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

中图分类号: 

• TP391