基于自适应虚拟机迁移的云资源调度机制

Cloud Resource Scheduling Mechanism Based on Adaptive Virtual Machine Migration

虚拟机迁移是当前云计算资源调度的重要研究方向之一。目前,用户规模的不断增长带来了一些新的挑战,传统迁移策略很难适应动态变化的内外部环境。对此,设计了自适应虚拟机迁移的总体框架,通过对虚拟机迁移建模,提出了“迁移路径”和“服务开销”等概念,并以服务器的CPU利用率和服务器间的带宽利用率为指标,为系统中所有迁移的虚拟机规划最优的迁移路径,以使系统总的服务开销最小化。首先,设计了基于阈值的虚拟机筛选算法来挑选可迁移的虚拟机;接着,设计了基于自回归积分滑动平均模型的时间序列预测算法,用以预测服务器未来时间窗口内的服务开销;然后,利用动态规划基于服务器服务开销的预测值设计了迁移路径计算算法,为每个待迁移虚拟机规划出最优的迁移方案;最后,利用迁移路径下服务器服务开销的预测值与真实值之间的差距所反映出的预测窗口性能的优劣,设计并实现了一个预测窗口自适应调整算法。实验表明,该自适应虚拟机迁移算法在自适应性调整和最小化服务开销等方面具有良好的效果。

Virtual machine(VM)migration is an important research field of current cloud computing resource scheduling.Now the continuous growth of users has brought some new challenges,and current typical migration strategies are difficult to adapt to dynamically changing internal and external environments.Aiming at this problem,this paper proposed an overall framework of adaptive VM migration.Via modeling VM migration,the concepts of“migration path”and“service overhead”were proposed,and the server’s CPU utilization and bandwidth utilization of links between servers were used as indicators to plan the optimal migration path for all to-be-migrated VMs in the system to minimize the total service overhead.Firstly,a threshold-based selection algorithm is presented for the selection of the to-be-migrated VMs.Secondly,an auto regressive integrated moving average model(ARIMA)-based time series prediction algorithm is designed to predict the service overhead within the server’s future time window.Then,the migration path calculation algorithm is designed based on servers’predicted service overhead and dynamic programming,and an optimal migration plan is made for each to-be-migrated VM.Finally,based on the performance of the prediction window determined by the difference between the predicted service overhead and the real value via the migration path,a prediction window adaptive adjustment algorithm is designed and implemented.Experiments prove that the adaptive VM migration has good effects in terms of adaptive adjustment and minimizing service overhead.