DECCo-A Dynamic Task Scheduling Framework for Heterogeneous Drone Edge Cluster

The heterogeneity of unmanned aerial vehicle (UAV) nodes and the dynamic service demands make task scheduling particularly complex in the drone edge cluster (DEC) scenario. In this paper, we provide a universal intelligent collaborative task scheduling framework, named DECCo, which schedules dynamically changing task requests for the heterogeneous DEC. Benefiting from the latest advances in deep reinforcement learning (DRL), DECCo autonomously learns task scheduling strategies with high response rates and low communication latency through a collaborative Advantage Actor-Critic algorithm, which avoids the interference of resource overload and local downtime while ensuring load balancing. To better adapt to the real drone collaborative scheduling scenario, DECCo switches between heuristic and DRL-based scheduling solutions based on real-time scheduling performance, thus avoiding suboptimal decisions that severely affect Quality of Service (QoS) and Quality of Experience (QoE). With flexible parameter control, DECCo can adapt to various task requests on drone edge clusters. Google Cluster Usage Traces are used to verify the effectiveness of DECCo. Therefore, our work represents a state-of-the-art method for task scheduling in the heterogeneous DEC.

Automated summary

In this paper, a universal intelligent collaborative task scheduling framework named DECCo is proposed to handle the complexity of task scheduling in the drone edge cluster (DEC). by utilizing deep reinforcement learning (DRL), DECCo autonomously learns task scheduling strategies with high response rates and low communication latency. DECCo switches between heuristic and DRL-based scheduling solutions based on real-time scheduling performance to avoid suboptimal decisions that affect Quality of Service (QoS) and Quality of Experience (QoE) in a real drone collaborative scheduling scenario.