One of the biggest problems in heterogeneous computing is how tasks should be mapped in these kinds of environments. Because this problem of mapping tasks has been shown to be NP-complete, it requires heuristic techniques. Therefore, we present new schedulers based on the apportionment methods used in elections. In order to obtain the performances of these schedulers we compare them with other known and used heuristics in many different parameters. The presented heuristics can be used when the tasks are big and when they can be divided in smaller sub-tasks. The comparison in this paper shows that these apportionment methods can cope well with the other methods when the number of tasks in the system is no bigger than a certain level. The new apportionment scheduler, based on Hamilton’s method, copes well with the existing ones and it outperforms the other schedulers when some conditions are met.
Schedulers Mapping Heuristics Apportionment Methods
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