Inter-domain Traffic Engineering for multi-homed networks faces a scalability challenge, as the size of BGP routing table continue to grow. In this context, the choice of the best path must be made potentially for each destination prefix, requiring all available paths to be characterized (e.g., through measurements) and compared with each other. Fortunately, it is well-known that a few number of prefixes could carry a dominant part of the traffic. As a natural consequence, to engineer a majority of traffic only a handful of prefixes need to be managed. Yet, traffic characteristics of a given prefix can vary greatly over time, which requires us to predict most significant prefixes in the near future. Moreover, little is known on the traffic dynamism at this aggregation level and sophisticated prediction methods won’t scale in such context. In this paper, we study the relationship between prefix volume, stability, and predictability, based on recent traffic traces from nine different networks. Three simple and resource-efficient methods to se- lect the prefixes associated with the most important foreseeable traffic volume are then proposed. Such proposed methods are capable of select sets of prefixes with both excellent representativeness (volume cover- age) and stability in time, for which the best routes are identified. The analysis carried out confirms the potential benefits of a route decision engine.