Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - An analysis of the server characteristics and resource utilization in Google Cloud
AU - Garraghan, Peter
AU - Townend, Paul
AU - Xu, Jie
PY - 2013/6/13
Y1 - 2013/6/13
N2 - Understanding the resource utilization and server characteristics of large-scale systems is crucial if service providers are to optimize their operations whilst maintaining Quality of Service. For large-scale data enters, identifying the characteristics of resource demand and the current availability of such resources, allows system managers to design and deploy mechanisms to improve data enter utilization and meet Service Level Agreements with their customers, as well as facilitating business expansion. In this paper, we present a large-scale analysis of server resource utilization and a characterization of a production Cloud data enter using the most recent data enter trace logs made available by Google. We present their statistical properties, and a comprehensive coarse-grain analysis of the data, including submission rates, server classification, and server resource utilization. Additionally, we perform a fine-grained analysis to quantify the resource utilization of servers wasted due to the early termination of tasks. Our results show that data enter resource utilization remains relatively stable at between 40 - 60%, that the degree of correlation between server utilization and Cloud workload environment varies by server architecture, and that the amount of resource utilization wasted varies between 4.53 - 14.22% for different server architectures. This provides invaluable real-world empirical data for Cloud researchers in many subject areas.
AB - Understanding the resource utilization and server characteristics of large-scale systems is crucial if service providers are to optimize their operations whilst maintaining Quality of Service. For large-scale data enters, identifying the characteristics of resource demand and the current availability of such resources, allows system managers to design and deploy mechanisms to improve data enter utilization and meet Service Level Agreements with their customers, as well as facilitating business expansion. In this paper, we present a large-scale analysis of server resource utilization and a characterization of a production Cloud data enter using the most recent data enter trace logs made available by Google. We present their statistical properties, and a comprehensive coarse-grain analysis of the data, including submission rates, server classification, and server resource utilization. Additionally, we perform a fine-grained analysis to quantify the resource utilization of servers wasted due to the early termination of tasks. Our results show that data enter resource utilization remains relatively stable at between 40 - 60%, that the degree of correlation between server utilization and Cloud workload environment varies by server architecture, and that the amount of resource utilization wasted varies between 4.53 - 14.22% for different server architectures. This provides invaluable real-world empirical data for Cloud researchers in many subject areas.
KW - dependability
KW - Cloud computing
KW - empirical analysis
KW - server characterization
KW - resource utilization
U2 - 10.1109/IC2E.2013.40
DO - 10.1109/IC2E.2013.40
M3 - Conference contribution/Paper
SN - 9780769549453
SP - 124
EP - 131
BT - 2013 IEEE International Conference on Cloud Engineering (IC2E)
PB - IEEE
ER -