Posts Tagged ‘latency’

vSphere SDRS Design Considerations

June 26, 2016

data storageIf you happen to have your vSphere cluster to be licensed with Enterprise Plus edition, you may be aware of some of the advanced storage management features it includes, such as Storage DRS and Profile-Driven Storage.

These two features work together to let you optimise VM distribution between multiple VMware datastores from capability, capacity and latency perspective, much like DRS does for memory and compute. But they have some interoperability limitations, which I want to discuss in this post.

Datastore Clusters

In simple terms, datastore cluster is a collection of multiple datastores, which can be seen as a single entity from VM provisioning perspective.


VMware poses certain requirements for datastore clustes, but in my opinion the most important one is this:

Datastore clusters must contain similar or interchangeable datastores.

In other words, all of the datastores within a datastore cluster should have the same performance properties. You should not mix datastores provisioned on SSD tier with datastores on SAS and SATA tier and vise versa. The reason why is simple. Datastore clusters are used by SDRS to load-balance VMs between the datastores of a datastore cluster. DRS balances VMs based on datastore capacity and I/O latency only and is not storage capability aware. If you had SSD, SAS and SATA datastores all under the same cluster, SDRS would simply move all VMs to SSD-backed datastores, because it has the lowest latency and leave SAS and SATA empty, which makes little sense.

Design Decision 1:

  • If you have several datastores with the same performance characteristics, combine them all in a datastore cluster. Do not mix datastores from different arrays or array storage tiers in one datastore cluster. Datastore clusters is not a storage tiering solution.

Storage DRS

As already mentioned, SDRS is a feature, which when enabled on a datastore cluster level, lets you automatically (or manually) distribute VMs between datastores based on datastore storage utilization and I/O latency basis. VM placement recommendations and datastore maintenance mode are amongst other useful features of SDRS.


Quite often SDRS is perceived as a feature that can work with Profile-Driven Storage to enforce VM Storage Policy compliance. One of the scenarios, that is often brought up is what if there’s a VM with multiple .vmdk disks. Each disk has a certain storage capability. Mistakenly one of the disks has been storage vMotion’ed to a datastore, which does not meet the storage capability requirements. Can SDRS automatically move the disk back to a compliant datastore or notify that VM is not compliant? The answer is – no. SDRS does not take storage capabilities into account and make decisions only based on capacity and latency. This may be implemented in future versions, but is not supported in vSphere 5.

Design Decision 2:

  • Use datastore clusters in conjunction with Storage DRS to get the benefit of VM load-balancing and placement recommendations. SDRS is not storage capability aware and cannot enforce VM Storage Policy compliance.

Profile-Driven Storage

So if SDRS and datastore clusters are not capable of supporting  multiple tiers of storage, then what does? Profile-Driven Storage is aimed exactly for that. You can assign user-defined or system-defined storage capabilities to a datastore and then create a VM Storage Policy and assign it to a VM. VM Storage Policy includes the list of required storage capabilities and only those datastores that mach them, will be suggested as a target for the VM that is assigned to that policy.

You can create storage capabilities manually, such as SSD, SAS, SATA. Or more abstract, such as Bronze, Silver and Gold and assign them to corresponding datastores. Or you can leverage VASA, which automatically assigns corresponding storage capabilities. Below is an example of a datastore connected from a Dell Compellent storage array.


You can then use storage capabilities from the VASA provider to create VM Storage Policies and assign them to VMs accordingly.


Design Decision 3:

  • If you have more than one datastore storage type, use Profile-Driven Storage to enforce VM placement based on VM storage requirements. VASA can simplify storage capabilities management.


If all of your datastores have the same performance characteristics, such as a number of LUNs auto-tiered on the storage array side, then one SDRS-enabled datastore cluster is a perfect solution for you.

But if your storage design is slightly more complex and you have datastores with different performance characteristics, such as SSD, SAS and SATA, leverage Profile-Driven Storage to control VM placement and enforce compliance. Just make sure to use a separate cluster for each tier of storage and you will get the most benefit out of vSphere Storage Policy-Based Management.

