基于实时计算平台的虚拟机调度策略

Virtual Machine Scheduling Policy Based on Real-time Computing Platform

在云计算环境中,有效的虚拟机在线迁移和动态扩容技术有助于保障服务等级协议(SLA)的实现,并能够降低能耗成本。但虚拟机的迁移会导致性能下降,频繁迁移会增加SLA违约率。为此,以降低SLA违约率和迁移次数为目标,提出一种可伸缩的实时虚拟机调度策略,利用分布式消息系统Kafka和分布式计算系统Spark构建可扩展系统,基于历史数据预测负载并实时产生调度方案。仿真实验结果表明,与CloudSim原有策略相比,该策略在维持低SLA违约率的同时,迁移次数下降50%左右。

In cloud computing environment, technologies such as dynamic migration and scaling decrease both violations of Service Level Agreement （SLA） and energy cost. But frequent migration of Virtual Machine （VM） can increase SLA violations and cause the performance degradation. To minimize SLA violations and migrations,this paper proposes a scalable real-time VM scheduling policy. It builds a scalable system with Kafka and Spark to analyze history data,predicates future load and generates migration plan. Simulation experimental result shows that the policy decreases migrations by 50% while maintaining low SLA violation rate compared with native policy of CloudSim.