Title :
How will Your Workload Look Like in 6 Years? Analyzing Wikimedia´s Workload
Author :
Eldin, Ahmed Ali ; Rezaie, Ali ; Mehta, A. ; Razroev, Stanislav ; Sjöstedt-de Luna, Sara ; Seleznjev, Oleg ; Tordsson, Johan ; Elmroth, Erik
Author_Institution :
Dept. of Comput. Sci., Umea Univ., Umeå, Sweden
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%.
Keywords :
Web sites; cloud computing; mean square error methods; resource allocation; splines (mathematics); time series; Web sites; Wikimedia workload analysis; cloud resource management; large-scale applications; mean absolute percentage error; page popularity; polynomial splines; short term prediction algorithm; time-series analysis; Aggregates; Electronic publishing; Encyclopedias; Internet; Market research; Servers;
Conference_Titel :
Cloud Engineering (IC2E), 2014 IEEE International Conference on
Conference_Location :
Boston, MA
DOI :
10.1109/IC2E.2014.50