A heuristic algorithm for mapping communicating tasks on heterogeneous resources

Abstract

A heuristic algorithm that maps data processing tasks onto heterogeneous resources (i.e. processors and links of various capacities) is presented. The algorithm tries to achieve a good throughput of the whole data processing pipeline, taking both parallelism (load balance) and communication volume (locality) into account. It performs well both under computationally intensive and communication-intensive conditions. When all tasks/processors are of the same size and communication is negligible, it quickly distributes the computation load over the processors and finds the optimal mapping. As communication becomes significant and reveals a bottleneck, it trades parallelism for reduction of communication traffic. Experimental results using a topology generator that models the Internet show that it performs significantly better than communication-ignorant schedulers.