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

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 multiproces...

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Vydáno v:Cluster computing Ročník 27; číslo 10; s. 13435 - 13453
Hlavní autoři: Liu, Junnan, Liu, Yifan, Ding, Yongkang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.12.2024
Springer Nature B.V
Témata:
Accuracy
Algorithms
Completion time
Computer Communication Networks
Computer Science
Computing time
Energy consumption
Energy efficiency
Failure
Failure rates
High performance systems
Machine learning
Microprocessors
Multiprocessing
Operating Systems
Optimization
Optimization algorithms
Performance enhancement
Power management
Processor Architectures
Scheduling
System reliability
Task scheduling
Voltage reduction
Task scheduling
Swarm intelligence optimization algorithms
High performance computing
Heterogeneous multi-core processors
Distributed computing
ISSN:1386-7857, 1573-7543
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Shrnutí: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.
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-024-04606-0