Resource Management with Deep Reinforcement Learning - Mao, Hongzi; Alizadeh, Mohammad; Menache, Ishai; Kandula, Srikanth (HotNets-XV, Atlanta, GA, USA, 2016)
Resource management problems in systems and networking often manifest as difficult online decision making tasks where appropriate solutions depend on understanding the workload and environment. Inspired by recent advances in deep reinforcement learning for AI problems, we consider building systems that learn to manage resources directly from experience. We present DeepRM, an example solution that translates the problem of packing tasks with multiple resource demands into a learning problem. Our initial results show that DeepRM performs comparably to state-of-the-art heuristics, adapts to different conditions, converges quickly, and learns strategies that are sensible in hindsight.