A genetic algorithm-based method for optimizing the energy consumption and performance of multiprocessor systems

Pillai, Anju S.; Singh, Kaumudi; Saravanan, Vijayalakshmi; Anpalagan, Alagan; Woungang, Isaac; Barolli, Leonard

doi:10.1007/s00500-017-2789-y

Cited by 32 publications

(9 citation statements)

References 27 publications

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“…It is also possible to observe the increase, in recent years, in the number of works that consider metrics focused on sustainability [36,48,50,56,60,62]. These works deal with issues related to the temperature and the energy consumption of computers (processors).…”

Section: Optimization Criteriamentioning

confidence: 99%

A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling

Silva

Gabriel

2020

Computation

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The multiprocessor task scheduling problem has received considerable attention over the last three decades. In this context, a wide range of studies focuses on the design of evolutionary algorithms. These papers deal with many topics, such as task characteristics, environmental heterogeneity, and optimization criteria. To classify the academic production in this research field, we present here a systematic literature review for the directed acyclic graph (DAG) scheduling, that is, when tasks are modeled through a directed acyclic graph. Based on the survey of 56 works, we provide a panorama about the last 30 years of research in this field. From the analyzes of the selected studies, we found a diversity of application domains and mapped their main contributions.

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Section: Optimization Criteriamentioning

confidence: 99%

A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling

Silva

Gabriel

2020

Computation

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“… Centralization: Centralization refers to shifting of applications, storage, and infrastructure to cloud where all computing relevant software sans applications are shifted to central server in order to minimize cost and make efficient use of resources [3,4].…”

Section: Characteristics Of Cloud Computingmentioning

confidence: 99%

Implementing and Evaluating Impact of Task Scheduling in Cloud Environment

2019

International Journal of Research Studies in Computer Science a

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Cloud computing has found its widespread use because of the several provisions it has enabled and made easier in technology sector. The research paper is intended to analyze and implement the three prominent task scheduling algorithms (FCFS, SJF, RR) which plays critical role in cloud computing. The more efficiently the processes are organized for execution on remote servers, the more optimal gets the performance of cloud setup. The paper elaborated the performance of three task scheduling algorithms under different scenarios and an effective conclusion has been reached.

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“…The representative task scheduling scheme on heterogeneous multi-processors is more complex. The performance parameters, such as execution efficiency and total time of completion, need to be considered [4][5][6][7]. Research on task scheduling strategies for heterogeneous multi-processors has drawn much attention in recent years [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Task scheduling algorithm based on fireworks algorithm

Tian

Zhang

et al. 2018

J Wireless Com Network

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To give full play to the high efficiency and parallelism of multi-processor systems, the fireworks algorithm (FWA) is improved, and a multi-processor task scheduling algorithm based on improved FWA, named IMFWA, is proposed. IMFWA maps continuous space to discrete space by designing the fireworks location coding method, improves the Gaussian mutation process, and sets adaptive dimensions to accelerate the convergence speed of the algorithm. At the same time, in order to reduce the time complexity of the algorithm and shorten the time finding the optimal task scheduling sequence, the fitness-based tournament selection strategy is used instead of the rule based on Euclidean distance. Finally, IMFWA is compared with the basic fireworks algorithm and the genetic algorithms on the Matlab platform for performance analysis. The results show that the IMFWA has advantages in the convergence speed, and the negative impact of the number of tasks is also lower than the fireworks algorithm and genetic algorithm.

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A genetic algorithm-based method for optimizing the energy consumption and performance of multiprocessor systems

Cited by 32 publications

References 27 publications

A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling

A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling

Implementing and Evaluating Impact of Task Scheduling in Cloud Environment

Task scheduling algorithm based on fireworks algorithm

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