We propose a constant approximation algorithm for generalizations of the Flexible Flow Shop (FFS) problem which form a realistic model for non-preemptive scheduling in MapReduce systems. Our results concern the minimization of the total weighted completion time of a set of MapReduce jobs on unrelated processors and improve substantially on the model proposed by Moseley et al. (SPAA 2011) in two directions: (i) we consider jobs consisting of multiple Map and Reduce tasks, which is the key idea behind MapReduce computations, and (ii) we introduce into our model the crucial cost of the data shuffle phase, i.e., the cost for the transmission of intermediate data from Map to Reduce tasks. Moreover, we experimentally evaluate our algorithm compared with a lower bound on the optimal cost of our problem as well as with a fast algorithm, which combines a simple online assignment of tasks to processors with a standard scheduling policy. As we observe, for random instances that capture data locality issues, our algorithm achieves a better performance.
Bibtex: Dimitris et al. (2014)
Fotakis Dimitris, Ioannis Milis, Zampetakis Emmanouil, and Zois Georgios. Scheduling mapreduce jobs on unrelated processors. In Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference, 2–5. 2014. ↩