01407nas a2200157 4500008004100000245009000041210006900131520088900200653002001089653001401109653002501123653002601148100002001174700001501194856004001209 2011 eng d00aTowards Optimal Resource Provisioning for Running MapReduce Programs in Public Clouds0 aTowards Optimal Resource Provisioning for Running MapReduce Prog3 aRunning MapReduce programs in the public cloud introduces the important problem: how to optimize resource provisioning to minimize the ﬁnancial charge for a speciﬁc job? In this paper, we study the whole process of MapReduce processing and build up a cost function that explicitly models the relationship between the amount of input data, the available system resources (Map and Reduce slots), and the complexity of the Reduce function for the target MapReduce job. The model parameters can be learned from test runs with a small number of nodes. Based on this cost model, we can solve a number of decision problems, such as the optimal amount of resources that can minimize the ﬁnancial cost with a time deadline or minimize the time under certain ﬁnancial budget. Experimental results show that this cost model performs well on tested MapReduce programs.10aCloud Computing10aMapReduce10aPerformance Modeling10aResource Provisioning1 aTian, Fengguang1 aChen, Keke uhttp://knoesis.wright.edu/node/1032