How will your workload look like in 6 years?: Analyzing Wikimedia's workload
2014 (English)In: Proceedings of the 2014 IEEE International Conference on Cloud Engineering (IC2E 2014) / [ed] Lisa O’Conner, IEEE Computer Society, 2014, 349-354 p.Conference paper (Refereed)
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%.
Place, publisher, year, edition, pages
IEEE Computer Society, 2014. 349-354 p.
, IEEE, ISSN 2373-3845
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Research subject Computing Science
IdentifiersURN: urn:nbn:se:umu:diva-87235DOI: 10.1109/IC2E.2014.50ISI: 000361018600043ISBN: 978-1-4799-3766-0OAI: oai:DiVA.org:umu-87235DiVA: diva2:707725
IC2E 2014, IEEE International Conference on Cloud Engineering, Boston, Massachusetts, 11-14 March 2014
FunderSwedish Research Council, C0590801eSSENCE - An eScience Collaboration