DAG Scheduling and Analysis on Multi-Core Systems by Modelling Parallelism and Dependency

With ever more complex functionalities being implemented in emerging real-time applications, multi-core systems are demanded for high performance, with directed acyclic graphs (DAG) being used to model functional dependencies. For a single DAG task, our previous work presented a concurrent provider and consumer (CPC) model that captures the node-level dependency and parallelism, which are the two key factors of a DAG. Based on the CPC, scheduling and analysis methods were constructed to reduce makespan and tighten the analytical bound of the task. However, the CPC-based methods cannot support multi-DAGs as the interference between DAGs (i.e., inter-task interference) is not taken into account. To address this limitation, this article proposes a novel multi-DAG scheduling approach which specifies the number of cores a DAG can utilise so that it does not incur the inter-task interference. This is achieved by modelling and understanding the workload distribution of the DAG and the system. By avoiding the inter-task interference, the constructed schedule provides full compatibility for the CPC-based methods to be applied on each DAG and reduces the pessimism of the existing analysis. Experimental results show that the proposed multi-DAG method achieves an improvement up to 80% in schedulability against the original work that it extends, and outperforms the existing multi-DAG methods by up to 60% for tightening the interference.