Task scheduling algorithms for energy optimization in cloud environment: a comprehensive review

Cluster Computing, Год журнала: 2022, Номер 25(2), С. 1035 - 1093

Опубликована: Янв. 5, 2022

Язык: Английский

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A two-stage scheduler based on New Caledonian Crow Learning Algorithm and reinforcement learning strategy for cloud environment

Journal of Network and Computer Applications, Год журнала: 2022, Номер 202, С. 103385 - 103385

Опубликована: Апрель 4, 2022

Язык: Английский

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A dynamic multipopulation genetic algorithm for multiobjective workflow scheduling based on the longest common sequence

Swarm and Evolutionary Computation, Год журнала: 2023, Номер 78, С. 101291 - 101291

Опубликована: Март 16, 2023

Язык: Английский

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HICA: A Hybrid Scientific Workflow Scheduling Algorithm for Symmetric Homogeneous Resource Cloud Environments

Symmetry, Год журнала: 2025, Номер 17(2), С. 280 - 280

Опубликована: Фев. 12, 2025

With the increasing volume of scientific computation data and advancement computer performance, is becoming more dependent on powerful computing capabilities cloud computing. On platforms, tasks in workflows are assigned to computational resources executed according specific strategies. Therefore, workflow scheduling has become a key factor affecting efficiency. This paper proposes hybrid algorithm, HICA, address problem symmetric homogeneous environments with optimization goals makespan cost. HICA combines Imperialist Competitive Algorithm (ICA) HEFT integrating into initial population ICA accelerate convergence ICA. Experimental results show that proposed approach outperforms other algorithms real-world applications. Specifically, when scale 100, average improvements cost 133.89 273.33, respectively; 1000, 371.62 9178.98. The for Earth System Model parameter tuning compared scenario without using were improved by 13% 21%, respectively.

Язык: Английский

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Resource scheduling methods for cloud computing environment: The role of meta-heuristics and artificial intelligence

Engineering Applications of Artificial Intelligence, Год журнала: 2022, Номер 116, С. 105345 - 105345

Опубликована: Сен. 5, 2022

Язык: Английский

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An adaptive symbiotic organisms search for constrained task scheduling in cloud computing

Journal of Ambient Intelligence and Humanized Computing, Год журнала: 2022, Номер 14(7), С. 8839 - 8850

Опубликована: Янв. 23, 2022

Язык: Английский

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Energy-aware intelligent scheduling for deadline-constrained workflows in sustainable cloud computing

Egyptian Informatics Journal, Год журнала: 2023, Номер 24(2), С. 277 - 290

Опубликована: Апрель 18, 2023

It is challenging to handle the non-linear power consumption model, complex workflow structures, and diverse user-defined deadlines for energy-efficient scheduling in sustainable cloud computing. Although metaheuristics are very attractive solve this problem, most of existing work regards problem as a black-box ignores use domain knowledge. To make up their shortcomings, paper tailors an energy-aware intelligent algorithm (EIS) with three new mechanisms. First, we derive optimal execution time that minimizes energy each task on given resource. Second, based task, EIS distributes slack (difference between its completion deadline) reduce voltages frequencies executions saving. Third, mines idle gaps caused by precedence constraints further dynamic whilst satisfying workflows' deadline constraints. measure performance EIS, conduct extensive comparison experiments actual applications. The results demonstrate much lower than competitors under different deadlines, has faster descend rate evolution process.

Язык: Английский

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AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic review

Cluster Computing, Год журнала: 2024, Номер 27(8), С. 10265 - 10298

Опубликована: Май 8, 2024

Язык: Английский

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Energy-efficient virtual-machine mapping algorithm (EViMA) for workflow tasks with deadlines in a cloud environment

Journal of Network and Computer Applications, Год журнала: 2022, Номер 203, С. 103400 - 103400

Опубликована: Апрель 29, 2022

Processing large scientific applications generates a huge amount of data, which makes running experiments in the cloud computing environment very expensive and energy-consuming. To find an optimal solution to workflow scheduling problem, several approaches have been presented for on resources. However, more efficient are needed improve service delivery. In this paper, energy-efficient virtual machine mapping algorithm (EViMA) is proposed resource management achieve effective that reduces data center energy consumption, execution makespan, cost. This ensures requirements users met, improves quality services offered by providers. Our mechanism considers heterogeneity from both users' applications' perspectives. Through simulation real datasets, EViMA can provide better solutions providers reducing cost than state-of-the-art.

Язык: Английский

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Redefining teaching-and-learning-process in TLBO and its application in cloud

Applied Soft Computing, Год журнала: 2023, Номер 135, С. 110017 - 110017

Опубликована: Янв. 18, 2023

Язык: Английский

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