An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities on-chip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of the chip. In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first (EA-EDF) scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip (SOC). Dynamic power management (DPM) enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration. Task migration avoids peak temperature values in the multi-core system. High utilization factor ( on central processing unit (CPU) core consumes more energy and increases the temperature on-chip. Our technique switches the core by migrating such task to a core that has less temperature and is in a low power state. The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core. The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works. The simulation results show the improvement in performance by optimizing 4.8% on 9%, 16%, 23% and 25% at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.