In this paper, a method for estimating task execution times is presented in order to facilitate dynamic scheduling in a heterogeneous metacomputing environment. Execution time is treated as a random variable and is statistically estimated from past observations. This method predicts the execution time as a function of several parameters of the input data and does not require any direct information about the algorithms used by the tasks or the architecture of the machines. Techniques based upon the concept of analytic benchmarking/code profiling are used to characterize the performance differences between machines, allowing observations from dissimilar machines to be used when making a prediction. Experimental results are presented which use actual execution time data gathered from 16 heterogeneous machines.

An efficient run time, statistical scheme for estimating the execution time of a task is presented, in order to facilitate run time matching and scheduling in a distributed heterogeneous computing environment. This scheme is based upon a nonparametric regression technique, where the execution time estimate for a task is computed from past observations. Furthermore, this technique is able to compensate for different parameters upon which the execution time depends, and does not require any knowledge of the architecture of the target machine. It is also able to make accurate predictions when erroneous data is present in the set of observations, and has been experimentally shown to produce estimates with very low error even with few past values from which to calculate a new estimate.