Multi-task end-edge offloading based on Lyapunov optimization and deep reinforcement learning

To enable collaborative processing of heterogeneous industrial tasks in the scenario with multiple devices and multiple edge servers, this paper proposes a multi-task end-edge offloading algorithm based on Lyapunov optimization and deep reinforcement learning. First, to jointly optimize task offloading decision, offloading ratio and transmit power, a long-term average system overhead minimization problem is formulated with full consideration of computing frequency, transmission power, long-term energy consumption and task deadline. As variables are coupled among different time slots in the long-term objective and constraints, the problem is difficult to solve. Thus, the long-term average system overhead minimization problem is decoupled into some independent time-slot optimization problems based on the Lyapunov optimization theory. By Markov decision process modelling and employing a double dueling deep neural network architecture, a deep reinforcement learning-based multi-task offloading algorithm is proposed. Experiments show that the proposed algorithm can converge stably, and can effectively reduce the long-term average system overhead under long-term energy consumption constraints and task deadline requirements. © 2024 Northeast University. All rights reserved.