Monitoring ESX Storage Queues

July 30, 2013

6a00d8341c328153ef01774354e2fd970d-500wiQueue Limits

I/O data goes through several storage queues on its way to disk drives. VMware is responsible for VM queue, LUN queue and HBA queue. VM and LUN queues are usually equal to 32 operations. It means that each ESX host at any moment can have no more than 32 active operations to a LUN. Same is true for VMs. Each VM can have as many as 32 active operations to a datastore. And if multiple VMs share the same datastore, their combined I/O flow can’t go over the 32 operations limit (per LUN queue for QLogic HBAs has been increased from 32 to 64 operations in vSphere 5). HBA queue size is much bigger and can hold several thousand operations (4096 for QLogic, however I can see in my config that driver is configured with 1014 operations).

Queue Monitoring

You can monitor storage queues of ESX host from the console. Run “esxtop”, press “d” to view disk adapter stats, then press “f” to open fields selection and add Queue Stats by pressing “d”.

AQLEN column will show the queue depth of the storage adapter. CMDS/s is the real-time number of IOPS. DAVG is the latency which comes from the frame traversing through the “driver – HBA – fabric – array SP” path and should be less than 20ms. Otherwise it means that storage is not coping. KAVG shows the time which operation spent in hypervisor kernel queue and should be less than 2ms.

Press “u” to see disk device statistics. Press “f” to open the add or remove fields dialog and select Queue Stats “f”. Here you’ll see a number of active (ACTV) and queue (QUED) operations per LUN.  %USD is the queue load. If you’re hitting 100 in %USD and see operations under QUED column, then again it means that your storage cannot manage the load an you need to redistribute your workload between spindles.

Some useful documents:

Unexpected Deduplication Impact on VMware I/O Latency

May 28, 2013

NetApp deduplication is a postponed process. During normal operation Data ONTAP only calculates hashes for the data blocks. Actual deduplication is carried out off-hours as per configured schedule. Hash calculation doesn’t affect performance in most cases. I talked about that in my previous post. NetApp states in its documentation that deduplication is a low-priority process:

When one deduplication process is running, there is 0% to 15% performance degradation on other applications.

Once I faced a situation when deduplication was configured to be carried out during business hours on one of the volumes. No one noticed that at some point volume run out of space and Data ONTAP wasn’t able to perform deduplication from that time. Situation became worse when Data ONTAP was upgraded from version 7.3.2 to 8.1.0. Now during deduplication filer tried to upgrade the fingerprint metadata to a new version at 15:00 every day with the message: “Fingerprint is being upgraded” and failed. It seems that the metadata upgrade is a very resource-intensive process and heavily affects I/O latency.

This volume was not a VMware datastore, but it sit on the same aggregate together with the several VMFS LUNs. Here what happened to the VMware I/O latency every day at 15:00 (click to enlarge):


I deleted the host name and the datastores names from the graph. You can see the large latency spike, which won’t turn yourVMs into kernel panic, but it’s not the thing you would want your production environment to experience every day.

The solution was simple. After space was increased on this volume, deduplication metadata upgrade performed successfully and problem went away. Additionally, deduplication was shifted to off-hours.

The simple lesson to learn: don’t schedule deduplication during the day, you never know what could possibly go wrong.

Storwize V7000 with vSphere 5 storage configuration

December 1, 2012

storwizeInformation on how to configure Storwize for optimal performance is very scarce. I’ll try to build some understanding of it from bits an pieces gathered throughout the Internet and redbooks.

Barry Whyte gave many insights on Storwize internals in his blog. Particularly his “Configuring IBM Storwize V7000 and SVC for Optimal Performance” series of posts. I’ll quote him here. The main Storwize redbook “Implementing the IBM Storwize V7000 V6.3” is mostly an administration guide and gives no useful information on the topic. I find “SAN Volume Controller Best Practices and Performance Guidelines” way more helpful (Storwize firmware is built on SVC code).

