Task Scheduling in Cloud Environments

Ramezani, Fahimeh; Naderpour, Mohsen; Taheri, Javid; Romanous, Jack; Zomaya, Albert Y.

doi:10.1002/9781119574293.ch8

Cited by 4 publications

(1 citation statement)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, a wide variety of scheduling mechanisms are available, including dynamic, static, workflow, and cloud service scheduling [22]. The cloud maintains both internal and external resource demands, with specifications for response time, resource costs, storage, and bandwidth varying by task [23].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method Based on Gravitational Search and Genetic Algorithms for Task Scheduling in Cloud Computing

Zhang¹

2023

IJACSA

View full text Add to dashboard Cite

Cloud computing has emerged as a novel technology that offers convenient and cost-effective access to a scalable pool of computing resources over the internet. Task scheduling plays a crucial role in optimizing the functionality of cloud services. However, inefficient scheduling practices can result in resource wastage or a decline in service quality due to under-or overloaded resources. To address this challenge, this research paper introduces a hybrid approach that combines gravitational search and genetic algorithms to tackle the task scheduling problem in cloud computing environments. The proposed method leverages the strengths of both gravitational search and genetic algorithms to achieve enhanced scheduling performance. By integrating the unique search capabilities of the gravitational search algorithm with the optimization and adaptation capabilities of the genetic algorithm, a more effective and efficient solution is achieved. The experimental results validate the superiority of the proposed method over existing approaches in terms of total cost optimization. The experimental evaluation demonstrates that the hybrid method outperforms previous scheduling methods in achieving optimal resource allocation and minimizing costs. The improved performance is attributed to the combined strengths of the gravitational search and genetic algorithms in effectively exploring and exploiting the solution space. These findings underscore the potential of the proposed hybrid method as a valuable tool for addressing the task scheduling problem in cloud computing, ultimately leading to improved resource utilization and enhanced service quality.

show abstract

Section: Introductionmentioning

confidence: 99%