Multicore real-time task allocation algorithms with shared resource constraints

The task allocation algorithm was analyzed under partitioned fixed priority scheduling policy in order to increase the efficiency of task allocation in multicore real-time systems. The impact of blockings between tasks on different cores on the worst case response time of tasks was analyzed, and a task allocation failure problem incurred by resource sharing conflicts was pointed out. Load-unbalancing algorithms like first-fit and best-fit can easily trigger such task allocation problem. A grouping and load-balancing based task allocation algorithm was proposed in order to avoid such problem. The proposed algorithm preferentially co-locates tasks that may incur resource sharing conflicts to avoid blocking between tasks on different cores, and allocates the tasks that can not be allocated to the same core to the lightest-loaded core to avoid task allocation failure. Schedulability experiments show that the proposed algorithm can avoid task allocation failure and reduce the number of cores needed for task allocation (about as less as 10%~40% than that of the worst-fit algorithm needed).