Total Number of MDisks

That’s what Barry says:

… At the heart of each V7000 controller canister is an Intel Jasper Forrest (Sandy Bridge) based quad core CPU. … When we added the tried and trusted (SSA) DS8000 RAID functionality in 2010 (6.1.0) we therefore assigned RAID processing on a per mdisk basis to a single core. That means you need at least 4 arrays per V7000 to get maximal CPU core performance. …

Number of MDisks per Storage Pool

SVC Redbook:

The capability to stripe across disk arrays is the single most important performance advantage of the SVC; however, striping across more arrays is not necessarily better. The objective here is to only add as many arrays to a single Storage Pool as required to meet the performance objectives.

If the Storage Pool is already meeting its performance objectives, we recommend that, in most cases, you add the new MDisks to new Storage Pools rather than add the new MDisks to existing Storage Pools.

Table 5-1 shows the recommended number of arrays per Storage Pool that is appropriate for general cases.

Controller type       Arrays per Storage Pool
DS4000/DS5000         4 - 24
DS6000/DS8000         4 - 12
IBM Storwise V7000    4 - 12

The development recommendations for Storwize V7000 are summarized below:

  • One MDisk group per storage subsystem
  • One MDisk group per RAID array type (RAID 5 versus RAID 10)
  • One MDisk and MDisk group per disk type (10K versus 15K RPM, or 146 GB versus 300 GB)

There are situations where multiple MDisk groups are desirable:

  • Workload isolation
  • Short-stroking a production MDisk group
  • Managing different workloads in different groups

We recommend that you have at least two MDisk groups, one for key applications, another for everything else.

Number of LUNs per Storage Pool

SVC Redbook:

We generally recommend that you configure LUNs to use the entire array, which is especially true for midrange storage subsystems where multiple LUNs configured to an array have shown to result in a significant performance degradation. The performance degradation is attributed mainly to smaller cache sizes and the inefficient use of available cache, defeating the subsystem’s ability to perform “full stride writes” for Redundant Array of Independent Disks 5 (RAID 5) arrays. Additionally, I/O queues for multiple LUNs directed at the same array can have a tendency to overdrive the array.

Table 5-2 provides our recommended guidelines for array provisioning on IBM storage subsystems.

Controller type                     LUNs per array
IBM System Storage DS4000/DS5000    1
IBM System Storage DS6000/DS8000    1 - 2
IBM Storwize V7000                  1

General considerations

vsphere5-logoLets take a look at vSphere use case scenario on top of Storwize with 16 x 600GB SAS drives in control enclosure and 10 x 2TB NL-SAS in extension enclosure (our personal case).

First of all we need to decide how many arrays we need. Do we have different workloads? No. All storage will be assigned to virtual machines which have in general the same random read/write access pattern. Do we need to isolate workloads? Probably yes, it’s generally a good idea to separate highly critical production VMs from everything else. Do we have different drive types? Yes. Obviously we don’t want to mix drive types in one RAID. Are we going to make different RAID types? Again, yes. RAID 10 is appropriate on SAS and RAID 5 on NL-SAS. So two MDisks – one RAID 10 on SAS and one RAID 5 on NL-SAS would be enough. Storwize nodes have 4 cores each. It may seem that you would benefit from 4 MDisks, but in fact you won’t. Here what Barry says:

In the case where you only have 1 or 2 HDD arrays, then the core stuff doesn’t really come into play. Its only when you get to larger systems, where you are driving more I/O than a single RAID core can handle that you need to spread them.

This is also true if you are running all SSD arrays, so 24x SSD would be best split into 4 arrays to get maximum IOPs, whereas 24x HDD are not going to saturate a single core, so (if you could create a 23+P! [ you can’t 15+P is largest we support ] then it would perform as well as 2x 11+P etc

To storage pools. In our example we have two MDisks, so you simply make two storage pools. In future if you hit performance limit, you create additional MDisks and then you have two options. If each MDisk separately is able to sustain your performance requirements, you make additional storage pools and redistribute workload between them. If you have huge load on storage and even redistribution of VMs between two arrays doesn’t help, then you better combine two MDisks of each type in its own storage pool for striping between MDisks.

