计算机工程 ›› 2019, Vol. 45 ›› Issue (7): 86-94.doi: 10.19678/j.issn.1000-3428.0051836

• 体系结构与软件技术 • 上一篇    下一篇

基于演化博弈的弹性调控平台任务调度研究

熊文1, 于全喜2, 吴任博1, 伦惠勤1, 孔海斌2, 谭军光2   

  1. 1. 广州供电局有限公司 电力调度控制中心, 广州 510620;
    2. 东方电子股份有限公司 技术中心, 山东 烟台 264000
  • 收稿日期:2018-06-15 修回日期:2018-07-30 出版日期:2019-07-15 发布日期:2019-07-23
  • 作者简介:熊文(1973-),男,高级工程师、硕士,主研方向为电力系统调度;于全喜(通信作者),工程师、硕士;吴任博、伦惠勤,高级工程师;孔海斌、谭军光,硕士。
  • 基金项目:
    南方电网公司广州供电局科技项目“基于云技术的调度控制系统支撑框架和处理模式研究”(GZHKJXM20160006)。

Research on Task Scheduling of Flexible Regulation and Control Platform Based on Evolutionary Game

XIONG Wen1, YU Quanxi2, WU Renbo1, LUN Huiqin1, KONG Haibin2, TAN Junguang2   

  1. 1. Power Dispatching and Control Center, Guangzhou Power Supply Bureau Co., Ltd., Guangzhou 510620, China;
    2. Technology Center, Dongfang Electronics Co., Ltd., Yantai, Shandong 264000, China
  • Received:2018-06-15 Revised:2018-07-30 Online:2019-07-15 Published:2019-07-23

摘要: 针对电网生产控制云PaaS类弹性调控平台上任务调度性能波动大的问题,构建包含节点感知器、资源状态服务器和任务调度器等核心构件的任务调度框架。在模型选择阶段,采用混合博弈法,根据任务对不同资源的偏好编排执行节点,完成节点负载预估计算。在模型突变阶段,分析任务执行效果调整其资源分配,获得具有较高节点评分的任务调度策略,指导后续任务的博弈节点选择。在分布式监视控制与数据采集系统上进行任务调度框架的测试验证,实现了7个~25个分片、500万量测点级的任务负载均衡和容灾处理,结果表明基于演化博弈的任务调度策略相比开源任务调度工具性能更加稳定。

关键词: 演化博弈模型, 平台即服务, 分布式任务调度, 目录服务, Docker容器引擎

Abstract: Aiming at the problem of fluctuation of task scheduling performance on the Platform as a Service(PaaS) flexible regulate and control platform of the power grid production control cloud,a task scheduling framework including core components such as Node Perceptron(NP),Plat Resource Status Server(PRSS) and Task Scheduler(TS) is constructed.In the model selection stage,the hybrid game method is adopted,and the execution nodes are arranged according to the preference of the tasks for different resources,to complete the node load estimation calculation.In the abrupt change stage of the model,the execution effects of the tasks are analyzed to adjust its resource allocation,obtain a task scheduling strategy with higher node scores,and guide the selection of game nodes for subsequent tasks.The task scheduling framework is tested and verified on the distributed Supervisory Control and Data Acquisition(SCADA) system,and the task load balancing and disaster recovery processing of 7~25 fragments and 5 million measurement points are realized.Results show that the task scheduling strategy based on evolutionary game has more stable scheduling performance than the open source task scheduling tool.

Key words: evolutionary game model, Platform as a Service(PaaS), distributed task scheduling, directory service, Docker container engine

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