A. Ali-Eldin, A. Rezaie, Amardeep Mehta, Stanislav Razroev, S. S. Luna, O. Seleznjev, Johan Tordsson, E. Elmroth
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引用次数: 39
Abstract
Accurate understanding of workloads is key to efficient cloud resource management as well as to the design of large-scale applications. We analyze and model the workload of Wikipedia, one of the world's largest web sites. With descriptive statistics, time-series analysis, and polynomial splines, we study the trend and seasonality of the workload, its evolution over the years, and also investigate patterns in page popularity. Our results indicate that the workload is highly predictable with a strong seasonality. Our short term prediction algorithm is able to predict the workload with a Mean Absolute Percentage Error of around 2%.
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