Energy-cognizant scheduling for preference-oriented fixed-priority real-time tasks

Low Power Scheduling Approach for Heterogeneous System Based on Heuristic and Greedy Method

“…Li et al [ 23 ] point out the deficiency of researches on the energy and thermal issues of real-time applications with precedence-constrained tasks on heterogeneous systems and then propose both energy/thermal-aware task scheduling approach by assigning tasks in an energy/thermal-aware heuristic way and reducing the waiting time between parallel tasks. Bansal et al [ 24 ] combine both the dynamic voltage scaling (DVS) and dynamic power management (DPM) techniques to save energy while scheduling preference-oriented fixed-priority periodic real-time tasks and then propose preference-oriented energy-aware rate-monotonic scheduling and preference-oriented extended energy-aware rate-monotonic scheduling algorithms to maximize energy savings while fulfilling preference value of tasks. Silberstein and Maruyama [ 25 ] have considered the energy of tasks on each processor, and they construct a minimum energy consumption scheduling method for multiple interdependent tasks according to the directed acyclic graph and verify the feasibility of the method when the processor has no overhead.…”

“…Energytemp_K_GPUIndex ⟵ AccumPer_GPUIndex1 * the energy of task number ptm_K_GPUIndex (22) end if (23) end if (24) // e above calculation traverses K_GPUIndex array (25) for i ⟵ 1, NumGPU − 1 do//Select the minimum product (26) if Energytemp_i < MinEnergy (27) assign the GPU number i to Return_GPUIndex (28) assign the Energytemp_i to MinEnergy (29) end if (30) end for (31) if Return_TempGPUIndex is from array (32) assign 0 to flag_ Return_TempGPUIndex (33) return Return_GPUIndex, pth_Return_GPUIndex, 0 (34) else (35) return Return_GPUIndex, ptm_Return_GPUIndex, 1 (36) end if ALGORITHM 3: Getting the task and GPU number with the smallest product of accumulated time and energy of its task.…”