GCRS – Green Computing Resource Scheduler: An Optimized Energy Efficient Cloud Data Centers to Scale Down Carbon Emission DOI

K. Chitra,

M. Getzi

Published: Oct. 18, 2023

Cloud computing is a powerful technology that rapidly growing in popularity with energy consumption being major concern. data centers consume lot of energy, which can lead to high operating costs and carbon emissions. This significant bottleneck restricting the development cloud computing. If people continue rely on traditional centers, environmental impact will only get worse. Green solutions are needed address this challenge. designed utilize resources while minimizing efficiently. providers offer different levels service, defined service-level agreements (SLAs). SLAs typically include things like availability response time for requests, assigned clients. try best exploit their by allocating them users enhances utilization lowers idle time. be done using scheduling algorithms. In proposed work" or servers maintain clients read write. Servers classified as read-and-write separately based capacity, tasks accordingly. The model uses every server's full potential, resulting an environment consumes less energy. objective Computing Resource Scheduling (GCRS) reduce consumed emitted center through resources.

Language: Английский

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

‪R. Ghafari,

F. Hassani Kabutarkhani,

N. Mansouri

et al.

Cluster Computing, Journal Year: 2022, Volume and Issue: 25(2), P. 1035 - 1093

Published: Jan. 5, 2022

Language: Английский

Citations

65
A two-stage scheduler based on New Caledonian Crow Learning Algorithm and reinforcement learning strategy for cloud environment DOI

Behnam Mohammad Hasani Zade,

N. Mansouri, Mohammad Masoud Javidi

et al.

Journal of Network and Computer Applications, Journal Year: 2022, Volume and Issue: 202, P. 103385 - 103385

Published: April 4, 2022

Language: Английский

Citations

30
A dynamic multipopulation genetic algorithm for multiobjective workflow scheduling based on the longest common sequence DOI
Huixian Qiu, Xuewen Xia, Yuanxiang Li

et al.

Swarm and Evolutionary Computation, Journal Year: 2023, Volume and Issue: 78, P. 101291 - 101291

Published: March 16, 2023

Language: Английский

Citations

17
Resource scheduling methods for cloud computing environment: The role of meta-heuristics and artificial intelligence DOI
Rajni Aron, Ajith Abraham

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 116, P. 105345 - 105345

Published: Sept. 5, 2022

Language: Английский

Citations

27
An adaptive symbiotic organisms search for constrained task scheduling in cloud computing DOI
Mohammed Abdullahi, Md Asri Ngadi,

Salihu Idi Dishing

et al.

Journal of Ambient Intelligence and Humanized Computing, Journal Year: 2022, Volume and Issue: 14(7), P. 8839 - 8850

Published: Jan. 23, 2022

Language: Английский

Citations

26
Energy-aware intelligent scheduling for deadline-constrained workflows in sustainable cloud computing DOI Creative Commons
Min Cao, Yaoyu Li, Xupeng Wen

et al.

Egyptian Informatics Journal, Journal Year: 2023, Volume and Issue: 24(2), P. 277 - 290

Published: April 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.

Language: Английский

Citations

12
AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic review DOI
Navid Khaledian,

Marcus Voelp,

Sadoon Azizi

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(8), P. 10265 - 10298

Published: May 8, 2024

Language: Английский

Citations

4
HICA: A Hybrid Scientific Workflow Scheduling Algorithm for Symmetric Homogeneous Resource Cloud Environments DOI Open Access
Liang Hu,

Xianwei Wu,

Xilong Che

et al.

Symmetry, Journal Year: 2025, Volume and Issue: 17(2), P. 280 - 280

Published: Feb. 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.

Language: Английский

Citations

0
Resource allocation strategies and task scheduling algorithms for cloud computing: A systematic literature review DOI Creative Commons
Waleed Kareem Awad, Khairul Akram Zainol Ariffin, Mohd Zakree Ahmad Nazri

et al.

Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 34(1)

Published: Jan. 1, 2025

Abstract The concept of cloud computing has completely changed how computational resources are delivered and used. By enabling on-demand access to collective through the internet. While this technological shift offers unparalleled flexibility, it also brings considerable challenges, especially in scheduling resource allocation, particularly when optimizing multiple objectives a dynamic environment. Efficient allocation critical computing, as they directly impact system performance, utilization, cost efficiency heterogeneous conditions. Existing approaches often face difficulties balancing conflicting objectives, such reducing task completion time while staying within budget constraints or minimizing energy consumption maximizing utilization. As result, many solutions fall short optimal leading increased costs degraded performance. This systematic literature review (SLR) focuses on research conducted between 2019 2023 Following preferred reporting items for reviews meta-analyses guidelines, ensures transparent replicable process by employing inclusion criteria bias. explores key concepts management classifies existing strategies into mathematical, heuristic, hyper-heuristic approaches. It evaluates popular algorithms designed optimize metrics consumption, reduction, makespan minimization, performance satisfaction. Through comparative analysis, SLR discusses strengths limitations various schemes identifies emerging trends. underscores steady growth field, emphasizing importance developing efficient address complexities modern systems. findings provide comprehensive overview current methodologies pave way future aimed at tackling unresolved challenges management. work serves valuable practitioners academics seeking environments, contributing advancements computing.

Language: Английский

Citations

0
Energy-efficient virtual-machine mapping algorithm (EViMA) for workflow tasks with deadlines in a cloud environment DOI Creative Commons
J. Kok Konjaang, John Murphy, Liam Murphy

et al.

Journal of Network and Computer Applications, Journal Year: 2022, Volume and Issue: 203, P. 103400 - 103400

Published: April 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.

Language: Английский

Citations

17