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Computing workflows in heterogeneous multiprocessor systems are frequentlymodeled as directed acyclic graphs of tasks and data blocks, which representcomputational modules and their dependencies in the form of data produced by atask and used by others. However, for some workflows, such as the task schedulein a digital signal processor may run out of memory by exposing too muchparallelism. This paper focuses on the data allocation and task schedulingproblem under memory constraints, and concentrates on shared memory platforms.We first propose an integer linear programming model to formulate the problem.Then we consider the problem as an extended flexible job shop schedulingproblem, while trying to minimize the critical path of the graph. To solve thisproblem, we propose a tabu search algorithm (TS) which combines severaldistinguished features such as a greedy initial solution construction methodand a mixed neighborhood evaluation strategy based on exact evaluation andapproximate evaluation methods. Experimental results on randomly generatedinstances show that the the proposed TS algorithm can obtain relativelyhigh-quality solutions in a reasonable computational time. In specific, thetabu search method averagely improves the makespan by 5-25\% compared to theclassical load balancing algorithm that are widely used in the literature.Besides, some key features of TS are also analyzed to identify its successfactors.

A heuristic method for data allocation and task scheduling on heterogeneous multiprocessor systems under memory constraints