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A rich body of literature assembled over the last 30 years shows that traffic traversing wide-area Internet links is consistent with self-similar (temporal) scaling behavior and that sets of observed addresses have multifractal (spatial) scaling behavior. In view of this empirical evidence, these behaviors cannot be viewed as mere mathematical curiosities but should justifiably be called invariants of measured Internet traffic (Internet invariants, for short). At the same time, it is fair to say that the early architects of the Internet were largely unaware of these properties and certainly did not intend to design a network so that the traffic traversing its links would exhibit self-similar scaling in time or multifractal scaling in the IP address space. In this paper, we resolve this apparent disconnect between architectural intentions and observed behaviors by applying a three-part framework that leverages, at its core, the perspective of Highly Optimized Tolerance (HOT). In particular, we take inspiration from studies on the origins of (temporal) self-similarity in measured Internet traffic but focus on a fundamentally new approach to understanding multifractal (spatial) scaling behavior. Specifically, we examine whether this invariant can be viewed as a visible hallmark of underlying but largely unknown robust design efforts, and explore a reverse-engineering approach to determine the concrete nature of the constrained optimization problems that these robust designs solve. Based on the insights gained from such reverse-engineering efforts, we speculate on the benefits of future efforts at forward-engineering - systematically leveraging the identified robust designs in order to provide scientifically sound intellectual foundations and practical principles for designing future networked systems.