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179 Clearly, most of these receptions would not occur in an actual Ostra deployment. However, we could not access the spam filter’s per-message junk mail tags, so we randomly removed 95% of the incoming messages as junk. To determine how often a given setting of L and U would affect Ostra, we simulated how messages in the email trace would be delayed due to the credit bounds. We ran two experiments with different assumptions about the average delay between the time when a message arrives and the time when the receiving user classifies the message. We first simulated casual email users who classify messages after six hours, and we then simulated heavy email users who classify messages after two hours. Table 8.3 presents the results of these two experiments with L=–3 and U=3. We found that messages are rarely delayed (less than 1.5% of the time in all cases), and that the average delay is on the order of a few hours. We also found that the delays for receiving messages are more significant than the delays for sending messages. We believe this is an artifact of our methodology. Over 98% of the delayed messages were received by just 3 users. In practice, it is likely that these users (who receive a high volume of relevant email) check and classify their email very frequently. This effect would reduce the frequency and magnitude of delays, but our simulation does not account for it.PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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