Adaptive Local Assignment Algorithm for Scheduling Soft-Aperiodic Tasks on Multiprocessors

With the emergence of multiprocessors, embedded systems are nowadays capable of handling diverse and complicated applications. Besides static workloads such as periodic tasks, dynamic workloads such as aperiodic tasks happen more frequently and become challenging to researchers to deal with. In this study, an effective scheduling approach for aperiodic tasks in multiprocessors is presented. An adaptive Local Assignment Algorithm with the integration of servers is introduced to schedule mixture systems of periodic and aperiodic tasks. Servers are dedicatedly preserved for aperiodic tasks at runtime. This scheduling scheme guarantees the schedulability of 100% without significantly increasing the time complexity. Simulation results show that the proposed mechanism effectively improves the responsiveness of aperiodic tasks while maintaining relatively low runtime overhead. In addition, it causes much fewer scheduler invocations in comparison with the existing algorithms.