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Scheduling based energy optimization technique in multiprocessor embedded systems

At the operating system level, multi-core and multiprocessor SoC(MPSoC) started a new computing era but brought various twofold scheduling challenges in current developed thermal aware algorithms for multi-core processors. An offline thermal aware scheduling algorithm is proposed for improvement in multi core embedded system in case of energy, reliability as well as performance of a multi core system has been introduced. Embedded systems are increasing at much rapid speed than ever before. The temperature of a multi-core processor is managed and measured by the hardware management system due to shrinking of chip size power densities are increasing due to this increase in the temperature of chip occurs that reduces the processor's speed in multi-core embedded system. High peak temperature on chip adversely affects the life span of chip. Task migration is a common technique to avoid peak temperature values in multi core system. Those tasks have been migrated in a multi core system which produces more heat to such individual core that has less temperature. The proposed technique also considers other thermal problems which affects the reliability and performance of multi-core system. In this research, a suitable scheduling mechanism assign tasks to the core that has less temperature by considering power and performance of the multi core system. This scheduling technique migrate load on the cores that is far away from the core reaches threshold temperature. For attaining stability in temperature among multiple cores results are evaluated by comparing different task migration techniques which are introduced previously. All types of hot and cold tasks are considered to predict accurate temperature by using thermal history. The scheduling policy attains maximum efficiency in terms of energy by considering only those cores that are executing some tasks in highest energy state such as running state while considering all other cores in lowest energy state such as sleep or a deep sleep mode. The simulation results shows that the proposed technique reduces almost 5°C temperature at 10% utilization and works efficiently when least number of tasks is in running state. The proposed technique has the ability to schedule more tasks to make a slower and energy efficient processor to control and manage the thermal effects on chip and also mange the processor energy consumption.