Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  investigate the problem of scheduling a set of periodic conditional task graphs of non-preemtible tasks on a MPSoCs (Multi Processor System-on-Chip) with shared memory such that the total expected processor energy consumption of the tasks in each scenario is minimized under two por models, namely dynamic and static por model, and propose a novel offline scheduling approach. Our approach consists of a novel two-phase task scheduler that aims at minimizing total worst-case utilization of each processor and an optimal task execution speed selection algorithm using convex NLP (Non-Linear Programming). Furthermore,  propose an O(1) time online DVS (Dynamic Voltage Scaling) heuristic that assigns each task a speed online. Our experimental results show that our two-phase scheduler achieves a 95.2% success rate of constructing a feasible schedule, compared to a 42% success rate of the state-of-the-art. For energy saving, our offline scheduling approach achieves an average improvement, a maximum improvement and a minimum improvement of 8.03%, 14.08%, and 4.8%, respectively over our online DVS heuristic.
['Umair Ullah Tariq', 'Hui Wu']
I am writing a research paper on a idea Energy-Aware Scheduling of Periodic Conditional Task Graphs on MPSoCs
[{"from": "human", "value": "I am writing a research paper on a idea Energy-Aware Scheduling of Periodic Conditional Task Graphs on MPSoCs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  investigate the problem of scheduling a set of periodic conditional task graphs of non-preemtible tasks on a MPSoCs (Multi Processor System-on-Chip) with shared memory such that the total expected processor energy consumption of the tasks in each scenario is minimized under two por models, namely dynamic and static por model, and propose a novel offline scheduling approach. Our approach consists of a novel two-phase task scheduler that aims at minimizing total worst-case utilization of each processor and an optimal task execution speed selection algorithm using convex NLP (Non-Linear Programming). Furthermore,  propose an O(1) time online DVS (Dynamic Voltage Scaling) heuristic that assigns each task a speed online. Our experimental results show that our two-phase scheduler achieves a 95.2% success rate of constructing a feasible schedule, compared to a 42% success rate of the state-of-the-art. For energy saving, our offline scheduling approach achieves an average improvement, a maximum improvement and a minimum improvement of 8.03%, 14.08%, and 4.8%, respectively over our online DVS heuristic."}]