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A simulation model to implement multiple client class server-client ...

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when tenants are placed in on a shared resource environment the <strong>to</strong>tal capacity with mixed workload<br />

of these tenants is less than that of when system is considered as single <strong>class</strong>. For instance,<br />

if two tenants share 15 resource units each <strong>to</strong>tal workload capacity it can handle is approximately<br />

60 req/sec. Such behavior also pointed out by Kwok et al in [10].<br />

7. Summery<br />

This chapter presented the characteristics, requirements and importance of a <strong>simulation</strong> environment<br />

<strong>to</strong> represent a multi-<strong>client</strong> <strong>class</strong> system. Using popular discrete event <strong>simulation</strong> mechanism,<br />

we presented an appropriate discrete event <strong>simulation</strong> <strong>model</strong> <strong>to</strong> <strong>implement</strong> multi-<strong>client</strong><br />

<strong>class</strong> systems with different settings. Then the <strong>model</strong> <strong>implement</strong>ation was validated using queuing<br />

theoretic principles. The <strong>simulation</strong> settings that will be used in the rest of the chapter were<br />

also presented. Finally, the behavior of the <strong>simulation</strong> environments was compared <strong>to</strong> the behavior<br />

of physical systems utilizing the case studies available from the literature.<br />

References<br />

[1] J. Banks, J. Carson, B. L. Nelson, D. Nicol, Discrete-Event System Simulation (4th Edition), 4th Edition, Prentice<br />

Hall, 2004.<br />

[2] C. Lu, Y. Lu, T. F. Abdelzaher, J. A. Stankovic, S. H. Son, Feedback control architecture and design methodology<br />

for service delay guarantees in web <strong>server</strong>s, IEEE Trans. Parallel Distrib. Syst. (2006) 1014–1027.<br />

[3] C. Lu, Feedback control real-time scheduling, Ph.D. thesis, University of Virginia (2001).<br />

[4] P. Padala, Au<strong>to</strong>mated management of virtualized data centers, Ph.D. thesis, University of Michigan (2010).<br />

[5] J. L. Hellerstein, Y. Diao, S. Parekh, D. M. Tilbury, Feedback Control of Computing Systems, John Wiley and<br />

Sons, 2004.<br />

[6] L. Chenyang, J. Stankovic, G. Tao, S. Son, Design and evaluation of a feedback control edf scheduling algorithm,<br />

in: Real-Time Systems Symposium, 1999. Proceedings. The 20th IEEE, 1999, pp. 56 –67.<br />

[7] Z. Wang, X. Zhu, S. Singhal, Z. Wang, X. Zhu, S. Singhal, Utilization vs. slo-based control for dynamic sizing of<br />

resource partitions (2006).<br />

[8] X. Zhu, Z. Wang, S. Singhal, Utility-driven workload management using nested control design, no. HPL-2005-<br />

193R1, Hewlett Packard Labora<strong>to</strong>ries, 2006, p. 8.<br />

[9] P. Pradeep, H. Kai-Yuan, S. K. G., Z. Xiaoyun, U. Mustafa, W. Zhikui, S. Sharad, M. Arif, Au<strong>to</strong>mated control of<br />

<strong>multiple</strong> virtualized resources (2009).<br />

[10] T. Kwok, A. Mohindra, Resource calculations with constraints, and placement of tenants and instances for multitenant<br />

saas applications, in: Proceedings of the 6th International Conference on Service-Oriented Computing,<br />

ICSOC ’08, Springer-Verlag, 2008, pp. 633–648.<br />

[11] Z. H. Wang, C. J. Guo, B. Gao, W. Sun, Z. Zhang, W. H. An, A study and performance evaluation of the multitenant<br />

data tier design patterns for service oriented computing, in: IEEE International Conference on e-Business<br />

Engineering, 2008. ICEBE ’08., 2008, pp. 94 –101.<br />

[12] Y. Lu, T. Abdelzaher, C. Lu, L. Sha, X. Liu, Feedback control with queueing-theoretic prediction for relative delay<br />

guarantees in web <strong>server</strong>s (2003).<br />

[13] M. Karlsson, X. Zhu, C. Karamanolis, An adaptive optimal controller for non-intrusive performance differentiation<br />

in computing services, in: In IEEE Conference on Control and Au<strong>to</strong>mation (ICCA), 2005.<br />

[14] M. Li<strong>to</strong>iu, A performance analysis method for au<strong>to</strong>nomic computing systems, ACM Trans. Au<strong>to</strong>n. Adapt. Syst. 2.<br />

12

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