Queueing Latency Aware Learning-based Online Computation Offloading for In-network Computing

This paper investigates the online scheduling problem of path selection and workload allocation for in-network computing. We formulate the problem as a mixed-integer programming model that jointly minimizes server operating cost and end-to-end latency, and prove that it is NP-hard. To enable efficient online decision-making, we further transform the problem into a Markov decision process. Based on this formulation, we propose a reinforcement learning–based scheduling algorithm using proximal policy optimization (PPO), which incorporates a Long Short-Term Memory (LSTM)-based temporal encoder to capture historical deployment information and dynamically evolving network states. This design allows the algorithm to make adaptive, queueing-aware scheduling decisions in dynamic network environments. Extensive simulation results demonstrate that the proposed algorithm is robust and consistently outperforms state-of-the-art baseline methods in terms of the joint cost–latency performance metric.