Exploring Characteristics Of Inter-Cluster Machines And Cloud Applications On Google Clusters

Modern cluster management systems have been evolving to cope with running and managing diverse cloud applications on heterogeneous computing clusters. Consequently, the system behaviours become complex and non-trivial to explain. In this paper we take the recently published Google trace data set version 3 (V3) as a case study to explore various aspects of intercluster differences. We analyse the distribution of underlying physical machines resource, e.g. number and types of machine, and metrics of computational job requests, e.g. job duration, utilisation and Cycles Per Instruction (CPI). We also apply an unsupervised learning algorithm on the metrics to characterise jobs. Our analysis suggests that the composition of the underlying machine resources in different cells can be substantially different, and the cells with similar machine resource structures can utilise resources differently depending on the characteristics of job requests.