Distributed image processing on a network of workstations

Abstract

In distributed computing systems, a critical concern is to efficiently partition and schedule the tasks among available processors in such a way that the overall processing time of the submitted tasks is at a minimum. On a network of workstations, using parallol virtual machine communication library, we conducted distributed image-processing experiments following two different scheduling and partitioning strategies. In this article, following the recently evolved paradigm, referred to as divisible load theory (DLT), we conducted an experimental study on the time performance to process a very large volume of image data on a network of workstations. As a case study, we use edge detection using Sobel operator as an application to demonstrate the performance of the strategy proposed by DLT. Then, we present our program model and timing mechanism for the distributed image processing. Following our system models, we compare two different partitioning and scheduling strategies: the partitioning and scheduling strategy following divisible load theory (PSSD) and the traditional, equal-partitioning strategy (EQS). Prom the experimental results and performance analysis using different image sizes, kernel sizes, and number of workstations, we observe that the time performance using PSSD is much better than that obtained using EQS. We also demonstrate the speed-up achieved by these strategies. Furthermore, we observe that the theoretical analysis using DLT agrees with the experimental results quite well, which verifies the feasibility of DLT in practical applications.