Tips for optimizing performance in virtual environments

Best Practices

As I mentioned earlier, virtualization is the art of trading off one facility (the CPU) for an otherwise unavailable set of functionalities. If your workload saturates the CPU, you should think twice before planning to virtualize it. In addition to this all-important criterion are some other suggestions that will help you get the most from your processor.

The first task is to examine whether it is possible to "pin" a dedicated CPU (or a core) to a specific virtual machine, effectively creating a mapping between that VM's virtual CPU and a dedicated physical processor. Doing so drastically reduces cache trashing, and as any performance maven knows, modern processor performance is tied to cache hits more than to any other single factor. If this is not possible, it is generally wiser to at least assign the same number of CPUs to all VMs hosted on a given machine – even when overcommitting. This strategy derives from the inherently simpler picture that the hypervisor's thread scheduler will have to contend with if the CPUs are balanced. Similarly, avoid assigning more virtual CPUs than are strictly necessary: If your workload cannot make effective use of multiple cores, avoid virtual SMP (Symmetric Multiprocessing) configurations – the additional virtual CPU still requires interrupts and creates overhead just by being present.

Of course, if your virtual guest is indeed SMP-enabled, you will want to consider tuning affinity within the guest to prevent too many processor migrations from adversely affecting performance. Make sure you are always using the right kernel flavor: SMP for multiple cores and uniprocessor for a single virtual CPU. The uniprocessor kernel will not make use of additional virtual CPUs, and the SMP kernel carries additional overhead, which is wasteful when a single processor is in use. Another suggestion is to remember that CPU affinity can be assigned for IRQ requests as well as threads under the Linux kernel: Consider offloading the interrupt servicing to a dedicated processor or spreading it uniformly where interrupt-intensive devices (such as multiple network cards) are present in your system.

Some virtualization architectures cleverly detect kernel idle loops and reduce the VM's scheduling priority, This strategy can affect performance, and you will want to know the exact mechanics under which this occurs in your system to determine whether it is beneficial or harmful to the workload.

The availability of shared memory pages between multiple identical guests is a very significant factor to consider when choosing how your workload is hosted: If multiple VMs are running on the same host, you can gain a non-trivial advantage by choosing to deploy the same OS image for all the VMs, irrespective of any workload differences. If you use the same image for all VMs on an architecture on which shared memory pages are well implemented, you will achieve a significant reduction in the allocation of actual physical RAM because the multiple copies of those identical OS pages are loaded in memory only once.

It is a good idea to spend some time tuning virtual memory allocation for the needs of the workload: You will want to provide your virtual systems with a comfortable amount of RAM, which will minimize, and possibly eliminate, the need for swapping. Page faults in virtual environments affect performance more than in physical systems, and you should avoid them as much as possible. It is, however, also advisable to avoid assigning excessive amounts of memory, in that this complicates the hypervisor's memory management work, which can result in complex swapping situations if multiple overcommitted VMs are running simultaneously and the hypervisor must force one to yield resources.

Large page support can also improve the performance of workloads that would benefit from a similar setup in non-virtual environments; benchmark your load and determine whether the change is helpful or detrimental in your case. Finally, a significant number for Linux guests is 896MB: Memory pages up to this RAM size are mapped directly into the kernel space, whereas those beyond this boundary require a slightly more involved addressing scheme, an unnecessary overhead if you can possibly avoid it.

Mass storage benefits from simplification just as other components do, and you should avoid complex layouts when they are unnecessary. One example seen in the field is significantly degraded performance with the use of LVM volumes simultaneously on the guest and on the host. LVM is hardly necessary for the guest because the guest's virtual disks are inherently resizable and can be structured on different physical storage media. Swapping should be avoided as a matter of course, but when you can't eliminate it, it makes sense to optimize it by directing I/O activity to different physical disks.

Solid state units are great candidates for fast swap, but one should also remember that, because of the properties of zone bit recording (ZCAV), the outer tracks of a standard hard drive provide much higher raw data transfer rates than the inner tracks. As you lay out your physical partitions, keep this fact in mind and spread the layout to multiple disks if you can. Conversely, you will want to avoid specific I/O scheduler choices within your guests: Their built-in assumptions will most likely not hold in a virtual environment. As a result, it is often best to default to the NOOP scheduler for the guests' kernel because the duty of optimizing read/write performance falls to the host and the complexity of more sophisticated schemes at the guest level will not be helpful and might indeed be harmful.

To ensure optimum performance, defragment disks, both virtual and physical. Just proceed from the guests outward to the hosts, and take into consideration the properties of snapshots in your particular system. Incidentally, as of this writing, several vendors recommend SCSI virtual disks as offering the best performing I/O subsystem: The EIDE bus, even a virtual one, is limited to a single transaction at a time.

A study of network performance would require another full article. Some common pitfalls include the use of a virtual driver that is sub-optimal (the typical example is the use of VMware's vlance instead of the more optimized vmxnet) or the unrecognized failure of duplex auto-negotiation. Performance tuning of the network side of virtualization is evolving rapidly with the appearance of hardware-assist technologies such as Virtual Machine Device Queues (VMDQs), which offload the burden of network I/O management from the hypervisor into NIC hardware that supports multiple parallel queues.

Because much attention is paid to the low-level details, higher level decisions, such as what network protocols to use for data storage, warrant significant consideration, too. Recent results show that iSCSI in both software and hardware implementations and NFS are largely comparable solutions [9] , with the more expensive Fibre Channel still standing out as providing significant improvement.


Carefully choose a workload, simplify the configuration of the virtual machine it will run within, and proceed to performance characterization and tuning. These simple steps are but a start; many specific details inherent to your chosen virtualization technology will have to enter the picture as you test and measure to achieve your target performance.

After you repeat the process a few times, you will learn to value predictable VMs that can be accommodated with static resource allocations, in that they are much easier to plan for than those whose resource usage expands and contracts unpredictably; such guests make poor neighbors to other workloads.


  1. Xen and the Art of Virtualization:
  2. A Performance Comparison of Hypervisors:
  3. Container-Based Operating System Virtualization:
  4. VMmark: A Scalable Benchmark for Virtualized Systems:
  5. Hypervisor Functional Specification:
  6. Performance of WMware VMI:
  7. A Comparison of Software and Hardware Techniques for x86 Virtualization:
  8. VProbes Programming Reference:
  9. Comparison of Storage Protocol Performance:

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