to turn data into [*information*]{}. In our case, data come from real-world measurements, and the information we seek has to do with next-generation networks based on MEC. In spite of all their (perceived) abundance and the (apparent) easiness with which they are processed, real-world measurements can only deal with present-day systems and, thus, they are not naturally well-suited to study future ones. In this paper, we demonstrate how this limitation can be overcome with the help of additional data, real-world experiments, and domain knowledge.

The remainder of the paper is organized as follows. We begin by reviewing related work in [Sec. \[sec:relwork\]]{}. Then we present the real-world demand traces we use as a starting point in [Sec. \[sec:traces\]]{}, and describe the experiments we perform to enrich them in [Sec. \[sec:enrich\]]{}. [Sec. \[sec:design\]]{} introduces and discusses our MEC network design strategy. After presenting our numerical results in [Sec. \[sec:results\]]{}, we conclude the paper in [Sec. \[sec:conclusion\]]{}.

Related work {#sec:relwork}
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Our study is connected to three main categories of prior work: papers presenting real-world mobile traces and datasets; studies addressing MEC in general and MEC-based 5G networks specifically; works doing the latter using the first.

Many real-world traces come from volunteers, 