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MULTI-CLASS RESOURCE SHARING WITH PREEMPTIVE PRIORITIES

Published online by Cambridge University Press:  11 July 2017

Isi Mitrani
Isi Mitrani*
Affiliation:
School of Computing Science, Newcastle University, Newcastle upon Tyne, UK E-mail: isi.mitrani@ncl.ac.uk
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Abstract

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Different virtual machines can share servers, subject to resource constraints. Incoming jobs whose resource requirements cannot be satisfied are queued and receive service according to a preemptive-resume scheduling policy. The problem is to evaluate a cost function, including holding and server costs, with a view to searching for the optimal number of servers. A model with two job types is analyzed exactly and the results are used to develop accurate approximations, which are then extended to more than two classes. Numerical examples and comparisons with simulations are presented.

Keywords

queueing theorysimulationstochastic modeling
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 32 , Issue 3 , July 2018 , pp. 323 - 339
Copyright
Copyright © Cambridge University Press 2017 

References

1.Ezhilchelvan, P. & Mitrani, I. (2017). Optimal provisioning of servers for hosting services of multiple types. Simulation Modelling Practice and Theory 75: 17–28.Google Scholar
2.Feng, W., Kawada, M. & Adachi, K. (2000). Analysis of a multi-server queue with two priority classes and (M,N)-threshold service schedule I: Non-preemptive priority. International Trans. in Operational Research 7: 653–671.Google Scholar
3.Feng, W., Kawada, M. & Adachi, K. (2001). Analysis of a multiserver queue with two priority classes and (M,N)-threshold service schedule. II. preemptive priority. Asia-Pacific Journal of Operations Research 18: 101–124.Google Scholar
4.Gail, H., Hantler, S. & Taylor, B. (1988). Analysis of a non-preemptive priority multiserver queue. Advances in Applied Probability 20: 852–879.Google Scholar
5.Gail, H., Hantler, S. & Taylor, B. (1992). On a preemptive Markovian queues with multiple servers and two priority classes. Mathematics of Operations Research 17: 365–391.Google Scholar
6.Harchol-Balter, M., Osogami, T., Scheller-Wolf, A. & Wierman, A. (2005). Multi-server queueing systems with multiple priority classes. Queueing Systems Theory and Applications 51(3): 331–360.Google Scholar
7.Kao, E. & Wilson, S. (1999). Analysis of nonpreemptive priority queues with multiple servers and two priority classes. European Journal of Operational Research 118: 181–193.Google Scholar
8.Kao, E. & Narayanan, K. (1991). Modeling a multiprocessor system with preemptive priorities. Management Science 2: 185–197.Google Scholar
9.Kao, E. & Narayanan, K.S. (1990). Computing steady-state probabilities of a nonpreeptive priority multiserver queue. Journal on Computing 2(3): 211–218.Google Scholar
10.Kella, O. & Yechiali, U. (1985). Waiting times in the non-preemptive priority M/M/c queue. Stochastic Models 1: 257–262.Google Scholar
11.Mitrani, I. (1998). Probabilistic modelling. Cambridge, UK: Cambridge University Press.Google Scholar
12.Mitrani, I. & King, P. (1981). Multiprocessor systems with preemptive priorities. Performance Evaluation 1: 118–125.Google Scholar
13.Voorsluys, W., Broberg, J., Venugopal, S. & Buyya, R. (2009). Cost of virtual machine live migration in clouds: A performance evaluation. LNCS 5931: 254–265.Google Scholar
14.Wierman, A., Osogami, T., Harchol-Balter, M. & Scheller-Wolf, A. (2006). How many servers are best in a dual-priority M / PH / k system?. Performance Evaluation 63(12): 1253–1272.Google Scholar