Mapping workflow applications with types on heterogeneous specialized platforms

In this paper, we study the problem of optimizing the throughput of coarse-grain workflow applications, for which each task of the workflow is of a given type, and subject to failures. The goal is to map such an application onto a heterogeneous specialized platform, which consists of a set of processors that can be specialized to process one type of tasks. The objective function is to maximize the throughput of the workflow, i.e., the rate at which the data sets can enter the system. If there is exactly one task per processor in the mapping, then we prove that the optimal solution can be computed in polynomial time. However, the problem becomes NP-hard if several tasks can be assigned to the same processor. Several polynomial time heuristics are presented for the most realistic specialized setting, in which tasks of the same type can be mapped onto the same processor, but a processor cannot process two tasks of different types. Also, we give an integer linear program formulation of this problem, which allows us to find the optimal solution (in exponential time) for small problem instances. Experimental results show that the best heuristics obtain a good throughput, much better than the throughput obtained with a random mapping. Moreover, we obtain a throughput close to the optimal solution in the particular cases on which the optimal throughput can be computed (small problem instances or particular mappings).

► Maximizing throughput of workflow execution on heterogeneous platforms with failures.
► When typed tasks can share a processor, the mapping problem is NP-hard.
► An ILP formulation is given that allows to compute optimal solution.
► Polynomial heuristics can efficiently solve the problem.