.png){kind=icon}
返回
Deadline-Constrained Cost Minimisation for Cloud Computing Environments
.png){kind=icon}
PRE
AI
DOI：10.1109/ACCESS.2023.3258682.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The interest in performing scientific computations using commercially available cloud computing resources has grown rapidly in the last decade. However, scheduling multiple workflows in cloud computing is challenging due to its non-functional constraints and multi-dimensional resource requirements. Scheduling algorithms proposed in literature use search-based approaches which often result in very high computational overhead and long execution time. In this paper, a Deadline-Constrained Cost Minimisation (DCCM) algorithm is proposed for resource scheduling in cloud computing. In the proposed scheme, tasks were grouped based on their scheduling deadline constraints and data dependencies. Compared to other approaches, DCCM focuses on meeting the user-defined deadline by sub-dividing tasks into different levels based on their priorities. Simulation results showed that DCCM achieved higher success rates when compared to the state-of-the-art approaches.
Keyword:
Task analysis
Costs
Cloud computing
Optimization
Scheduling algorithms
Dynamic scheduling
Delays
deadline constraints
resource scheduling
scientific workflow
INDEX TERMS
optimisation
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Role of the S128, H186, and N187 Triad in Substrate Binding and Decarboxylation in the Sheep Liver 6-Phosphogluconate Dehydrogenase Reaction.png)
Biochemistry
BiochemistryIF0
PREAI
分享收藏分享收藏
查看更多内容