.png){kind=icon}
返回
Energy-efficient virtual-machine mapping algorithm (EViMA) for workflow tasks with deadlines in a cloud environment
.png){kind=icon}
OA
AI
DOI：10.1016/j.jnca.2022.103400.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 the workflow scheduling problem, several approaches have been presented for scheduling workflow on cloud resources. However, more efficient approaches are needed to improve cloud service delivery. In this paper, an energy-efficient virtual machine mapping algorithm (EViMA) is proposed to improve resource management in the cloud computing environment to achieve effective scheduling that reduces cloud data center energy consumption, execution makespan, and execution cost. This ensures that the requirements of cloud users are met, and improves the quality of services offered by cloud providers. Our proposed mechanism considers the heterogeneity of scheduling from both cloud users' and workflow applications' perspectives. Through simulation experiments on real workflow datasets, the proposed EViMA can provide better solutions for both cloud users and cloud providers by reducing energy consumption, execution makespan, and execution cost better than the state-of-the-art.
Keyword:
Energy consumption
Execution Cost
Execution makespan
Workflow scheduling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8
论文数:
3.6K
被引数:
1.1W
机构
引用论文
.png){kind=icon}
分享收藏Cost and makespan scheduling of workflows in clouds using list multiobjective optimization technique使用列表多目标优化技术在云中进行工作流的成本和makespan调度
PREAI
分享收藏分享收藏
查看更多内容