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<p>A model comprising several servers, each equipped with its own queue and with possibly different service speeds, is considered. Each server receives a dedicated arrival stream of jobs; there is also a stream of generic jobs that arrive to a job scheduler and can be individually allocated to any of the servers. It is shown that if the arrival streams are all Poisson and all jobs have the same exponentially distributed service requirements, the probabilistic splitting of the generic stream that minimizes the average job response time is such that it balances the server idle times in a weighted least-squares sense, where the weighting coefficients are related to the service speeds of the servers. The corresponding result holds for nonexponentially distributed service times if the service speeds are all equal. This result is used to develop adaptive quasi-static algorithms for allocating jobs in the generic arrival stream when the load parameters are unknown. The algorithms utilize server idle-time measurements which are sent periodically to the central job scheduler. A model is developed for these measurements, and the result mentioned is used to cast the problem into one of finding a projection of the root of an affine function, when only noisy values of the function can be observed.</p>
adaptive optimal load balancing; job allocation; nonhomogeneous multiserver system; central job scheduler; queue; service speeds; dedicated arrival stream; generic jobs; Poisson; exponentially distributed service requirements; probabilistic splitting; generic stream; average job response time; server idle times; weighted least-squares; weighting coefficients; nonexponentially distributed service times; adaptive quasi-static algorithms; load parameters; affine function; noisy values; multiprocessing systems; queueing theory; scheduling.

A. Kumar and F. Bonomi, "Adaptive Optimal Load Balancing in a Nonhomogeneous Multiserver System with a Central Job Scheduler," in IEEE Transactions on Computers, vol. 39, no. , pp. 1232-1250, 1990.
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