  Nowadays most of the cloud applications process large amount of data to
provide the desired results. Data volumes to be processed by cloud applications
are growing much faster than computing power. This growth demands new
strategies for processing and analyzing information. Dealing with large data
volumes requires two things: 1) Inexpensive, reliable storage 2) New tools for
analyzing unstructured and structured data. Hadoop is a powerful open source
software platform that addresses both of these problems. The current Hadoop
implementation assumes that computing nodes in a cluster are homogeneous in
nature. Hadoop lacks performance in heterogeneous clusters where the nodes have
different computing capacity. In this paper we address the issues that affect
the performance of hadoop in heterogeneous clusters and also provided some
guidelines on how to overcome these bottlenecks

Distributed, Parallel, and Cluster Computing