A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters

In this paper, a heuristic dynamic scheduling scheme for parallel real-time jobs executing on a heterogeneous cluster is presented. In our system model, parallel real-time jobs, which are modeled by directed acyclic graphs, arrive at a heterogeneous cluster following a Poisson process. A job is said to be feasible if all its tasks meet their respective deadlines. The scheduling algorithm proposed in this paper takes reliability measures into account, thereby enhancing the reliability of heterogeneous clusters without any additional hardware cost. To make scheduling results more realistic and precise, we incorporate scheduling and dispatching times into the proposed scheduling approach. An admission control mechanism is in place so that parallel real-time jobs whose deadlines cannot be guaranteed are rejected by the system. For experimental performance study, we have considered a real world application as well as synthetic workloads. Simulation results show that compared with existing scheduling algorithms in the literature, our scheduling algorithm reduces reliability cost by up to 71.4% (with an average of 63.7%) while improving schedulability over a spectrum of workload and system parameters. Furthermore, results suggest that shortening scheduling times leads to a higher guarantee ratio. Hence, if parallel scheduling algorithms are applied to shorten scheduling times, the performance of heterogeneous clusters will be further enhanced.