An Empirical Failure-Analysis of a Large-Scale Cloud Computing Environment

Abstract

Cloud computing research is in great need of statistical parameters derived from the analysis of real-world systems. One aspect of this is the failure characteristics of Cloud environments composed of workloads and servers, currently, few metrics are available that quantify failure and repair times of workloads and servers at a large-scale. Workload metrics in particular are critical for characterizing and modeling accurate workload behavior, enabling more realistic workload simulation and failure scenarios of systems. This paper presents the analysis of failure data of a large-scale production Cloud environment (consisting of over 12,500 servers), and includes a study of failure and repair times and characteristics for both Cloud workloads and servers. Our results show that failure characteristics for workload and servers are highly variable and that production Cloud workloads can be accurately modeled by a Gamma distribution. Repair times range between 30 seconds to 4 days, and 25 minutes to 8 days, for workloads and servers respectively.