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====Size of tasks====
Perfect knowledge of the [[execution time]] of each of the tasks allows to reach an optimal load distribution (see algorithm of [[prefix sum]]).<ref name="Sequential and parallel algorithms">{{cite book |last1=Sanders |first1=Peter |last2=Mehlhorn |first2=Kurt |last3=Dietzfelbinger |first3=Martin |last4=Dementiev |first4=Roman |title=Sequential and parallel algorithms and data structures : the basic toolbox |date=11 September 2019 |isbn=978-3-030-25208-3}}</ref> Unfortunately, this is in fact an idealized case. Knowing the exact [[execution
For this reason, there are several techniques to get an idea of the different execution times. First of all, in the fortunate scenario of having tasks of relatively homogeneous size, it is possible to consider that each of them will require approximately the average execution time. If, on the other hand, the execution time is very irregular, more sophisticated techniques must be used. One technique is to add some [[metadata]] to each task. Depending on the previous execution time for similar metadata, it is possible to make inferences for a future task based on statistics.<ref>{{cite journal |last1=Liu |first1=Qi |last2=Cai |first2=Weidong |last3=Jin |first3=Dandan |last4=Shen |first4=Jian |last5=Fu |first5=Zhangjie |last6=Liu |first6=Xiaodong |last7=Linge |first7=Nigel |title=Estimation Accuracy on Execution Time of Run-Time Tasks in a Heterogeneous Distributed Environment |journal=Sensors |date=30 August 2016 |volume=16 |issue=9 |pages=1386 |doi=10.3390/s16091386|pmid=27589753 |pmc=5038664 |bibcode=2016Senso..16.1386L |s2cid=391429 |doi-access=free }}</ref>
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