Analyzing real cluster data for formulating allocation algorithms in cloud platforms

• We analyze a large cluster trace released by Google.
• We provide information about static and dynamic features of dominant jobs.
• We show that memory usage of tasks is independent of CPU usage for most jobs.
• We analyze the independence of machine failures.
• Based on this analysis, we propose several algorithmic formulations for allocation problems.

A problem commonly faced in Computer Science research is the lack of real usage data that can be used for the validation of algorithms. This situation is particularly true and crucial in Cloud Computing. The privacy of data managed by commercial Cloud infrastructures, together with their massive scale, makes them very uncommon to be available to the research community. Due to their scale, when designing resource allocation algorithms for Cloud infrastructures, many assumptions must be made in order to make the problem tractable.This paper provides deep analysis of a cluster data trace recently released by Google and focuses on a number of questions which have not been addressed in previous studies. In particular, we describe the characteristics of job resource usage in terms of dynamics (how it varies with time), of correlation between jobs (identify daily and/or weekly patterns), and correlation inside jobs between the different resources (dependence of memory usage on CPU usage). From this analysis, we propose a way to formalize the allocation problem on such platforms, which encompasses most job features from the trace with a small set of parameters.