Scale applies policy-based deep reinforcement learning to SLO-aware container scheduling in serverless edge computing, achieving near-optimal results with drastically reduced decision time in simulations.
Freyr ++: Harvesting idle resources in serverless computing via deep reinforcement learning.IEEE Transactions on Parallel and Distributed Systems, 35(11):2254–2269
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Scale: Deep Reinforcement Learning for Container Scheduling in Serverless Edge Computing
Scale applies policy-based deep reinforcement learning to SLO-aware container scheduling in serverless edge computing, achieving near-optimal results with drastically reduced decision time in simulations.