摘要
随着电力大数据的深化应用,对基于多数据中心的任务规划、调度、计算能力的均衡和计算节点的效率提出了更高的要求,文章详细阐述了电力大数据广域计算任务流规划与调度问题,探讨了适用于多数据中心的广域计算任务流处理框架,介绍了顾及业务逻辑关系的最优粒度任务分解方法,分析了面向电力大数据的广域计算任务流规划策略,提出面向电力大数据的分布式任务协同调度方法,为电力大数据广域并发计算任务流的合理规划与协同调度提供技术参考,综合提升电力大数据广域并发计算任务流的处理效率。
With the deepening application of power big data, higher requirement based on multi data center mode is put forward, including the task scheduling, computing power equalization and computing nodes efficiency. In this paper, the task scheduling and scheduling problem of big data wide area computing is discussed in detail. And the power big data area calculation problem of task flow planning wide area computing task flow processing for multi data center and scheduling is described and the framework of is discussed. A distributed task scheduling method based on power big data is proposed. It provides a technical reference for the reasonable planning and collaborative scheduling of the wide area data concurrent computing tasks, and comprehensively improves the processing efficiency of big data concurrent computing task flow.
作者
朱力鹏
饶玮
裘洪彬
吴舜
靳丹
ZHU Li-peng RAO Wei QIU Hong-bin WU Shun JIN Dan(Global Energy lnterconnection Research Institute, Nanjing 210003, China State Grid Jibei Electric Power Company, Beijing 100053, China State Grid Gansu Electric Power Company, Lanzhou 730046, China)
出处
《电力信息与通信技术》
2017年第3期62-66,共5页
Electric Power Information and Communication Technology
关键词
电网
大数据
数据中心
任务流
规划策略
协同调度
grid
big data： data center
task flow
planning strategy： collaborative scheduling
