A Reinforcement Learning Scheme for Adaptive Link Allocation in ATM Networks
Ernst Nordström, Jakob Carlström · 1999
This paper presents an adaptive scheme for a sub-function in Asynchronous Transfer Mode (ATM) network routing, called link allocation. The scheme adapts the link allocation policy to the offered Poisson call traffic such that the long-term revenue in maximized. It decomposes the link allocation task into aset of link admission control (LAC) tasks,formulatedas semi-MarkovDecision Problems (SMDPs). The LAC policies are directly adapted by reinforcement learning. Simulations show that the direct adaptive SMDP scheme outperforms static methods, which maximize the short-term revenue. It also yields a long-term revenue comparable to an indirect adaptive SMDP method.