云计算中资源延迟感知的实时任务调度方法

绿色云计算已经成为一个研究焦点，动态整合虚拟机和关闭空闲主机是极具潜力的途径可降低云计算数据中心的能耗.当云平台的负载迅速增加时，系统需要启动更多的主机和创建更多的虚拟机来扩展可用资源.然而，启动主机和创建虚拟机需要一定的时间开销，使得紧急任务难以及时开始，从而延误了截止期.为了解决以上问题，首先提出具有机器启动时间感知的虚拟机扩展策略，以缓解机器启动时间冲击实时任务的时效性要求.基于该策略，设计算法STARS来调度实时任务和资源，以在保障任务时效性与节能2方面进行权横.最后，使用Google的负载数据进行模拟实验，比较算法STARS与其他2个算法的性能.实验结果表明，在保障任务时效性、节能和资源利用率方面，算法STARS优于对比算法.

Resource-Delay-Aware Scheduling for Real-Time Tasks in Clouds

Green cloud computing has become a central issue, and dynamical consolidation of virtual machines (VMs) and turning off the idle hosts show promising ways to reduce the energy consumption for cloud data centers. When the workload of the cloud platform increases rapidly, more hosts will be started on and more VMs will be deployed to provide more available resources. However, the time overheads of turning on hosts and starting VMs will delay the start time of tasks, which may violate the deadlines of real-time tasks. To address this issue, three novel startup-time-aware policies are developed to mitigate the impact of machine startup time on timing requirements of real-time tasks. Based on the startup-time-aware policies, we propose an algorithm called STARS to schedule real-time tasks and resources, such making a good trade-off between the schedulibility of real-time tasks and energy saving. Lastly, we conduct simulation experiments to compare STARS with two existing algorithms in the context of Google's workload trace, and the experimental results show that STARS outperforms those algorithms with respect to guarantee ratio, energy saving and resource utilization.