Abstract

The large scale of the Internet has offered unique economic opportunities, that in turn introduce overwhelming challenges for development and operations to provide reliable and fast services in order to meet the high demands on the performance of online services. In this paper, we investigate how performance engineers can identify three different classes of externally-visible performance problems (global delays, partial delays, periodic delays) from concrete traces. We develop a simulation model based on a taxonomy of root causes in server performance degradation. Within an experimental setup, we obtain results through synthetic monitoring of a target Web service, and observe changes in Web performance over time through exploratory visual analysis and changepoint detection. We extend our analysis and apply our methods to real-user monitoring (RUM) data. In a use case study, we discuss how our underlying model can be applied to real performance data gathered from a multinational, high-traffic website in the financial sector. Finally, we interpret our findings and discuss various challenges and pitfalls.

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