Title :
Modeling I/O Interference in Data Intensive Map-Reduce Applications
Author_Institution :
Inst. of Ind. Sci., Univ. of Tokyo, Tokyo, Japan
Abstract :
Map-Reduce is a popular framework for very large-scale data mining and processing. Recently, some works have attempted to model the behavior of Map-Reduce, but these existing models ignore the non-linearity of disk I/O performance under contention, which is a critical aspect of estimating the performance of data intensive applications. Understanding I/O interference between tasks running on the same node is critical in optimizing task scheduling for improved resource utilization. In this paper, we present a model to estimate the I/O behavior of Map-Reduce applications that can be used to achieve these goals.
Keywords :
cloud computing; data mining; resource allocation; scheduling; I/O behavior estimation; I/O interference modeling; cloud computing; data intensive MapReduce application; disk I/O performance nonlinearity; resource utilization; task scheduling optimization; very large-scale data mining; very large-scale data processing; Cloud computing; Computational modeling; Data models; Heart beat; Interference; Predictive models; Resource management; Map-Reduce; cloud computing; data intensive;
Conference_Titel :
Applications and the Internet (SAINT), 2012 IEEE/IPSJ 12th International Symposium on
Conference_Location :
Izmir
Print_ISBN :
978-1-4673-2001-6
Electronic_ISBN :
978-0-7695-4737-4
DOI :
10.1109/SAINT.2012.88
