Research and optimization of task scheduling algorithm based on heterogeneous multi-core processor

Junnan Liu

doi:10.1007/s10586-024-04606-0

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Research and optimization of task scheduling algorithm based on heterogeneous multi-core processor

2024-06-27

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Junnan Liu *

Yifan Liu

Yongkang Ding

DOI：10.1007/s10586-024-04606-0

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Abstract

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Heterogeneous multi-core processor has the ability to switch between different types of cores to perform tasks, which provides more space and possibility for realizing efficient operation of computer system and improving computer computing power. Current research focuses on heterogeneous multiprocessor systems with high performance or low power consumption to reduce system energy consumption. However, some studies have shown that excessive voltage reduction may lead to an increase in transient failure rates, reducing system reliability. This paper studies the energy optimal scheduling problem of HMSS with DVFS under the constraints of minimum time and reliability, and proposes an improved wild horse optimization algorithm (OIWHO), which improves the efficiency of heterogeneous task scheduling and shortens the task completion time. The algorithm uses the learning and chaos perturbation strategies based on opposition and crossover strategies to balance the search and utilization capabilities, and can further improve the performance of OIWHO. Compared with previous work, our proposed algorithm has more advantages than existing algorithms. Experimental results show that the average computing time of OIWHO algorithm is 12.58%, 11.42%, 7.53%, 4.20% and 3.21% faster than DRNN-BWO, PSO, GWO-GA, GACSH and OIWOAH, respectively. Especially when solving large-scale problems, our algorithm takes less time than other algorithms.

Keywords:

Distributed computing

Heterogeneous multi-core processors

High performance computing

Task scheduling

Swarm intelligence optimization algorithms