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Most existing criteria , ,  for sizing router buffers rely on explicit formulation of the relationship between buffer size and characteristics of Internet traffic. However, this is a non-trivial, if not impossible, task given that the number of flows, their individual RTTs, and congestion control methods, as well as flow responsiveness, are unknown. In this paper, we undertake a completely different approach that uses control- theoretic buffer-size tuning in response to traffic dynamics. Motivated by the monotonic relationship between buffer size and loss rate and utilization, we design a mechanism called Adaptive Buffer Sizing (ABS), which is composed of two Integral controllers for dynamic buffer adjustment and two gradient-based components for intelligent parameter training. We demonstrate via ns2 simulations that ABS successfully stabilizes the buffer size at its minimum value under given constraints, scales to a wide spectrum of flow populations and link capacities, exhibits fast convergence rate and stable dynamics in various network settings, and is robust to load changes and generic Internet traffic (including FTP, HTTP, and non-TCP flows). All of these demonstrate that ABS is a promising mechanism for tomorrow's router infrastructure and may be of significant interest for the ongoing collaborative research and development efforts (e.g., GENI and FIND) in reinventing the Internet.