A Method for Estimating the Execution Time of a Parallel Task on a Grid Node

Abstract

The mapping problem has been studied extensively and many algorithms have been proposed. However, unrealistic assumptions have made the practicality of those algorithms doubtful. One of these assumptions is the ability to precisely calculate the execution time of a task to be mapped on a node before the actual execution. Since the theoretical calculation of task execution time is impossible in real environments, an estimation methodology is needed. In this paper, a practical method to estimate the execution time of a parallel task to be mapped on a grid node is proposed. It is not necessary to know the internal design and algorithm of the application in order to apply this method. The estimation is based upon past observations of the task executions. The estimating technique is a k-nearest-neighbours algorithm (knn). A backward predictor elimination, leave-one-out cross validation, and a statistical technique are used to derive the relevant parameters to be used by knn. Experimental results show that on average the proposed method can produce 2.3 times the number of accurate estimated execution times (with errors less than 25%) greater than the existing method.