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Virtual Cpu Scheduling Techniques for Kernel Based Virtual Machine (Kvm)

机译:基于内核的虚拟机(Kvm)的Virtual Cpu调度技术

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In a multi-CPU Virtual Machine(VM), virtual CPUs (VCPUs) are not guaranteed to be scheduled simultaneously. Operating System (OS) constructs, such as busy-wait (for e.g., spin locks) are written with an assumption of CPUs running concurrently on bare-metal wastes lot of CPU time. The Hardware assisted Pause Loop Exit (PLE) feature detects unnecessary busy-loop constructs in guest VMs and traps to the VCPU scheduler a.k.a PLE handler to choose a best VCPU candidate to run. The existing approach (before the optimization mentioned in the paper) does a directed yieldfootnote{ A task giving away it's CPU time to another task.} to a random VCPU and needs more intelligence. We also need to carefully consider the over-commit ratiofootnote { Ratio of total virtual CPUs to physical CPUs.} while designing the VCPU scheduling algorithm. For e.g., trapping to the PLE handler is an overhead during under-commit cases. The existing approach lacks the over-commit ratio awareness. Hence we need effective scheduling of VCPUs to boost the performance of VMs. We present three major improvements to old VCPU scheduling technique that include choosing a better VCPU for directed yield and optimizing for under-commit cases. All these approaches have been accepted into Linux kernel. These changes potentially bring around 300-400% improvements to I/O intensive cloud VMs (large under-committed guests) and up to 25% improvement to over-committed CPU intensive VMs.
机译:在多CP​​U虚拟机(VM)中,不能保证同时调度虚拟CPU(VCPU)。在假设CPU在裸机上同时运行的情况下编写操作系统(OS)结构(例如忙等待(例如,自旋锁))会浪费大量CPU时间。硬件辅助的暂停循环退出(PLE)功能可检测来宾VM中不必要的繁忙循环结构,并陷阱到VCPU调度程序(又称为PLE处理程序)以选择最佳的VCPU候选对象来运行。现有方法(在本文中提到的优化之前)针对随机VCPU进行了直接的yieldfootnote {一个任务,将它的CPU时间分配给另一个任务。}在设计VCPU调度算法时,我们还需要仔细考虑过量使用的比率脚注{虚拟虚拟CPU与物理CPU的总数之比。}。例如,在提交不足的情况下,捕获到PLE处理程序是一项开销。现有方法缺乏超额承诺比率意识。因此,我们需要对VCPU进行有效的调度以提高VM的性能。我们提出了对旧VCPU调度技术的三项重大改进,包括选择更好的VCPU以获得有针对性的产量以及针对提交不足的情况进行优化。所有这些方法已被Linux内核接受。这些更改可能会为I / O密集型云虚拟机(未充分使用的大型来宾)带来300-400%的改进,而对过量使用CPU密集型VM则可带来多达25%的改进。

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