Abstract. 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 datacenters, identifying the characteristics of resource demand and the current availability of such resources, allows system managers to design and deploy mechanisms to improve datacenter 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 datacenter using the most recent datacenter 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 datacenter 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.
Garraghan et al. made in REF a coarse-grain statistical analysis of submission rates, server classification, and wasted resource server utilization due to task failure.
An Analysis of the Server Characteristics and Resource Utilization in Google Cloud
2013 IEEE International Conference on Cloud Engineering (IC2E)
2013 IEEE International Conference on Cloud Engineering (IC2E)
Computer Science