Calculation of CPU performance, power and cost using Hadoop

Technology is progressing at a fast pace, and it is the need of the hour to scrutinize big data workloads on cloud computing. The requisite of working with large data sets and more secure platforms gives rise to the necessity of analyzing techniques and tools regarding performance and power consumption. One of the most frequently used big data cloud computing framework is Hadoop. Hadoop — an open source implementation of MapReduce — is employed for processing big data, while handling extensive workload applications for several purposes. The performance, cost and power estimations of underlying processors on Hadoop can be done by characterizing different workloads on Hadoop framework. A comprehensive study is carried out to calculate CPU performance, cost and power consumption regarding different workloads, so as to facilitate efficient usage of different processors to deal with various workloads according to the user requirements.

[1]  Jamil Ahmed,et al.  Hadoop Architecture and Its Issues , 2014, 2014 International Conference on Computational Science and Computational Intelligence.

[2]  Yang Yang,et al.  K-Means Method for Grouping in Hybrid MapReduce Cluster , 2013, J. Comput..

[3]  Umesh Bellur,et al.  An Empirical Study of Hadoop's Energy Efficiency on a HPC Cluster , 2014, ICCS.

[4]  Jared Evans Fault Tolerance in Hadoop for Work Migration , 2011 .

[5]  Ravinder Kaur,et al.  Hadoop: Addressing challenges of Big Data , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[6]  Bryan Schauer Multicore Processors - A Necessity , 2008 .

[7]  Lavanya Ramakrishnan,et al.  Performance and energy efficiency of big data applications in cloud environments: A Hadoop case study , 2014, J. Parallel Distributed Comput..

[8]  Beng Chin Ooi,et al.  The performance of MapReduce , 2010, Proc. VLDB Endow..

[9]  Yun Tian,et al.  Improving MapReduce performance through data placement in heterogeneous Hadoop clusters , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

[10]  Geoffrey C. Fox,et al.  High Performance Parallel Computing with Clouds and Cloud Technologies , 2009, CloudComp.

[11]  Christoforos E. Kozyrakis,et al.  On the energy (in)efficiency of Hadoop clusters , 2010, OPSR.

[12]  Joseph Issa,et al.  Hadoop and memcached: Performance and power characterization and analysis , 2012, Journal of Cloud Computing: Advances, Systems and Applications.

[13]  Dave Cliff,et al.  A financial brokerage model for cloud computing , 2011, Journal of Cloud Computing: Advances, Systems and Applications.