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Stochastic convexity of sums of i.i.d. non-negative random variables with applications

Published online by Cambridge University Press:  14 July 2016

Armand M. Makowski*
Affiliation:
University of Maryland, College Park
Thomas K. Philips*
Affiliation:
IBM Thomas J. Watson Research Center
*
Postal address: Electrical Engineering Department and Systems Research Center, University of Maryland, College Park, MD 20742.
∗∗ Postal address: IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA.

Abstract

We present some monotonicity and convexity properties for the sequence of partial sums associated with a sequence of non-negative independent identically distributed random variables. These results are applied to a system of parallel queues with Bernoulli routing, and are useful in establishing a performance comparison between two scheduling strategies in multiprocessor systems.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1992 

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Footnotes

The work of this author was performed while he was a summer visitor at the IBM Thomas J. Watson Research Center.

References

[1]Baccelli, F. and Makowski, A. M. (1989) Queueing models for systems with synchronization constraints. Proc. IEEE 77, 138161.Google Scholar
[2]Chang, C.-S., Chao, X.-L. and Pinedo, M. (1989) Some inequalities for bulk queues with Poisson arrivals. Unpublished paper, ORSA/TIMS meeting, Vancouver, Canada, May 1989.Google Scholar
[3]Kleinrock, L. (1976) Queueing Systems I: Theory. Wiley, New York.Google Scholar
[4]Makowski, A. M. and Philips, T. K. (1990) Stochastic convexity of sums of i.i.d. non-negative random variables with applications. IBM Research Report RC-16313.Google Scholar
[5]Makowski, A. M., Philips, T. K. and Varma, S. (1991) Comparing two scheduling strategies in multi-processor systems. Technical Report SRC 91-1, Systems Research Center, University of Maryland, College Park.Google Scholar
[6]Marshall, A. W. and Olkin, I. (1979) Inequalities: Theory of Majorization and its Applications. Academic Press, New York.Google Scholar
[7]Ross, S. (1984) Stochastic Processes. Wiley, New York.Google Scholar
[8]Shaked, M. and Shanthikumar, J. G. (1988) Stochastic convexity and its applications. Adv. Appl. Prob. 20, 427446.Google Scholar
[9]Shaked, M. and Shanthikumar, J. G. (1990) Convexity of a set of stochastically ordered random variables. Adv. Appl. Prob. 22, 160177.Google Scholar
[10]Shaked, M. and Shanthikumar, J. G. (1990) Parametric stochastic convexity and concavity of stochastic processes. Ann. Inst. Statist. Math. 42, 509531.Google Scholar
[11]Stoyan, D. (1984) Comparison Methods for Queues and Other Stochastic Models. English translation, ed. Daley, D. J. Wiley, New York.Google Scholar