Abstract
This paper describes, Scout, a statistical modeling driven approach to automatically recommend new Point of Presence (PoP) centers for web sites. PoPs help reduce a website’s page download time dramatically. However, where to build the new PoP centers given the current assets of existing ones is a problem that has rarely been studied in a quantitative and principled way before; it was mainly done through empirical studies or through applying industry experience and intuitions. In this paper, we propose a novel approach that estimates the impact of the PoP centers by building a statistical model using the real user monitoring data collected by the web sites and recommend the next PoPs to build. We also consider the problem of recommending PoPs using other metrics such as user’s number of page views. We show empirically that our approach works well, by experiments that use real data collected from millions of user visits in a major social network site.
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Notes
- 1.
Another strategy is to use CDNs to deliver dynamic content, but it is less common due to security, privacy and cost concerns.
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Acknowledgments
We are grateful to Samir R. Das for his valuable feedback on an earlier draft of this paper. We would also like to thank the anonymous reviewers for their insightful comments.
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Yang, Y., Zhang, L., Maheshwari, R., Kahn, Z.A., Agarwal, D., Dubey, S. (2016). Scout: A Point of Presence Recommendation System Using Real User Monitoring Data. In: Karagiannis, T., Dimitropoulos, X. (eds) Passive and Active Measurement. PAM 2016. Lecture Notes in Computer Science(), vol 9631. Springer, Cham. https://doi.org/10.1007/978-3-319-30505-9_16
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