Web server workload characterization: the search for invariants
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Measuring and characterizing end-to-end Internet service performance
ACM Transactions on Internet Technology (TOIT)
Gaming On and Off the Social Graph: The Social Structure of Facebook Games
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Network level footprints of facebook applications
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Sampling bias in BitTorrent measurements
EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
Characterizing and modelling popularity of user-generated videos
Performance Evaluation
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Popular online social applications hosted by social platforms serve, each, millions of interconnected users. Understanding the workloads of these applications is key in improving the management of their performance and costs. In this work, we analyse traces gathered over a period of thirty-one months for hundreds of Facebook applications. We characterize the popularity of applications, which describes how applications attract users, and the evolution pattern, which describes how the number of users changes over the lifetime of an application. We further model both application popularity and evolution, and validate our model statistically, by fitting five probability distributions to empirical data for each of the model variables. Among the results, we find that most applications reach their maximum number of users within a third of their lifetime, and that the lognormal distribution provides the best fit for the popularity distribution.