.png){kind=icon}
返回
SensWorkflow: a high-performance framework for remote sensing big data processing on heterogeneous clusters
.png){kind=icon}
OA
AI
DOI：10.1080/20964471.2025.2602292.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The increasing volume and complexity of remote sensing data have made high performance computing (HPC) essential, with workflows becoming key to enabling efficient and automated processing. However, challenges remain in heterogeneous distributed computing environments. To address these issues, we propose SensWorkflow, a dynamic and visual framework for high-performance remote sensing applications designed to simplify processing workflows and improve computational efficiency. SensWorkflow integrates efficient large-scale data storage and management, a novel blind dating optimization (BDO) task scheduling algorithm newly proposed in this study to improve load balancing and task assignment, model operator management, and visual workflow and task management. It is deployed on a heterogeneous distributed cluster and integrates the Bee grid file system (BeeGFS) for data storage, a not only structured query language (NoSQL) database for metadata management, and the high throughput Condor (HTCondor) framework for distributed task scheduling. To evaluate its performance, the synergetic retrieval of aerosol properties (SRAP) aerosol optical depth (AOD) retrieval algorithm was used as a case study to process 10-day moderate resolution imaging spectroradiometer (MODIS) data in 2022.
Keyword:
Remote sensing workflow
high performance computing
remote sensing big data
blind dating optimization
distributed task scheduling
期刊
B
IF:
3.8
论文数:
103
被引数:
0
机构
引用论文
Optimal design of second generation current conveyor using craziness-based particle swarm optimisation基于疯狂粒子群优化的第二代电流传输器最优设计
.png){kind=icon}
PREAI
分享收藏分享收藏A high throughput geocomputing system for remote sensing quantitative retrieval and a case study
PREAI
分享收藏
查看更多内容