Modeling the Task of Google MapReduce Workload

Author

Xiaoyang Lin ; Piyuan Lin ; Peijie Huang ; Linxiao Chen ; Ziwei Fan ; Peisen Huang

Author_Institution

Coll. of Math. & Inf., South China Agric. Univ., Guangzhou, China

fYear

2015

fDate

4-7 May 2015

Firstpage

1229

Lastpage

1232

Abstract

In order to better understand and describe tasks and improve the ability of Cloud, the analyzing of tasks inessential. A coarse-grained analysis, cluster analysis, anointer-cluster analysis are used to model tasks for the analysis of a one-month trace of a Google MapReduce cluster across about 12,000 machines. In this paper, we consider the k value which is central to the performance of k-means algorithm can effect on modelling. Besides, we also take the selection of attributes into account which are used as the dimension when tasks are classified. Experiment results by using different type of task attributes and k value show the well performance odour approach.

Keywords

cloud computing; pattern classification; pattern clustering; Google MapReduce cluster; Google MapReduce workload; cloud; coarse-grained analysis; intracluster analysis; k-means algorithm; one-month trace; task attributes; Analytical models; Computational modeling; Data models; Elbow; Google; Logistics; Mathematical model; Cloud; Google Trace; k-means; task modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on

Conference_Location

Shenzhen

Type

conf

DOI

10.1109/CCGrid.2015.104

Filename

7152628