A new highly accurate workload model for campus email traffic

E-mail has become a de-facto means of communication. Mail servers try to manage the explosive growth of e-mail usage and offer users good quality of service, while spam e-mails are expected to account for 90% of the e-mail traffic. The exceedingly heavy workload can lead to the replacement of existing e-mail servers due to their inability to cope with performance standards and storing capacity. In this study, we focus on modeling the workload of the email servers of a medium-sized Greek university, for all types of traffic (user and system e-mails, as well as spam). We collected a vast amount of e-mail logs with high variations in terms of size and volume over time. We tested some of the most popular distributions for workload characterization and used powerful statistical tests to evaluate our findings. Interestingly we come to different conclusions in comparison with previous works in the field. Our work indicates that, with the exception of some outliers, campus email traffic can be modeled and predicted quite accurately.