@article{ZHANG2010, 
author = {Wen ZHANG and Junwei CAO and Yisheng ZHONG and Lianchen LIU and Cheng WU},
title = {Concurrent and Storage-Aware Data Streaming for Data Processing Workflows in Grid Environments},
year = {2010},
journal = {Tsinghua Science and Technology},
volume = {15},
number = {3},
pages = {335-346},
keywords = {grid, data streaming, concurrent, storage-aware, workflow},
url = {https://www.sciopen.com/article/10.1016/S1007-0214(10)70071-7},
doi = {10.1016/S1007-0214(10)70071-7},
abstract = {Data streaming applications, usually composed of sequential/parallel data processing tasks organized as a workflow, bring new challenges to workflow scheduling and resource allocation in grid environments. Due to the high volumes of data and relatively limited storage capability, resource allocation and data streaming have to be storage aware. Also to improve system performance, the data streaming and processing have to be concurrent. This study used a genetic algorithm (GA) for workflow scheduling, using on-line measurements and predictions with gray model (GM). On-demand data streaming is used to avoid data overflow through repertory strategies. Tests show that tasks with on-demand data streaming must be balanced to improve overall performance, to avoid system bottlenecks and backlogs of intermediate data, and to increase data throughput for the data processing workflows as a whole.}
}