Searching for directions is one of the most used features of map applications. This paper shares our vision on how Direction Services will change, with LLM-based chat assistants rapidly becoming an integral part of the underlying path search mechanism. We anticipate an influx of more complex, conversational route planning sessions, where users colloquially describe route-related preferences as if they were talking to their personal chauffeur. We envision future systems able to support asks like "avoid the East River tunnel", "take the bridge", or "find me a scenic route around the lake, oh and by the way, I'm driving the EV today". At present, popular map search engines fail in even simple, yet very natural preferences, such as 'take me from A to B via road C'. The reason is mainly twofold, inadequate query understanding and lack of mechanisms in routing to satisfy this type of preferences. The here proposed solution is a novel treatment of routing, one which casts it into an end-to-end 2-layer relevance framework. The framework is capable of performing query understanding for route queries with complex preferences and intents. It treats routes as richly annotated documents and the routing engine, in addition to performing optimization, acts (1) as a retriever of route documents that match the user intent and (2) as a ranker that ranks route candidates not just by a simple time-distance cost model, but by inferring the importance of many variables, some derived from explicitly stated preferences and others identified as relevant through data-driven methodology.

ISPs routinely rate-limit user traffic. In addition to correctly enforcing the desired rates, rate-limiting mechanisms must be able to support rich rate-sharing policies within each traffic aggregate (e.g. per-flow fairness, weighted fairness, and prioritization). This must be done at scale to support the vast magnitude of users efficiently. There are two primary rate-limiting mechanisms - traffic shaping (that buffers packets in queues to enforce the desired rates and policies) and traffic policing (that filters packets as per the desired rates without buffering them). Policers are lightweight and scalable but don't support rich policy enforcement and often provide poor rate enforcement (being notoriously hard to configure). Shapers, on the other hand, achieve desired rates and policies, but at the cost of high system resource (memory and CPU) utilization impacting scalability. This paper explores whether we can get the best of both worlds. We present our system BC-PQP, which augments a policer with (i) multiple phantom queues that simulate buffer occupancy using counters and enable rich policy enforcement, and (ii) a novel burst-control mechanism that enables auto-configuration of the queues for correct rate enforcement. Our system achieves the rate and policy enforcement properties close to that of a shaper with 7× higher efficiency.