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Strategic Competition in Oligopolies with Fluctuating Demand
Leslie Neubecker
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Business Strategy/Leadership; Organization; Industrial Organization; Microeconomics
Disponibilidad
Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
---|---|---|---|---|
No detectada | 2006 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-3-540-29556-3
ISBN electrónico
978-3-540-29557-0
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2006
Información sobre derechos de publicación
© Springer-Verlag Berlin Heidelberg 2006
Cobertura temática
Tabla de contenidos
Introduction
Leslie Neubecker
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.
Pp. 1-10
The State of the Research
Leslie Neubecker
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.
Pp. 11-42
Empirical Evidence on Long-Term Competition
Leslie Neubecker
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.
Pp. 43-67
Competition with Fluctuating Demand
Leslie Neubecker
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.
Pp. 69-114
Strategic Investment with Fluctuating Demand
Leslie Neubecker
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.
Pp. 115-153
Strategic Financing with Fluctuating Demand
Leslie Neubecker
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.
Pp. 155-178
Strategic Management Compensation with Fluctuating Demand
Leslie Neubecker
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.
Pp. 179-203
Discussion and Summary
Leslie Neubecker
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.
Pp. 205-211