Research on resource allocation for multi-tier web applications in a virtualization environment

Shuguo YANG

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PDF(162 KB)
Front. Comput. Sci. ›› 2011, Vol. 5 ›› Issue (4) : 506-512. DOI: 10.1007/s11704-011-0127-6
RESEARCH ARTICLE

Research on resource allocation for multi-tier web applications in a virtualization environment

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Abstract

Resource allocation for multi-tier web applications in virtualization environments is one of the most important problems in autonomous computing. On one hand, the more resources that are provisioned to a multi-tier web application, the easier it is to meet service level objectives (SLO). On the other hand, the virtual machine which hosts the multi-tier web application needs to be consolidated as much as possible in order to maintain high resource utilization. This paper presents an adaptive resource controller which consists of a feedback utilization controller and an auto-regressive and moving average model (ARMA)-based model estimator. It can meet application-level quality of service (QoS) goals while achieving high resource utilization. To evaluate the proposed controllers, simulations are performed on a testbed simulating a virtual data center using Xen virtual machines. Experimental results indicate that the controllers can improve CPU utilization and make the best trade-off between resource utilization and performance for multi-tier web applications.

Keywords

CPU utilization / resource allocation / quality of service / multi-tier web applications

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Shuguo YANG. Research on resource allocation for multi-tier web applications in a virtualization environment. Front Comput Sci Chin, 2011, 5(4): 506‒512 https://doi.org/10.1007/s11704-011-0127-6

References

[1]
Li K, Jamin S. A measurement-based admission-controlled web server. In: Proceedings of 19th IEEE International Conference on Computer Communications. 2000, 651–659
[2]
Voigt T, Tewari R, Freimuth D, Mehra A. Kernel mechanisms for service differentiation in overloaded Web servers. In: Proceedings of 2001 USENIX Annual Technical Conference. 2001, 189–202
[3]
Diao Y, Gandhi N, Hellerstein J L, Parekh S, Tilbury D M. Using MIMO feedback control to enforce policies for interrelated metrics with application to the Apache web server. In: Proceedings of the IEEE/IFIP Network Operations and Management Symposium. 2002, 219–234
[4]
Liu X, Sha L, Diao Y, Froehlich S, Hellerstein J L, Parekh S. On-line response time optimization of an Apache web server. In: Proceedings of 11th International Workshop on Quality of Service. 2003, 461–478
[5]
Kamra A, Misra V, Nahum E M. Yaksha: a self-tuning controller for managing the performance of 3-tiered web sites. In: Proceedings of 12th International Workshop Quality of Service. 2004, 47–56
[6]
Karlsson M, Zhu X, Karamanolis C. An adaptive optimal controller for non-intrusive performance differentiation in computing services. In: Proceedings of 5th International Conference on Control and Automation. 2005, 709–714
[7]
Zhu X, Wang Z, Singhal S. Utility-driven workload management using nested control design. In: Proceedings of 2006 American Control Conference. 2006
[8]
Zhang Q, Cherkasova L, Smirni E. A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In: Proceedings of 4th International Conference on Autonomic Computing. 2007
[9]
Padala P, Shin K G, Zhu X, Uysal M, Wang Z, Singhal S, Merchant A, Salem K. Adaptive control of virtualized resources in utility computing environments. In: Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems. 2007, 289–302
[10]
Padala P, Hou K Y, Shin K G, Zhu X, Uysal M, Wang Z, Singhal S, Merchant A. Automated control of multiple virtualized resources. In: Proceedings of the 4th ACM European conference on Computer systems. 2009, 13–26

Acknowledgements

This work was supported by the Natural Science Foundation of Shandong Province of China (ZR2009GM017).

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
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