In this paper, we present a generalized model for the performance evaluation of scheduling compute-intensive jobs with unknown service times in computational clusters. We propose the application of parameters defined in the SPECpower ssj2008 benchmark of the Standard Performance Evaluation Corporation to construct a performance evaluation model. In addition, we also define a method to rank physical servers based on either the high performance priority or the energy efficiency priority, and measures to characterize the performance of computational clusters.We investigate three schemes (separate queue, class queue and common queue) for buffering jobs in a computational cluster that is built from Commercial OffThe-Shelf (COTS) servers. Numerical results show that the buffering schemes do not have impact on performance measures related to the energy consumption of the investigated cluster. However, the buffering schemes play an important role in the quality of service parameters such the waiting time and the response time experienced by arriving jobs. Furthermore, Dynamic Voltage and Frequency Scaling should be carefully applied if one wants to reduce the energy consumption of computational clusters.

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.

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capability of computational clusters. An investigation is performed using a specific scenario of computing clusters with realistic parameters. Numerical results show that a trade-off between the performance and the energy efficiency can be controlled by the proposed algorithms.

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