Energy-Efficient and Reliable Task Mapping and Offloading for Multicore Edge Devices with DVFS

Multicore platforms based on NoC are promising architectures for safety-critical applications. Application execution performance is determined by task mapping, with reliable execution, real-time response, and energy efficiency as requirements. We can perform task duplication, DVFS, and multipath routing to meet these requirements during task mapping. Furthermore, the computation platforms have limited computation capacity and energy supply in several application domains. Some complex tasks can be offloaded from the edge device to the cloud for execution. However, such task offloading influences task mapping on the edge device. Existing approaches seldom consider the correlation of task offloading to the cloud and task mapping on the edge device. To address this limitation, we jointly consider task mapping inside the NoC-based multicore edge device and task offloading to the cloud to optimize energy consumption while satisfying reliability and real-time constraints. This problem is formulated as a mixed-integer nonlinear programming and linearized to find the optimal solution. We propose a novel three-step heuristic with a feedback mechanism to enhance task schedulability and reduce computation time. We evaluate the behavior of our approaches through exhaustive simulations. The results show that our approaches outperform existing methods in terms of energy efficiency, task reliability, and schedulability.