Building Block Components to Control a Data Rate in the Apache Hadoop Compute Platform

TL;DR

This paper introduces building block components that enable data rate control in Hadoop's compute platform by managing data pipes between containers and DataNodes, enhancing resource management beyond CPU and RAM.

Contribution

It presents a novel solution for controlling data rates in Hadoop by managing data pipes, extending resource management capabilities in YARN.

Findings

Implemented data rate control components successfully

Demonstrated effective data pipe management with measurements

Enhanced resource control in Hadoop environment

Abstract

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.