Every day, numerous VMs are migrated inside a datacenter to balance the load, save energy or prepare production servers for maintenance. Although VM placement problems are carefully studied, the underlying migration schedulers rely on vague adhoc models. This leads to unnecessarily long and energy-intensive migrations. We present mVM, a new and extensible migration scheduler. To provide schedules with minimal completion times, mVM parallelizes and sequentializes the migrations with regards to the memory workload and the network topology. mVM is implemented as a plugin of BtrPlace and its current library allows administrators to address temporal and energy concerns. Experiments on a real testbed shows mVM outperforms state-of-the-art migration schedulers. Compared to schedulers that cap the migration parallelism, mVM reduces the individual migration duration by 20.4 percent on average and the schedule completion time by 28.1 percent. In a maintenance operation involving 96 VMs migrated between 72 servers, mVM saves 21.5 percent Joules against BtrPlace. Compared to the migration model inside the cloud simulator CloudSim, the prediction error of the migrations duration is about 5 times lower with mVM. By computing schedules involving thousands of migrations performed over various fat-tree network topologies, we observed that the mVM solving time accounts for about 1 percent of the schedule execution time.

中文翻译：

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调度虚拟机的实时迁移

每天，数据中心内都会迁移大量虚拟机，以平衡负载、节省能源或准备生产服务器进行维护。尽管仔细研究了 VM 放置问题，但底层迁移调度程序依赖于模糊的临时模型。这会导致不必要的长时间和能源密集型迁移。我们提出了 mVM，一种新的可扩展迁移调度程序。为了提供具有最短完成时间的计划，mVM 会根据内存工作负载和网络拓扑对迁移进行并行化和顺序化。mVM 是作为 BtrPlace 的插件实现的，其当前库允许管理员解决时间和能量问题。在真实测试平台上的实验表明，mVM 优于最先进的迁移调度程序。与限制迁移并行性的调度程序相比，mVM 将单个迁移持续时间平均缩短了 20.4%，将计划完成时间缩短了 28.1%。在涉及在 72 个服务器之间迁移 96 个 VM 的维护操作中，mVM 比 BtrPlace 节省了 21.5% 的焦耳。与云模拟器 CloudSim 内部的迁移模型相比，mVM 迁移持续时间的预测误差降低了约 5 倍。通过计算涉及在各种胖树网络拓扑上执行的数千次迁移的调度，我们观察到 mVM 求解时间约占调度执行时间的 1%。与云模拟器 CloudSim 内部的迁移模型相比，mVM 迁移持续时间的预测误差降低了约 5 倍。通过计算涉及在各种胖树网络拓扑上执行的数千次迁移的调度，我们观察到 mVM 求解时间约占调度执行时间的 1%。与云模拟器 CloudSim 内部的迁移模型相比，mVM 迁移持续时间的预测误差降低了约 5 倍。通过计算涉及在各种胖树网络拓扑上执行的数千次迁移的调度，我们观察到 mVM 求解时间约占调度执行时间的 1%。