Adaptive scheduling on performance asymmetric multicore processors

Existing scheduling algorithms for performance asymmetric multicore systems either have low throughput or have bad scalability. Though scalability is considered in some algorithms, it is only confined to the number of cores, ignoring the scalability with respect to the number of tasks. To address these problems, an adaptive scheduling algorithm for performance asymmetric multicore systems, called AS4AMS, is proposed. By analyzing tasks' average stall time, AS4AMS obtains tasks' computing requirements, and then tasks are assigned to appropriate cores according to both the requirements of the tasks and the load of the cores. In addition, the above procedure is repeated to accommodate phase changes of tasks. Our experiment results show that as compared to existing algorithms, the newly proposed method delivers both higher scalability and greater throughput.