DocumentCode
1918785
Title
Characterizing Machines and Workloads on a Google Cluster
Author
Liu, Zitao ; Cho, Sangyeun
Author_Institution
Comput. Sci. Dept., Univ. of Pittsburgh, Pittsburgh, PA, USA
fYear
2012
fDate
10-13 Sept. 2012
Firstpage
397
Lastpage
403
Abstract
Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a large-scale Google cluster usage trace dataset and characterizes how the machines in the cluster are managed and the workloads submitted during a 29-day period behave. We focus on the frequency and pattern of machine maintenance events, job- and task-level workload behavior, and how the overall cluster resources are utilized.
Keywords
Internet; cloud computing; search engines; Google cluster; characterizing machines; characterizing workloads; cloud computing; cloud service providers; Cloud computing; Companies; Google; Parallel processing; Scalability; Single machine scheduling; Cloud computing; datacenter computing; job scheduling; system management;
fLanguage
English
Publisher
ieee
Conference_Titel
Parallel Processing Workshops (ICPPW), 2012 41st International Conference on
Conference_Location
Pittsburgh, PA
ISSN
1530-2016
Print_ISBN
978-1-4673-2509-7
Type
conf
DOI
10.1109/ICPPW.2012.57
Filename
6337507
Link To Document