Abstract-Running 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.
Tian and Chen REF proposed a cost model that estimates the performance of a job from a set of test runs on a small input datasets and small number of nodes.
Towards Optimal Resource Provisioning for Running MapReduce Programs in Public Clouds
2011 IEEE 4th International Conference on Cloud Computing
2011 IEEE 4th International Conference on Cloud Computing
Computer Science