Leveraging Website Popularity Differences to Identify Performance Anomalies
| Autor: | Chadi Barakat, Mark Crovella, Renata Teixeira, Giulio Grassi |
|---|---|
| Přispěvatelé: | Middleware on the Move (MIMOVE), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Design, Implementation and Analysis of Networking Architectures (DIANA), Inria Sophia Antipolis - Méditerranée (CRISAM), Computer Science Department [Boston] (Boston University), Boston University [Boston] (BU), Inria Project Lab BetterNet BetterNet, This work was supported by the French National ResearchAgency under grant BottleNet no. ANR-15-CE25-0013 and by Inria within the Project Lab BetterNet. Mark Crovella was supported by NSF grant CNS-1618207, and by grants from Inria Paris, U. Sorbonne-Pierre et Marie Curie LIP6, and the Laboratory of Information, Networking and Communication Sciences (LINCS), ANR-15-CE25-0013,BottleNet,Comprendre et diagnostiquer les dégradations des communications de bout en bout dans l'Internet(2015) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Information retrieval
Website monitoring business.industry Computer science Matrix factorization Missing data computer.software_genre Network measurement [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] User experience design Web performance anomalies Leverage (statistics) Anomaly detection Web performance Noise (video) Web service business computer |
| Zdroj: | INFOCOM 2021-IEEE International Conference on Computer Communications INFOCOM 2021-IEEE International Conference on Computer Communications, May 2021, Vancouver / Virtual, Canada. ⟨10.1109/INFOCOM42981.2021.9488832⟩ INFOCOM HAL |
| DOI: | 10.1109/INFOCOM42981.2021.9488832⟩ |
| Popis: | International audience; Web performance anomalies (e.g. time periods when metrics like page load time are abnormally high) have significant impact on user experience and revenues of web service providers. Existing methods to automatically detect web performance anomalies focus on popular websites (e.g. with tens of thousands of visits per minute). Across a wider diversity of websites, however, the number of visits per hour varies enormously, and some sites will only have few visits per hour. Low rates of visits create measurement gaps and noise that prevent the use of existing methods. This paper develops WMF, a web performance anomaly detection method applicable across a range of websites with highly variable measurement volume. To demonstrate our method, we leverage data from a website monitoring company, which allows us to leverage cross-site measurements. WMF uses matrix factorization to mine patterns that emerge from a subset of the websites to "fill in" missing data on other websites. Our validation using both a controlled website and synthetic anomalies shows that WMF's F1-score is more than double that of the state-of-the-art method. We then apply WMF to three months of web performance measurements to shed light on performance anomalies across a variety of 125 small to medium websites. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|