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A Novel Hybrid Algorithm Based on Butterfly and Flower Pollination Algorithms for Scheduling Independent Tasks on Cloud Computing

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Publicado en:International Journal of Advanced Computer Science and Applications vol. 16, no. 1 (2025)
Autor principal: PDF
Publicado:
Science and Information (SAI) Organization Limited
Materias:
Task scheduling
Convergence
Cloud computing
Optimization
Resource allocation
Resource scheduling
Statistical methods
Algorithms
Resource utilization
Statistical analysis
Heuristic methods
Load
Internet
Computer science
Exploitation
Optimization techniques
Software services
Mutualism
Workloads
Energy consumption
Efficiency
Scheduling
Infrastructure
Genetic algorithms
Optimization algorithms
Acceso en línea:Citation/Abstract
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Resumen:Cloud computing is an Internet-based computing paradigm where virtual servers or workstations are offered as platforms, software, infrastructure, and resources. Task scheduling is considered one of the major NP-hard problems in cloud environments, posing several challenges to efficient resource allocation. Many metaheuristic algorithms have been extensively employed to address these task-scheduling problems as discrete optimization problems and have given rise to some proposals. However, these algorithms have inherent limitations due to local optima and convergence to poor results. This paper suggests a hybrid strategy for organizing independent tasks in heterogeneous cloud resources by incorporating the Butterfly Optimization Algorithm (BOA) and Flower Pollination Algorithm (FPA). Although BOA suffers from local optima and loss of diversity, which may cause an early convergence of the swarm, our hybrid approach outperforms such weaknesses by exploiting a mutualism-based mechanism. Indeed, the proposed hybrid algorithm outperforms existing methods while considering different task quantities with better scalability. Experiments are conducted within the CloudSim simulation framework with many task instances. Statistical analysis is performed to test the significance of the obtained results, which confirms that the suggested algorithm is effective at solving cloud-based task scheduling issues. The study findings indicate that the hybrid metaheuristic algorithm could be a promising approach to improving resource utilization and optimizing cloud task scheduling.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2025.0160181
Fuente:Advanced Technologies & Aerospace Database