Task Decomposition and Hierarchical Scheduling for Collaborative Cloud-Edge-End Computing

The emerging computing paradigms offer effective resolutions for the escalating conflict arising from the heightened computational demands of portable terminals and their constrained capacity. Concurrently, the architecture has transitioned from a single-tier structure to a multi-tier collaborative framework, enhancing flexibility and enabling fine-grained computation offloading. Nevertheless, existing research on multi-tier computation offloading faces challenges, including inefficient resource perception and task decomposition; there is a notable absence of an effective hierarchical task scheduling strategy within the multi-tier collaborative architecture. To bridge these gaps, our paper investigates the multi-granularity task decomposition and hierarchical task scheduling in a cloud-edge-end collaborative computing network. We first introduce a large-small resource tree (LST) model to facilitate efficient resource perception across three-tier network nodes. Then we propose a multi-granularity task decomposition algorithm (MTDA) based on long short-term memory (LSTM) network resource prediction to fully utilize the distributed node resources. Finally, we propose a parallelized LST-DDQN task offloading algorithm to maximize the delay and energy consumption weighted utility function. Simulation results demonstrate the efficacy of our proposed task decomposition and parallel scheduling methods, showcasing a reduction in utility by approximately 6.31% to 13.01% compared to baseline algorithms.