. Hutchins, Fast transparent migration for virtual machines, in Proc. 2005 USENIX Annu. Technical Conf., Anaheim, CA, USA, 2005, pp. 391-394.
[4]
M. Lee, A. Krishnakumar, P. Krishnan, N. Singh, and S. Yajnik, Hypervisor-assisted application checkpointing in virtualized environments, in Proc. IEEE/IFIP 41st Int. Conf. Dependable Systems & Networks, Hong Kong, China, 2011, pp. 371-382.
Crossref
[5]
X. Zhang, Z. G. Huo, J. Ma, and D. Meng, Fast and live whole-system migration of virtual machines, (in Chinese), Journal of Computer Research and Development, vol. 49, no. 3, pp. 661-668, 2012.
Google Scholar
[6]
F. Xu, F. M. Liu, L. H. Liu, H. Jin, B. Li, and B. C. Li, iAware: Making live migration of virtual machines interference-aware in the cloud, IEEE Transactions on Computers, vol. 63, no.12, pp. 3012-3025, 2014.
Crossref Google Scholar
[7]
K. J. Ye, Z. H. Wu, C. Wang, B. B. Zhou, W. S. Si, X. H. Jiang, and A. Y. Zomaya, Profiling-based workload consolidation and migration in virtualized data centers, IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 3, pp. 878-890, 2015.
Crossref Google Scholar
[8]
H. K. Liu and B. S. He, VMbuddies: Coordinating live migration of multi-tier applications in cloud environments, IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 4, pp. 1192-1205, 2015.
Crossref Google Scholar
[9]
J. Zhu, W. Dong, Z. F. Jiang, X. G. Shi, Z. Xiao, and X. M. Li, Improving the performance of hypervisor-based fault tolerance, in Proc. IEEE Int. Symp. Parallel & Distributed Processing, Atlanta, GA, USA, 2010, pp. 1-10.
Crossref
[10]
J. Zhu, Z. F. Jiang, Z. Xiao, and X. M. Li, Optimizing the performance of virtual machine synchronization for fault tolerance, IEEE Transactions on Computers, vol. 60, no. 12, pp. 1718-1729, 2011.
Crossref Google Scholar
[11]
D. Shen, J. Z. Luo, F. Dong, and J. X. Zhang, VirtCo: Joint coflow scheduling and virtual machine placement in cloud data centers, Tsinghua Science and Technology, vol. 24, no. 5, pp. 630-644, 2019.
Crossref Google Scholar
[12]
Z. Liu, S. J. Sun, J. Xing, Z. Fu, X. H. Hu, J. W. Pi, X. F. Yang, Y. S. Lu, and J. Li, MN-SLA: A modular networking SLA framework for cloud management system, Tsinghua Science and Technology, vol. 23, no. 6, pp. 635-644, 2018.
Crossref Google Scholar
[13]
Q. Li, Q. F. Hao, L. M. Xiao, and Z. J. Li, Adaptive management and multi-objective optimization for virtual machine placement in cloud computing, (in Chinese), Chinese Journal of Computers, vol. 34, no. 12, pp. 2253-2264, 2011.
Crossref Google Scholar
[14]
K. Tsakalozos, M. Roussopoulos, and A. Delis, Hint-based execution of workloads in clouds with Nefeli, IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 7, pp. 1331-1340, 2013.
Crossref Google Scholar
[15]
F. Farahnakian, A. Ashraf, T. Pahikkala, P. Liljeberg, J. Plosila, I. Porres, and H. Tenhunen, Using ant colony system to consolidate VMs for green cloud computing, IEEE Transactions on Services Computing, vol. 8, no. 2, pp. 187-198, 2015.
Crossref Google Scholar
[16]
E. G. Coffman, M. R. Garey, and D. S. Johnson, Approximation algorithms for bin packing: A survey, in Approximation Algorithms for NP-Hard Problems. Boston, MA, USA: PWS Publishing, 1997, pp. 46-93.
[17]
N. Bobroff, A. Kochut, and K. Beaty, Dynamic placement of virtual machines for managing SLA violations, in Proc. 10th IFIP/IEEE Int. Symp. Integrated Management, Munich, Germany, 2007, pp. 119-128.
Crossref
[18]
S. Chaisiri, B. S. Lee, and D. Niyato, Optimization of resource provisioning cost in cloud computing, IEEE Transactions on Services Computing, vol. 5, no. 2, pp. 164-177, 2012.
Crossref Google Scholar
[19]
C. H. Lien, Y. W. Bai, and M. B. Lin, Estimation by software for the power consumption of streaming-media servers, IEEE Transactions on Instrumentation and Measurement, vol. 56, no. 5, pp. 1859-1870, 2007.
Crossref Google Scholar
[20]
A. Verma, G. Dasgupta, T. K. Nayak, P. De, and R. Kothari, Server workload analysis for power minimization using consolidation, in Proc. 2009 Conf. USENIX Annu. Technical Conf., San Diego, CA, USA, 2009, p. 28.
[21]
J. K. Dong, H. B. Wang, and S. D. Cheng, Energy-performance tradeoffs in IaaS cloud with virtual machine scheduling, China Communications, vol. 12, no. 2, pp. 155-166, 2015.
