To celebrate the appearance of Volume 100 of the journal Queueing Systems, it has seemed appropriate to us to publish a special issue with 100 short notes by prominent and upcoming researchers in the field. In December 2021 we have invited a group of about 160 researchers to write such a note, choosing one of the following topics: (1) What is a very challenging problem that you are presently working on or on which you would like to work in the near future, and/or which in your view deserves a large research effort; present a problem sketch, indication of the main difficulties and a possible approach. (2) What is a problem on which you have worked in your career, about which you feel that it is not yet successfully completed? (3) What is a problem that you should have tackled (or devoted more time to) and now regret that you didn’t?

It was our firm belief that such a ‘100 views on queues’ collection may be very inspiring for a new generation of researchers in queueing theory and performance analysis, and could give a good overview of the present state of queueing and its main challenges.

The response to the invitation was extremely enthusiastic; within six weeks we received well over 100 contributions. Each note was refereed by one or two independent reviewers, and all of them went through one or two revisions. We are grateful to the referees who all provided valuable, often very insightful, reviews within a matter of days. The whole review process was thus completed on February 28, and 100 papers were selected for publication.

In addition, we decided to single out the beautiful contribution of Peter Glynn, titled ‘Queueing theory: past, present and future’, as the preamble. He suggests a global classification of queueing research directions which is very much in line with the grouping of papers we had in mind. The subsequent 100 notes are accordingly divided in three groups. Group 1 mainly considers descriptive queueing papers (50 notes, further subdivided in exact and asymptotic analysis), Group 2 mainly prescriptive ones (30 notes on optimization and control), and Group 3 mainly predictive ones (20 notes on, e.g., simulation, machine learning). Nothing is perfect, and our partition of the contributions definitely isn’t, but hopefully it helps readers a bit with the challenging task of navigating through 100 papers!

Finally, we would like to express our gratitude to the Springer support team led by Karthikeyan Sekaran for the way they have handled this exciting but rather unusual project, and to Matthew Amboy from Springer for his strong and enthusiastic support.