Research on Low Power Scheduling of Heterogeneous Multi Core Mission Based on Genetic Algorithm
Kai Zhu, Yun Ding · 2017
In recent years, the problem of energy consumption is becoming more and more serious. Multi core processor system, which can effectively control the operation speed and power consumption of the process engine, has become one of the effective ways to reduce the energy consumption of the system. Low power task scheduling based on heterogeneous multi-core system is a typical NP complete problem. Aiming at the existing poor operating efficiency of the algorithm is low and the energy saving effects of the problem, this paper proposes an improved heterogeneous multi-core low-power scheduling strategy. An improved genetic algorithm is proposed for the task partitioning strategy of heterogeneous multi-core low power scheduling algorithms. A low power scheduling algorithm based on key task analysis is proposed in this paper, which is based on the analysis of the current popular dynamic voltage scaling technology. Study on the influence of key task of real-time tasks is first analyzed in this paper, the task allocation strategy under a given priority task node, timeliness and urgency, in order to meet the deadline requirements, in order to minimize the energy consumption of the system as the goal, adjust the voltage level of the task execution unit. The experimental results show that the task partitioning strategy for heterogeneous multi-core low-power scheduling algorithm to expand the solution space of the problem, provide a more extensive task allocation scheme for low power scheduling, low power scheduling algorithm to effectively reduce the energy consumption of the system significantly reduces the time complexity of the algorithm.