Crossref Google Scholar
[22]
X. Jing and J. A. B. Fortes, Multi-objective virtual machine placement in virtualized data center environments, in Proc. 2010 IEEE/ACM Int’l Conf. Green Computing and Communications & Int’l Conf. Cyber, Physical and Social Computing, Hangzhou, China, 2010, pp. 179-188.
[23]
F. Ma, F. Liu, and Z. Liu, Multi-objective optimization for initial virtual machine placement in cloud data center, Journal of Information and Computational Science, vol. 9, no. 16, pp. 5029-5038, 2012.
Google Scholar
[24]
S. N. Wang, H. X. Gu, and G. Wu, A new approach to multi-objective virtual machine placement in virtualized data center, in Proc. IEEE 8th Int. Conf. Networking, Architecture and Storage, Xi’an, China, 2013, pp. 331-335.
Crossref
[25]
F. Machida, M. Kawato, and Y. Maeno, Redundant virtual machine placement for fault-tolerant consolidated server clusters, in Proc. 2010 IEEE Network Operations and Management Symposium, Osaka, Japan, 2010, pp. 32-39.
Crossref
[26]
G. Y. Jung, K. R. Joshi, M. A. Hiltunen, R. D. Schlichting, and C. Pu, Performance and availability aware regeneration for cloud based multitier applications, in Proc. IEEE/IFIP Int. Conf. Dependable Systems and Networks, Chicago, IL, USA, 2010, pp. 497-506.
Crossref
[27]
E. Bin, O. Biran, O. Boni, E. Hadad, E. K. Kolodner, Y. Moatti, and D. H. Lorenz, Guaranteeing high availability goals for virtual machine placement, in Proc. 31st Int. Conf. Distributed Computing Systems, Minneapolis, MN, USA, 2011, pp. 700-709.
Crossref
[28]
D. Epping and F. Denneman, VMware vSphere 4.1 HA and DRS Technical Deepdive. North Charleston, SC, USA: CreateSpace, 2010, pp. 15-22.
[29]
Z. B. Zhen, T. C. Zhou, M. R. Lyu, and I. King, FTCloud: A component ranking framework for fault-tolerant cloud applications, in Proc. IEEE 21st Int. Symp. Software Reliability Engineering, San Jose, CA, USA, 2010, pp. 398-407.
Crossref
[30]
Z. B. Zheng, T. C. Zhou, M. R. Lyu, and I. King, Component ranking for fault-tolerant cloud applications, IEEE Transactions on Services Computing, vol. 5, no. 4, pp. 540-550, 2012.
Crossref Google Scholar
[31]
F. Hermenier, J. Lawall, and G. Muller, BtrPlace: A flexible consolidation manager for highly available applications, IEEE Transactions on Dependable and Secure Computing, vol. 10, no. 5, pp. 273-286, 2013.
Crossref Google Scholar
[32]
X. X. Cui, Multi-Objective Evolutionary Algorithms and Their Applications, (in Chinese). Beijing, China: National Defense Industry Press, 2006, pp. 10-20.
[33]
T. Stützle and H. H. Hoos, MAX-MIN ant system, Future Generation Computer Systems, vol. 16, no. 8, pp. 889-914, 2000.
Crossref Google Scholar
[34]
R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software: Practice and Experience, vol. 41, no.1, pp. 23-50, 2011.
Crossref Google Scholar
[35]
E. Cecchet, A. Chanda, S. Elnikety, J. Marguerite, and W. Zwaenepoel, Performance comparison of middleware architectures for generating dynamic web content, in Proc. 2003 ACM/IFIP/USENIX Int. Conf. Middleware, Rio de Janeiro, Brazil, 2003, pp. 242-261.
Crossref
[36]
Y. Tamura, Kemari: Fault tolerant VM synchronization based on KVM, https://www.linux-kvm.org/images/0/0d/0.5.kemari-kvm-forum-2010.pdf, 2010.
[37]
A. Jin and J. H. Jiang, Fault injection scheme for embedded systems at machine code level and verification, in Proc. 15th IEEE Pacific Rim Int. Symp. Dependable Computing, Shanghai, China, 2009, pp. 55-62.
Crossref
[38]
J. W. Hu and J. H. Jiang, Design and implementation of a fault injection mechanism for software reliability evaluation, (in Chinese), Journal of Computer-Aided Design & Computer Graphics, vol. 24, no. 6, pp. 741-751, 2012.
Google Scholar
[39]
D. Q. Zhang, J. H. Jiang, and L. B. Chen, A method for validating the effectiveness of fault clustering and failure clustering of programs, Scientia Sinica Informationis, vol. 44, no. 10, pp. 1323-1344, 2014.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 26 Issue 1,
February 2021
Pages 95-111
DOI: 10.26599/TST.2019.9010044
Cite this article:
Zhang W, Chen X, Jiang J. A Multi-Objective Optimization Method of Initial Virtual Machine Fault-Tolerant Placement for Star Topological Data Centers of Cloud Systems. Tsinghua Science and Technology, 2021, 26(1): 95-111. https://doi.org/10.26599/TST.2019.9010044

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Received: 27 June 2019
Accepted: 28 August 2019
Published: 19 June 2020
© The author(s) 2021.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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