Abenefit-aware on-demand provisioning approach for multi-tier applications in cloud computing

doi:10.1007/s11704-013-2201-8

Front. Comput. Sci.

2013, Vol. 7

Issue (4) : 459-474 https://doi.org/10.1007/s11704-013-2201-8

RESEARCH ARTICLE

Abenefit-aware on-demand provisioning approach for multi-tier applications in cloud computing

Heng WU^1,^2,³(

), Wenbo ZHANG¹, Jianhua ZHANG^1,^2,³, Jun WEI^1,², Tao HUANG^1,²

¹. Institute of Software, Chinese Acad emy of Sciences, Beijing 100190, China
². State Key Laboratory Computer Science, Chinese Academy of Sciences, Beijing 100190, China
³. University of Chinese Academy of Sciences, Beijing 100049, China

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Abstract

Dynamic resource provisioning is a challenging technique to meet the service level agreement (SLA) requirements of multi-tier applications in virtualization-based cloud computing. Prior efforts have addressed this challenge based on either a cost-oblivious approach or a cost-aware approach. However, both approaches may suffer frequent SLA violations under flash crowd conditions. Because they ignore the benefit gained that a multi-tier application continuously guarantees the SLA in the new con figuration. In this paper, we propose a benefit-aware approach with feedback control theory to solve this problem. Experimental results based on live workload traces show that our approach can reduce resource provisioning cost by as much as 30% compared with a costoblivious approach, and can effectively reduce SLA violations compared with a cost-aware approach.

Keywords cloud computing visualization resource reconfiguration feedback control beneit-aware

Corresponding Author(s): Heng WU

Issue Date: 01 August 2013

Cite this article:

Heng WU,Wenbo ZHANG,Jianhua ZHANG, et al. Abenefit-aware on-demand provisioning approach for multi-tier applications in cloud computing[J]. Front. Comput. Sci., 2013, 7(4): 459-474.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-013-2201-8
https://academic.hep.com.cn/fcs/EN/Y2013/V7/I4/459

