  Resource management is one of the most indispensable components of
cluster-level infrastructure layers. Users of such systems should be able to
specify their job requirements as a configuration parameter (CPU, RAM, disk
I/O, network I/O) and have the scheduler translate those into an appropriate
reservation and allocation of resources. YARN is an emerging resource
management in the Hadoop ecosystem, which supports only RAM and CPU reservation
at present.
  In this paper, we propose a solution that takes into account the operation of
the Hadoop Distributed File System to control the data rate of applications in
the framework of a Hadoop compute platform. We utilize the property that a data
pipe between a container and a DataNode consists of a disk I/O subpipe and a
TCP/IP subpipe. We have implemented building block software components to
control the data rate of data pipes between containers and DataNodes and
provide a proof-of-concept with measurement results.
