Dynamic real-time scheduler for large-scale MPSoCs

Large-scale MPSoCs requires a scalable and dynamic real-time (RT) task scheduler, able to handle non-deterministic computational behaviors. Current proposals for MPSoCs have limitations, as lack of scalability, complex static steps, validation with abstract models, or are not flexible to enable changes at runtime of the RT constraints. This work proposes a hierarchical task scheduler with monitoring features. The scheduler is dynamic, supporting changes in RT constraints at runtime. An API enables these features allowing to the application developer to reconfigure the tasks' period, deadline, and execution time by annotating the task code. At runtime, according to the task execution, the scheduler handles the API calls and adjust itself to ensure RT guarantees according to the new constraints. Scalability is ensured by dividing the scheduler into two hierarchical levels: LS (Local Schedulers), and CS (Cluster Schedulers). The LS runs at the processor level, using the LST (Least Slack-Time) algorithm. The CS runs at the cluster level, i.e., a group of processors controlled by a manager processor. The CS receives messages from the LSs, informing the processor slack-time, deadline violations, and RT changes. The CS implements an RT adaptation heuristic, triggering task migrations according to RT reconfiguration or deadline misses. Results show a negligible overhead in the applications' execution time and the fulfillment of the applications' RT constraints even with a high degree of resources sharing, in both processors and NoC.