Optimizing task scheduling in cloud environments: a hybrid golden search whale optimization algorithm approach

Managing fluctuating workloads and optimizing resource utilization in cloud environments pose significant challenges, particularly in fields requiring real-time data processing, such as healthcare. This paper introduces a novel hybrid metaheuristic technique, the Golden Search Whale Optimization Alg...

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Vydáno v:Neural computing & applications Ročník 37; číslo 17; s. 10851 - 10873
Hlavní autoři: Acharya, Biswaranjan, Panda, Sucheta, Das, Satyabrata, Majhi, Santosh Kumar, Gerogiannis, Vassilis C., Kanavos, Andreas
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.06.2025
Springer Nature B.V
Témata:
Algorithms
Allocations
Artificial Intelligence
Biomedical data
Cloud computing
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Data processing
Heuristic methods
Image Processing and Computer Vision
Optimization
Optimization algorithms
Performance evaluation
Probability and Statistics in Computer Science
Real time
Resource utilization
S.I.: Timely Advances of Deep Learning with applications and Data Driven Modeling
Scheduling
Searching
Special Issue on Timely Advances of Deep Learning with applications and Data Driven Modeling
Task scheduling
Task scheduling
Cloud computing
Metaheuristic optimization
Resource utilization
Golden search whale optimization algorithm (GSWOA)
ISSN:0941-0643, 1433-3058
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Shrnutí:Managing fluctuating workloads and optimizing resource utilization in cloud environments pose significant challenges, particularly in fields requiring real-time data processing, such as healthcare. This paper introduces a novel hybrid metaheuristic technique, the Golden Search Whale Optimization Algorithm (GSWOA), specifically designed for scheduling independent dynamic biomedical data. GSWOA merges the strengths of Golden Search Optimization (GSO) and Whale Optimization Algorithm (WOA), optimizing numerical function optimization and achieving a balance between exploration and exploitation. The algorithm’s effectiveness was assessed using MATLAB by applying standard benchmark functions and further evaluated on a real-world biomedical dataset within the CloudSim environment. The performance evaluations demonstrate that GSWOA significantly outperforms existing metaheuristic and traditional scheduling techniques, achieving a 42.71% increase in resource utilization and a 14.17% reduction in makespan compared to conventional methods. These results highlight GSWOA’s potential to enhance scheduling efficiency substantially in cloud computing infrastructures, suggesting it is a powerful tool for complex task allocations. Future research will explore the scalability of GSWOA and its applicability across other data-intensive sectors.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-025-11113-9