1	L M Vaquero, L Rodero-Merino, J Caceres, M Lindner. A break in the clouds: towards a cloud definition. ACM SIGCOMM Computer Communication Review, 2008, 39(1): 50−55 https://doi.org/10.1145/1496091.1496100
2	M Armbrust, R A Fox. a, A D Joseph, R H Katz, A Konwinski, G Lee, D A Patterson, A Rabkin, M Zaharia. Above the clouds: a Berkeley view of cloud computing. Technical Report, Dept. Electrical Eng. And Comput. Sciences, University of California, Berkeley, 2009
3	C Pettey, H Stevens. Gartner maps out the rapidly evolving market for cloud infrastructure as a service.
4	Amazon ec2. , 2013
5	Ibm smartcloud. , 2013
6	Opsource cloud.
7	M N Bennani, D A Menasce. Resource allocation for autonomic datacenters using analytic performance models. In: Proceedings of the 2nd International Conference on Autonomic Computing. 2005, 229−240
8	Q Zhang, L Cherkasova, E Smirni. A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In: Proceedings of the 4th International Conference on Autonomic Computing, ICAC’07. 2007, 17−26
9	B Urgaonkar, P Shenoy, A Chandra, P Goyal, T Wood. Agile dynamic provisioning of multi-tier internet applications. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2008, 3(1): 1 https://doi.org/10.1145/1342171.1342172
10	U Sharma, P Shenoy, S Sahu, A Shaikh. A cost-aware elasticity provisioning system for the cloud. In: Proceedings of the 31st International Conference on Distributed Computing Systems (ICDCS). 2011, 559−570
11	G Jung, K Joshi, M Hiltunen, R Schlichting, C Pu. A cost-sensiti veadaptation engine for server consolidation of multitier applications. Middleware, 2009, 163−183
12	Bubis benchmark.
13	C Pettey, H Stevens. Gartner maps out the rapidly evolving market for cloud infrastructure as a service.
14	C Clifton, G T Leavens, C Chambers, T Millstein. Multijava: modular open classes and symmetric multiple dispatch for java. In: ACM Sigplan Notices. 2000, 130−145
15	The powerful open source industry standard for virtualization.
16	C Clark, K Fraser, S Hand, J G Hansen, E Jul, C Limpach, I Pratt, A Warfield. Live migration of virtual machines. In: Proceedings of the 2nd conference on Networked Systems Design & Implementation- Volume 2. 2005, 273−286
17	J Xu, M Zhao, J Fortes, R Carpenter, M Yousif. On the use of fuzzy modeling in virtualized data center management. In: Proceedings of the 4th International Conference on Autonomic Computing, 2007. ICAC’07. 2007
18	M N Bennani, D A Menasce. Resource allocation for autonomic data centers using analytic performance models. In: Proceedings of the 2nd International Conference on Autonomic Computing, 2005. ICAC 2005. 2005, 229−240
19	T Hoff. Friendster lost lead because of a failure to scale.
20	Hepric hq.
21	Apache commons math.
22	Q Zhang, L Cherkasova, N F Mi, E Smirni. A regression-based analytic model for capacity planning of multi-tier applications. In: Proceedings of the 2008 IEEE International Conference on Cluster Computing. 2008, 197−211
23	D Krishnamurthy, J A Rolia, S Majumdar. A synthetic workload generation technique for stress testing session-based systems. IEEE Transactions on Software Engineering, 2006, 32(11): 868−882 https://doi.org/10.1109/TSE.2006.106
24	L Cherkasova, P Phaal. Session-based admission control: a mechanism for peak load management of commercial web sites. IEEE Transactions on Computers, 2002, 51(6): 669−685 https://doi.org/10.1109/TC.2002.1009151
25	Z Ming, J Yin, W Yang, H Wang, Z Xiao. A web performance testing framework and its mixed performance modeling process. Journal of Computer Research and Development, 2010, 47(7): 1192−1200
26	P Xiong, Y Chi, S Zhu, H J Moon, C Pu, H Hacigumus. Intelligent management of virtualized resources for database systems in cloud environment. In: Proceedings of the 27th IEEE International Conference on Data Engineering (ICDE). 2011, 87−98
27	A Bagnall, G Janacek. Clustering time series from arma models with clipped data. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004, 49−58
28	M Arlitt, T Jin. A workload characterization study of the 1998 world cup web site. IEEE Network, 2000, 14(3): 30−37 https://doi.org/10.1109/65.844498
29	J A Dilley. Web server workload characterization. Hewlett-Packard Laboratories, Technical Publications Department, 1996
30	J Rolia, L Cherkasova, C McCarthy. Configuring workload manager control parameters for resource pools. In: Proceedings of the 10th IEEE/IFIP Network Operations and Management Symposium. 2006, 127−137
31	E Cecchet, R Singh, U Sharma, P Shenoy. Dolly: virtualization-driven database provisioning for the cloud. In: ACM SIGPLAN Notices. 2011, 51−62
32	H Tao, C Ningjiang, W Jun, Z Wen-bo, Z Yong. OnceAS/Q: a QoSenabled web application server. Journal of Software, 2004, 15(12): 1787−1799
33	J Norris, K Coleman, A Fox, G Candea. Oncall: defeating spikes with a free-market application cluster. In: Proceedings of the 2004 Interna- tional Conference on Autonomic Computing. 2004, 198−205
34	X Liu, X Zhu, S Singhal, M Arlitt. Adaptive entitlement control of resource containers on shared servers. In: Proceedings of the 9th IFIP/IEEE International Symposium onIntegrated Network Management. 2005, 163−176
35	X Zhu, Z Wang, S Singhal. Utility-driven workload management using nested control design. In: Proceedings of the 2006 American Control Conference. 2006, 6033−6038

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