面向长作业环境中的云调度策略

随着云计算的普及,大量的数据处理选择云服务来完成。现有算法较少考虑异构型系统中虚拟机计算能力的不同,导致某些任务等待时间过长。提出了虚拟机负载大小实时调整的算法。对云计算中资源虚拟化特征,给出一种评估虚拟机计算能力的方法。根据虚拟机能力和运行过程中的状态变化,自适应进行任务量大小调整,满足实时要求。通过任务调度,协调任务完成时间,保持各虚拟机负载的动态均衡,缩短长作业的总执行时间,提高了系统的吞吐量和整体服务能力,提升了效益。实验结果表明,本文算法能自适应地调整任务量大小,进行调度,以维持虚拟机负载均衡。

A cloud scheduling strategy for long jobs

With the popularity of cloud computing, a large number of data processing is completed by cloud services. Current algorithms seldom consider the different computing capability of the virtual machine in the heterogeneous system, so the waiting time is too long. We propose a scheduling algorithm which adapts to the size of real-time virtual machine load. Given the virtualization of cloud computing resources, we propose a method for evaluating the computing capability of virtual machines. Based on the virtual machine capability and the changes of the process, the adaptive task size is adjusted to meet the real-time requirements. Task completion time is unified and the load balancing of the virtual machine is maintained so the total execution time is shortened and the system throughput and the overall service capability are improved, hence the benefit is improved. Experimental results show that the algorithm can adaptively adjust the size of tasks according to the operating conditions, and maintain the load balance of the virtual machine.