Parallel and Distributed Computing, International Symposium on (2009)
June 30, 2009 to July 4, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ISPDC.2009.26
In this paper, we study the problem of optimizing the throughput for micro-factories subject to failures. The challenge consists in mapping several tasks onto a set of machines. The originality of our approach is the failure model for such applications in which tasks are subject to failures rather than machines. If there is exactly one task per machine 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 machine. Several polynomial time heuristics are presented for the most realistic specialized setting, in which tasks of a same type can be mapped onto the same machine. 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.
Distributed Systems, Fault Tolerance, Scheduling, Optimization Heuristics
A. Benoit, L. Philippe, J. Nicod and A. Dobrila, "Throughput Optimization for Micro-factories Subject to Failures," 2009 Eighth International Symposium on Parallel and Distributed Computing (ISPDC), Lisbon, 2009, pp. 11-18.