Keke Chen (Advisor), Tk Prasad (Committee Member), Bin Wang (Committee Member)
Master of Science (MS)
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 thesis, 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 on a small amount of data. 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.
Department or Program
Department of Computer Science
Year Degree Awarded
Copyright 2011, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.