Av2 系列Av2-series

Av2 系列 VM 可以部署在各种不同的硬件类型和处理器上。The Av2-series VMs can be deployed on a variety of hardware types and processors. Av2 系列 VM 的 CPU 性能和内存配置非常适合部署和测试等入门级工作负荷。Av2-series VMs have CPU performance and memory configurations best suited for entry level workloads like development and test. 无论部署在哪个硬件上,都会限制大小以为正在运行的实例提供一致的处理器性能。The size is throttled to offer consistent processor performance for the running instance, regardless of the hardware it is deployed on. 若要判断此大小部署所在的物理硬件,请从虚拟机中查询虚拟硬件。To determine the physical hardware on which this size is deployed, query the virtual hardware from within the Virtual Machine. 一些示例用例包括开发和测试服务器、低流量 Web 服务器、中小型数据库、概念证明和代码存储库。Some example use cases include development and test servers, low traffic web servers, small to medium databases, proof-of-concepts, and code repositories.

ACU:100ACU: 100

高级存储:不支持Premium Storage: Not Supported

高级存储缓存:不支持Premium Storage caching: Not Supported

实时迁移:支持Live Migration: Supported

内存保留更新:支持Memory Preserving Updates: Supported

大小Size vCPUvCPU 内存:GiBMemory: GiB 临时存储 (SSD) GiBTemp storage (SSD) GiB 最大临时存储吞吐量:IOPS/读取 MBps/写入 MBpsMax temp storage throughput: IOPS/Read MBps/Write MBps 最大数据磁盘数/吞吐量:IOPSMax data disks/throughput: IOPS 最大 NIC 数/预期网络带宽 (Mbps)Max NICs/Expected network bandwidth (Mbps)
Standard_A1_v2Standard_A1_v2 11 22 10 个10 1000/20/101000/20/10 2/2x5002/2x500 2/2502/250
Standard_A2_v2Standard_A2_v2 22 44 20 个20 2000/40/202000/40/20 4/4x5004/4x500 2/5002/500
Standard_A4_v2Standard_A4_v2 44 88 4040 4000/80/404000/80/40 8/8x5008/8x500 4/10004/1000
Standard_A8_v2Standard_A8_v2 88 1616 8080 8000/160/808000/160/80 16/16x50016/16x500 8/20008/2000
Standard_A2m_v2Standard_A2m_v2 22 1616 20 个20 2000/40/202000/40/20 4/4x5004/4x500 2/5002/500
Standard_A4m_v2Standard_A4m_v2 44 3232 4040 4000/80/404000/80/40 8/8x5008/8x500 4/10004/1000
Standard_A8m_v2Standard_A8m_v2 88 6464 8080 8000/160/808000/160/80 16/16x50016/16x500 8/20008/2000

大小表定义Size table definitions

  • 存储容量的单位为 GiB 或 1024^3 字节。Storage capacity is shown in units of GiB or 1024^3 bytes. 比较以 GB(1000^3 字节)为单位的磁盘与以 GiB(1024^3 字节)为单位的磁盘时,请记住以 GiB 为单位的容量数显得更小。When you compare disks measured in GB (1000^3 bytes) to disks measured in GiB (1024^3) remember that capacity numbers given in GiB may appear smaller. 例如,1023 GiB = 1098.4 GB。For example, 1023 GiB = 1098.4 GB.

  • 磁盘吞吐量的单位为每秒输入/输出操作数 (IOPS) 和 MBps,其中 MBps = 10^6 字节/秒。Disk throughput is measured in input/output operations per second (IOPS) and MBps where MBps = 10^6 bytes/sec.

  • 数据磁盘可以在缓存或非缓存模式下运行。Data disks can operate in cached or uncached modes. 对于缓存数据磁盘操作,主机缓存模式设置为 ReadOnlyReadWriteFor cached data disk operation, the host cache mode is set to ReadOnly or ReadWrite. 对于非缓存数据磁盘操作,主机缓存模式设置为 NoneFor uncached data disk operation, the host cache mode is set to None.

  • 若要获得 VM 的最佳性能,应将数据磁盘数限制为每 vCPU 2 个磁盘。If you want to get the best performance for your VMs, you should limit the number of data disks to two disks per vCPU.

  • 预期的网络带宽是指跨所有 NIC 为每个 VM 类型分配的最大聚合带宽,适用于所有目标。Expected network bandwidth is the maximum aggregated bandwidth allocated per VM type across all NICs, for all destinations. 有关详细信息,请参阅虚拟机网络带宽For more information, see Virtual machine network bandwidth.

    无法保证上限。Upper limits aren't guaranteed. 限值用于在为目标应用程序选择适当的 VM 类型时提供指导。Limits offer guidance for selecting the right VM type for the intended application. 实际的网络性能将取决于多种因素,包括网络拥塞、应用程序负载和网络设置。Actual network performance will depend on several factors including network congestion, application loads, and network settings. 有关如何优化网络吞吐量的信息,请参阅为 Azure 虚拟机优化网络吞吐量For information on optimizing network throughput, see Optimize network throughput for Azure virtual machines. 若要在 Linux 或 Windows 中达到预期的网络性能,可能需要选择特定版本,或者需要优化 VM。To achieve the expected network performance on Linux or Windows, you may need to select a specific version or optimize your VM. 有关详细信息,请参阅带宽/吞吐量测试 (NTTTCP)For more information, see Bandwidth/Throughput testing (NTTTCP).

其他大小Other sizes

后续步骤Next steps

了解有关 Azure 计算单元 (ACU) 如何帮助跨 Azure SKU 比较计算性能的详细信息。Learn more about how Azure compute units (ACU) can help you compare compute performance across Azure SKUs.