Analyzing Traces from a Google Data Center.

Summary Traces collected from an operational Google data center over 29 days represent a very rich and useful source of information for understanding the main features of a data center. In this paper, we characterize the strong heterogeneity of jobs and the medium heterogeneity of machine configurations. We analyze the off-periods of machines. We study the distribution of jobs per category, per scheduling class, per priority and per number of tasks. The distribution of job execution durations shows a high disparity, as does the job waiting time before being scheduled. The resource requests in terms of CPU and memory are also analyzed. The distribution of these parameter values is very useful to develop accurate models and algorithms for resource allocation in data centers.