A Hybrid Heuristic-Genetic Algorithm for Task Scheduling in Heterogeneous Multi-core System

Chuan, Wang; Gu, Jianhua; Wang, Yunlan; Zhao, Tianhai

doi:10.1007/978-3-642-33078-0_12

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…First the LDCP generate a high quality schedule that will be injected into the initial population of the GAS, and then the GAS proceeds to evolve shorter schedules. Wang et al [30] proposed a Hybrid Successor Concerned Heuristic-Genetic Scheduling (HSCGS) algorithm which incorporates a Successor Concerned List Heuristic Scheduling (SCLS) into a GA. The SCLS generates a high quality schedule using the up-ward rank that concerns the impact of a task's successor, and then the GA incorporate this schedule to produce new generations.…”

Section: ) Hybrid Metaheuristicsmentioning

confidence: 99%

Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment

He,

Liu

2023

IEEE Access

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At present, workflow scheduling in cloud computing environment is still a challenging optimization topic due to its NP-complete characteristics. In order to obtain better scheduling results, researchers are constantly coming up with new methods. In this study, we offer a hybrid metaheuristic for solving workflow scheduling in cloud to minimize the makespan of the workflow considering the heterogeneity of virtual resources. This hybrid approach combines the excellent optimization properties of Heterogeneous Earliest Finish Time (HEFT), Teaching-Learning-Based Optimization (TLBO), Opposition-Based Learning (OBL), and genetic manipulations, which is named Hybrid TLBO (HTLBO). Firstly, a HEFT-based method is proposed to produce the high-quality diverse initial population. Secondly, a Mixed OBL (MOBL) model is designed, in which the boundary search information and the population historical search information are systematically taken into account. Finally, an enhanced learner stage using genetic operations are added to effectively help the algorithm to jump out of the local optima. Rigorous experiments over various scientific workflows are conducted to validate HTLBO's performance. The obtained results are compared to HEFT and some state-of-the-art hybrid metaheuristics in terms of average makespan, running time and non-parametric statistics. A significant improvement in schedule quality demonstrates that HTLBO can increase population diversity and achieve a good balance between scheduling effectiveness and efficiency.

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Section: ) Hybrid Metaheuristicsmentioning

confidence: 99%

Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment

He,

Liu

2023

IEEE Access

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“…From the view of scheduling policies, static scheduling can be classified as search-based scheduling and heuristic-based scheduling. Search-based scheduling includes mathematical methods like ILP [19]- [23] or meta-heuristic algorithms such as Genetic Algorithm(GA) [24]- [26], Ant Colony Optimization(ACO) [27] and Particle Swarm Optimization(PSO) [28]. Among these algorithms, ILP-based methods have the advantages of reachable optimality, easy modeling, and easy access to various solving tools.…”

Section: Related Workmentioning

confidence: 99%

“…Well-known algorithms include HEFT [29], CPOP [29], PEFT [30], LDCP [31] and etc. Besides, there are works [24], [34] utilizing two or more above algorithms, which can exploit the advantages of several algorithms to achieve high-quality scheduling and optimize several performance metrics as well.…”

Section: Related Workmentioning

confidence: 99%

Fine-Grained Communication-Aware Task Scheduling Approach for Acyclic and Cyclic Applications on MPSoCs

Huang

Jiang

et al. 2019

IEEE Access

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Fine-grained task models can exploit parallelism to achieve high performance for multiprocessor system-on-chip (MPSoC). However, fine-grained models face the issues of high-communication overhead and difficult scheduling decisions, and the two challenges are interdependent. To address the issues, this paper gives a full analysis of the fine-grained communication optimization technique and communication pipeline, from both time and topology perspectives, and proposes a static fine-grained communication-aware task scheduling (FCATS) approach, which integrates scheduling with communication pipeline for acyclic and cyclic applications based on the fine-grained Simulink model. The approach contains search-based scheduling with high-quality solutions utilizing genetic algorithm-integer linear programming (GA-ILP) and hybrid GA-heuristic scheduling with short solving time to meet different demands for users. The experimental results with both synthetic and real-life benchmarks on the 4/8/16-CPU platform demonstrate the efficiency of the approach on performance improvements compared to previous works.

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“…In the second phase the good quality schedule generated by the above phase is fed into the genetic algorithm. The authors had proved that HSCGS give better results than HEFT and DLS (Dynamic Level Scheduling) [15] In 2013 Saeid Abrishami, Mahmoud Naghibzadeh et al proposed two algorithms for workflow scheduling based on the Partial Critical path to find the optimal solution in terms of minimal cost subject to the defined deadline constraints. IC-PCP (Iaas cloud partial critical path) tries to schedule the tasks on partial critical path by allocating them to the available instances of the service before its latest finish time.…”

Section: Related Workmentioning

confidence: 99%

Time Effective Workflow Scheduling using Genetic Algorithm in Cloud Computing

Nagar¹,

Gupta²,

Singh³

2018

IJITCS

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Abstract-Cloud computing is service based technology on internet which facilitates users to access plenty of resources on demand from anywhere and anytime in a metered manner i.e. pay per usage without paying much heed to the maintenance and implementation details of application. As cloud technology is evolving day by day it is being confronted by numerous challenges, such as time and cost under deadline constraints. Research work done so far mainly focused on reducing cost as well as execution time. In order to minimize cost and execution time previously existing workflow scheduling model known as predict earliest finish time is used. In this research work we have proposed a new PEFT genetic algorithm approach to further reduce the execution time on this model. A strategy is developed to let GA focus on to optimize chromosomes objective to get best suitable mutated children. After obtaining a feasible solution, the genetic algorithm focuses on optimizing the execution time. Experimental results show that our algorithm can find better solution within lesser time.

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A Hybrid Heuristic-Genetic Algorithm for Task Scheduling in Heterogeneous Multi-core System

Cited by 5 publications

References 15 publications

Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment

Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment

Fine-Grained Communication-Aware Task Scheduling Approach for Acyclic and Cyclic Applications on MPSoCs

Time Effective Workflow Scheduling using Genetic Algorithm in Cloud Computing

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