DECOFFEE applies decentralized reinforcement learning with DQN and LSTM to jointly optimize delay, energy, and drop rate for workload offloading in edge-cloud systems, outperforming rule-based and heuristic baselines in simulations.
PDPPnet: Prioritized Delay-aware and Peer-to- Peer Task Offloading in Cloud-Edge Continuum with Double Dueling Deep Q-Networks
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DECOFFEE: Decentralized Reinforcement Learning for Time-critical Workload Offloading and Energy Efficiency across the Computing Continuum
DECOFFEE applies decentralized reinforcement learning with DQN and LSTM to jointly optimize delay, energy, and drop rate for workload offloading in edge-cloud systems, outperforming rule-based and heuristic baselines in simulations.