.png){kind=icon}
返回
On-Orbit Remote Sensing Image Processing Complex Task Scheduling Model Based on Heterogeneous Multiprocessor
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2023.3327279.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Nowadays, the proliferation of small satellites brings the skyrocketing rise in space data, especially the shift to on-orbit computing needs. On one hand, with the increasing volume of data generation, like high-resolution remote sensing images, on-orbit computing produces near real-time onboard solutions and quick responses. However, constrained by the limited size and energy supply of satellites, achieving energy-efficient on-orbit computing remains a crucial challenge. In this article, an on-orbit remote sensing image processing complex task scheduling model facing a heterogeneous multiprocessor system (HMPS) is pro-posed. First, aiming at accelerating image processing, we establish a novel parallel task execution model using a directed acyclic graph (DAG) to universally describe typical missions, i.e., cloud detection, geometric correction, and image classification. Subsequently, a mathematical task scheduling formulation is defined to calculate the makes pan, and total energy consumption (TEC)required when executing DAG on HMPS. Second, a new Pareto-based iterated greedy optimizer (PIGO) is devised to complete the energy- and time-efficient task execution and resource allocation on HMPS through confined inserting mutation, destruction-reconstruction, and local search. Finally, we build an emulatedon-orbit HMPS to conduct experiments. The results show that, in comparison with the scheme without model scheduling, the cost savings of around 51% and 54% in make span and TEC, respectively, are achieved by the proposed model. More over, the HMPS configured with our methodology can obtain 2.2ximprovement in energy efficiency and process up to 2.56x105pixels per unit of power (W) and time (s)
Keyword:
Complex task scheduling model
heterogeneous computing
multiobjective optimization
on-orbit computing
remote sensing image processing
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
An Improved Hybrid Swarm Intelligence for Scheduling IoT Application Tasks in the Cloud一种改进的混合群智能在云中调度物联网应用任务
.png){kind=icon}
PREAI
分享收藏An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data基于多核处理器和gpu的高效地球科学工作流程: 基于MODIS卫星数据的气溶胶光学厚度反演案例研究
OAAI
OAAI分享收藏
查看更多内容