Allocation of Customer Types to Servers: Clustering is Optimal
The model under consideration consists of n customer types attended by m parallel non-identical servers. Customers are allocated to the servers in a probabilistic manner; upon arrival customers are sent to one of the servers according to an m x n matrix of routing probabilities. We consider the problem of finding an allocation that minimizes a weighted sum of the mean waiting times. We expose the structure of an optimal allocation and describe for some special cases in detail how the structure may be exploited in actually computing an optimal allocation.
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- 1.S.C. Borst, “Optimal probabilistic allocation of customer types to servers”, CWI Report BS-R9415, 1994.Google Scholar
- 3.J.P. Buzen and P.P.-S. Chen, “Optimal load balancing in memory hierarchies”, In: Proc. IFIP 1974, ed. J.L. Rosenfeld (North-Holland, Amsterdam), pp. 271-275, 1974.Google Scholar
- 4.W.C. Cheng and R.R. Muntz, “Optimal routing for closed queueing networks”, In: Perf’ 90, eds. P.J.B. King, I. Mitrani, R.J. Pooley (North-Holland, Amsterdam), pp. 3-17, 1990.Google Scholar
- 5.E. De Souza e Silva and M. Gerla, “Load balancing in distributed systems with multiple classes and site constraints”, In: Perf.’ 84, ed. E. Gelenbe (North-Holland, Amsterdam), pp. 17-33, 1984.Google Scholar
- 6.M.R. Garey and D.S. Johnson, “Computers and Intractability: a Guide to the Theory of NP-Completeness”, (Freeman, San Francisco), 1979.Google Scholar
- 8.A.N. Tantawi and D. Towsley, “A general model for optimal static load balancing in star network configurations”, In: Perf.’ 84, ed. E. Gelenbe (North-Holland, Amsterdam), pp. 277-291, 1984.Google Scholar
- 11.C.M. Woodside and S.K. Tripathi, “Optimal allocation of file servers in a local network environment”, IEEE Trans. Soflw. Eng. 12, pp. 844–848, 1986.Google Scholar