Evaluating I/O Scheduler in Virtual Machines for Mapreduce Application

Authors:
Jun Fang;Shoubao Yang;Wenyu Zhou;Hu Song
Affiliations:
-;-;-;-
Venue:
GCC '10 Proceedings of the 2010 Ninth International Conference on Grid and Cloud Computing
Year:
2010

Citing 0
Cited 2

Minimizing Cost of Virtual Machines for Deadline-Constrained MapReduce Applications in the Cloud

GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
An adaptive data transfer algorithm using block device reconfiguration in virtual MapReduce clusters

Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference

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Abstract

This paper explores the relationship between I/O scheduling in a virtual machine monitor (VMM) and performance of Map Reduce applications running on the virtual machines (VMs). Traditionally, Map Reduce applications running on the virtual machines perform worse than on the physical machines due to competing for I/O. In this paper, we discuss the effect of I/O scheduling on performance of Map Reduce running on VMs. We present two strategies to improve the I/O scheduling and evaluate the performance through experiments. The results show that the strategies could reduce the percentage of I/O waiting and time consuming of Map Reduce applications effectively.