Load-aware scheduling for heterogeneous multi-core systems.

Heterogeneous multi-core systems are becoming more and more common today. To be used to their full potential, the operating system has to be adapted to the new system environment. This is especially true for the scheduler as it is crucial to the overall system performance. In this paper, we present a scheduling approach for heterogeneous systems with two different kinds of cores. One that is very power efficient, but shows only a limited computing power, and the other one that has a very high performance and is very power consuming at the same time. We consider such heterogeneity for a centralized scheduler architecture. In our approach, we introduce a new load metric in order to classify tasks whether or not they are suited to be executed on a high-performance core. Based on this metric, we present a task state model for scheduling tasks according to their performance classification. We implemented the scheduling approach by extending the Brain Fuck Scheduler (BFS) and evaluated it on an eight core heterogeneous architecture with four low performance and four high-performance cores. The evaluation covers system responsiveness and high load behaviour compared to the vanilla BFS and the decentralized Completely Fair Scheduler (CFS). Even though our approach takes the heterogeneity into account, the results show that it scales better than the vanilla BFS while nearly maintaining its superior responsiveness.