TMHD: Twin-Bridge Scheduling of Multi-Heterogeneous Dependent Tasks for Edge Computing

Abstract

As an efficient computing paradigm, Mobile Edge Computing (MEC) is essential in assisting mobile devices with real-time complex tasks such as big data analytics. In MEC, application tasks consist of multiple dependent subtasks, and the way to process tasks ensuring lower response latency through efficient scheduling orders is of relevant importance. Most existing research on scheduling sorts the dependent tasks out by simple topological ordering, which leads to idle and wasteful resources. For such, we propose a Twin-Bridge Scheduling of Multi-Heterogeneous Dependent Tasks for Edge Computing (TMHD), with two state embedding layers for multi-dependent tasks that reconstruct the model of dependent tasks to find the optimal scheduling paths and concurrently make the best offloading decisions. Considering the limitations of local edge-end model training, we upload the trained models to the cloud periodically for local model aggregation update, speeding up the model convergence whilst guaranteeing the training accuracy and demonstrating the convergence of multi-model aggregation. Compared with existing dependent task processing methods, TMHD reduces the average task processing time on four different datasets by 18.68 ms, 71.79 ms, 70.99 ms, and 64.32 ms, which are shown to be effective in reducing the response time of the device task.