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Is Co-scheduling Too Expensive for SMP VMs?

机译:对于SMP VM,共同调度是否太昂贵?

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Symmetric multiprocessing (SMP) virtual machines (VMs) allow users to take advantage of a multiprocessor infrastructure. Despite the advantage, SMP VMs can cause synchronization latency to increase significantly, depending on task scheduling. In this paper, we show that even if a SMP VM runs non-concurrent applications, the synchronization latency problem can still occur due to synchronization in the VM kernel. Our experiments show that both of the widely used open source hypervisors, Xen and KVM, with the default schedulers are susceptible to the synchronization latency problem. To remediate this problem, previous works propose a co-scheduling solution where virtual CPUs (vCPUs) of a SMP VM are scheduled simultaneously. However, the co-scheduling approach can cause CPU fragmentation that reduces CPU utilization, priority inversion that degrades I/O performance, and execution delay, leading to deployment impediment. We propose a balance scheduling algorithm which simply balances vCPU siblings on different physical CPUs without forcing the vCPUs to be scheduled simultaneously. Balance scheduling can achieve similar or (up to 8%) better application performance than co-scheduling without the co-scheduling drawbacks, thereby benefiting various SMP VMs. The evaluation is thoroughly conducted against both concurrent and non-concurrent applications with CPU-bound, I/O-bound, and network-bound workloads in KVM. For empirical comparison, we also implement the co-scheduling algorithm on top of KVM's Completely Fair Scheduler (CFS). Compared to the synchronization-unaware CFS, balance scheduling can significantly improve application performance in a SMP VM (e.g. reduce the average TPC-W response time by up to 85%).
机译:对称多处理(SMP)虚拟机(VM)允许用户利用多处理器基础结构。尽管有优势,但根据任务计划,SMP VM可能导致同步延迟显着增加。在本文中,我们表明,即使SMP VM运行非并行应用程序,由于VM内核中的同步,同步延迟问题仍然可能发生。我们的实验表明,使用默认调度程序的广泛使用的开放源代码管理程序Xen和KVM都容易出现同步延迟问题。为了解决此问题,以前的工作提出了一种协同调度解决方案,其中可以同时调度SMP VM的虚拟CPU(vCPU)。但是,协同调度方法可能会导致CPU碎片化,从而降低CPU利用率,降低优先级,从而降低I / O性能以及执行延迟,从而导致部署障碍。我们提出了一种平衡调度算法,该算法可以简单地平衡不同物理CPU上的vCPU兄弟,而不强制同时调度vCPU。平衡计划可以实现比共同计划相似或更好的应用程序性能(最多提高8%),而没有共同计划的缺点,从而使各种SMP VM受益。针对KVM中具有CPU约束,I / O约束和网络约束的工作负载的并发应用程序和非并发应用程序都进行了彻底评估。为了进行实证比较,我们还在KVM的完全公平调度程序(CFS)之上实现了协同调度算法。与不同步的CFS相比,平衡调度可以显着提高SMP VM中的应用程序性能(例如,将平均TPC-W响应时间减少多达85%)。

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