No abstract

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.

YARN is an emerging resource management in the Hadoop ecosystem, where big data in the scale of petabytes/day are processed with the use of commercial off-the-shelf servers. At present YARN supports only RAM and CPU reservation/control. However, the reservation and the control of disk I/O throughput are also needed to provide a satisfactory performance for MapReduce applications. In this paper, we propose a solution with software components that can be integrated into YARN to support the reservation of disk I/O throughput in YARN. Experiments show that a proposed solution can provide a disk I/O throughput guarantee for MapReduce applications.

No abstract

No abstract