.png){kind=icon}
返回
Prediction based task scheduling approach for floodplain application in cloud environment
.png){kind=icon}
PRE
AI
DOI：10.1007/s00607-021-00936-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Natural and environmental sciences are one of the scientific domains which seek a lot of attention as it requires high performance computation and large storage space. Cloud computing is such a platform that offers a customizable infrastructure where scientific applications can provision the required resources prior to execution. The elasticity characteristic of cloud computing and it's pay-as-you-go pricing model can reduce the resource usage cost for cloud client's. The various services offered by the cloud providers and the extravagant developments in the domain of cloud computing has attracted many scientists to deploy their applications on cloud. The change in number of tasks of scientific application directly affects the demand of cloud resources. Therefore, to handle the fluctuating demand of resources, there is a need to manage the resources in an efficient manner. This research work focuses on the design of a prediction based scheduling approach which maps the tasks of scientific application with the optimal VM by combining the features of swarm intelligence and multi-criteria decision making approach. The proposed approach improves the accuracy rate, minimizes the execution time, cost and service level agreement violation rate in comparison to existing scheduling heuristics.
Keyword:
Resource prediction
Resource scheduling
Cloud environment
Virtual machine
Ensembling
Machine learning
Quality of service
期刊
C
IF:
2.8
论文数:
2.3K
被引数:
3.5K
机构
暂无机构信息
引用论文
An autonomic resource provisioning approach for service-based cloud applications: A hybrid dapproach
.png){kind=icon}
PREAI
分享收藏分享收藏A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing一种基于GSA的云计算双目标工作流调度混合算法
PREAI
分享收藏分享收藏
查看更多内容