Same story for number of LUNs. IBM recommends one to one LUN to MDisk relationship. But read carefully. Recommendation comes from the fact that different workloads can clash and degrade array performance. But if we have generally the same I/O patterns coming to the array it’s safe to make several LUNs on it, until latency is in the acceptable range. Moreover, when it comes to vSphere and VMFS, it’s beneficial to have at least two volumes in terms of manageability. With several LUNs you will at least have an ability to move VMs between LUNs for reconfiguration purposes. Also keep in mind that ESXi 5 hypervisor limit each host to storage queue of depth 32 per LUN. It means that if you have one big LUN and many VMs running on the host, you can quickly reach queue limit. On the other hand do not create too many LUNs or you will oversubscribe storage processors (SPs).

Sample configuration

IBM recommends constructing both RAID 10 and RAID 5 arrays from 8 drives + 1 spare drive. But since we have 16 SAS and 10 NL-SAS I would launch CLI and create two arrays: one 14 drives + 2 spares RAID 10 and one 8 drives + 2 spares RAID 5 (or 9 drives + 1 spare, but it’s not a good idea to create RAID with uneven number of drives). Each RAID in its own pool. Several LUNs in each pool. I would go for 2TB LUNs.

Jumbo Frames justified?

March 27, 2012

When it comes to VMware on NetApp, boosting  performance by implementing Jumbo Frames is always taken into consideration. However, it’s not clear if it really has any significant impact on latency and throughput.

Officially VMware doesn’t support Jumbo Frames for NAS and iSCSI. It means that using Jumbo Frames to transfer storage traffic from VMkernel interface to your storage system is the solution which is not tested by VMware, however, it actually works. To use Jumbo Frames you need to activate them throughout the whole communication path: OS, virtual NIC (change to Enchanced vmxnet from E1000), Virtual Switch and VMkernel, physical ethernet switch and storage. It’s a lot of work to do and it’s disruptive at some points, which is not a good idea for production infrastructure. So I decided to take a look at benchmarks, before deciding to spend a great amount of time and effort on it.

VMware and NetApp has a TR-3808-0110 technical report which is called “VMware vSphere and ESX 3.5 Multiprotocol Performance Comparison Using FC, iSCSI, and NFS”. Section 2.2 clearly states that:

  • Using NFS with jumbo frames enabled using both Gigabit and 10GbE generated overall performance that was comparable to that observed using NFS without jumbo frames and required approximately 6% to 20% fewer ESX CPU resources compared to using NFS without jumbo frames, depending on the test configuration.
  • Using iSCSI with jumbo frames enabled using both Gigabit and 10GbE generated overall performance that was comparable to slightly lower than that observed using iSCSI without jumbo and required approximately 12% to 20% fewer ESX CPU resources compared to using iSCSI without jumbo frames depending on the test configuration.
Another important statement here is:
  • Due to the smaller request sizes used in the workloads, it was not expected that enabling jumbo frames would improve overall performance.

I believe that 4K and 8K packet sizes are fair in case of virtual infrastructure. Maybe if you move large amounts of data through your virtual machines it will make sense for you, but I feel like it’s not reasonable to implement Jumbo Frames for virual infrastructure in general.

The another report finding is that Jumbo Frames decrease CPU load, but if you use TOE NICs, then no sense once again.

VMware supports jumbo frames with the following NICs: Intel (82546, 82571), Broadcom (5708, 5706, 5709), Netxen (NXB-10GXxR, NXB-10GCX4), and Neterion (Xframe, Xframe II, Xframe E). We use Broadcom NetXtreme II BCM5708 and Intel 82571EB, so Jumbo Frames implementation is not going to be a problem. Maybe I’ll try to test it by myself when I’ll have some free time.

Links I found useful: