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When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption is expected to increase significantly. Then it will be more effective to limit the number of employed processors, and use a combination of DVS and processor shutdown. Scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results show that our approach reduces the total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS. We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.

When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption is expected to increase significantly. Then it will be more effective to limit the number of employed processors, and use a combination of DVS and processor shutdown. Scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results show that our approach reduces the total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS. We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.

When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption is expected to increase significantly. Then it will be more effective to limit the number of employed processors, and use a combination of DVS and processor shutdown. Scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results show that our approach reduces the total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS. We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.

When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption is expected to increase significantly. Then it will be more effective to limit the number of employed processors, and use a combination of DVS and processor shutdown. Scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results show that our approach reduces the total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS. We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.

When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption is expected to increase significantly. Then it will be more effective to limit the number of employed processors, and use a combination of DVS and processor shutdown. Scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results show that our approach reduces the total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS. We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.

When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption is expected to increase significantly. Then it will be more effective to limit the number of employed processors, and use a combination of DVS and processor shutdown. Scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results show that our approach reduces the total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS. We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.

When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption is expected to increase significantly. Then it will be more effective to limit the number of employed processors, and use a combination of DVS and processor shutdown. Scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results show that our approach reduces the total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS. We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.

When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption is expected to increase significantly. Then it will be more effective to limit the number of employed processors, and use a combination of DVS and processor shutdown. Scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results show that our approach reduces the total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS. We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.

When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption is expected to increase significantly. Then it will be more effective to limit the number of employed processors, and use a combination of DVS and processor shutdown. Scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results show that our approach reduces the total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS. We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.

When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption is expected to increase significantly. Then it will be more effective to limit the number of employed processors, and use a combination of DVS and processor shutdown. Scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results show that our approach reduces the total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS. We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.