Abstract
This paper is motivated by the need of deadline-bounded applications in live mobile network environments to obtain the guarantee and the appropriate share of an input and output (I/O) data rate. However, data processing frameworks only support the request of memory and the computing capacity at present. In this paper, we propose a solution that allows the control of disk I/O and network I/O for data processing applications in YARN and Mesos frameworks. Experimental results show that our tool can provision the I/O data rate sharing of competing data processing applications.
Highlights
Introduction and MotivationWhen a specific application submits a job, a data processing framework such as Apache Hadoop [1, 26], Hadoop YARN [25], Mesos [2], reserves and allocates necessary computing resources for the execution of the job
Since a computing cluster can be built up of heterogeneous hardware and software components, the input and output (I/O) data rate perceived by applications is unpredictable due to the contention for the resources of the physical servers, and there is a need to monitor applications running on these platforms as well [12]
We demonstrate that the proposed functionalities can be integrated into two popular data processing frameworks such as Mesos and YARN to control the I/O data rates of applications, which may relieve the pain of service providers on the integration of schedulers to existing frameworks
Summary
When a specific application submits a job, a data processing framework such as Apache Hadoop [1, 26], Hadoop YARN [25], Mesos [2], reserves and allocates necessary computing resources for the execution of the job. Since a computing cluster can be built up of heterogeneous hardware and software components, the I/O data rate perceived by applications is unpredictable due to the contention for the resources of the physical servers, and there is a need to monitor applications running on these platforms as well [12]. Nowadays telecommunication operators often apply frameworks to regularly process big data sets with specific deadlines in their computing clusters. When applications compete for a resource in the hardware and the network level, which is hidden from programmers (and from applications), they may suffer the I/O performance degradation
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