Tag Archives: ATS

Unlimited VMs per datastore? Its not a myth with Nutanix!

Posted on by
5

For many years, I have been asked on countless occasions questions relating to how many VMs can (or should) be placed in one datastore.

In fact, just this morning I was asked this same question, and I decided to whip up a quick post.

I have previously posted an Example Architectural Decision relating to Datastore sizing for Block based storage. What this example was aimed to show was a how things like RPO/RTO and performance should be taken into consideration when choosing a datastore size.

The above example is not a hard and fast rule, but an example of one deployment which I was involved in.

There is a great article written on this topic by VCDX, Jason Boche (@jasonboche), titled  “VAAI and the Unlimited VMs per Datastore Urban Myth” which covers in great detail this topic as it relates to block based storage, being iSCSI, FC & FCoE.

But what about NFS, and what about with Hyper-converged solutions like Nutanix?

NFS has gained significant popularity in recent years, and in my opinion, people who know what they are talking about, no longer refer to NFS as “Tier 3 Storage” which was once common.

With traditional storage solutions, generally only a smaller number of controllers can actively serve IO to the one NFS mount, so the limiting factor preventing running more virtual machines per NFS mount, in my experience was performance but things like RPO/RTO were and are important considerations.

NFS does not suffer from SCSI reservations which resulted in increased latency ,which is what VAAI, specifically the Atomic Test & Set or ATS primitive helped too all but eliminate for block based datastores.

LUNs are limited by there queue depth, which in most cases is 32 (sometimes 64). This is also a limiting factor, as all the VMs in a datastore (LUN) share the same queue which can lead to contention. SIOC helps manage the contention by ensuring fairness based on share values, but it does not solve the issue.

NFS on the other hand has a much larger queue depth, in fact its basically unlimited as shown below.

NFSqueuedepth

So as NFS does not suffer from SCSI reservations, or queue depth issues, what is limiting us having hundreds or more VMs per datastore?

It comes down to how many active storage controllers are able to service the NFS mount, and the performance of the storage controller/s. In addition to this your business requirements around RPO/RTO. In other words, if a NFS mount is lost, how quickly can you recover.

For most traditional shared storage products,

1. Have only 1 or 2 active controllers – thus potentially limiting performance which would lead to lower VMs per NFS datastore.

2. Do snapshots at the NFS mount layer, so if you need to recover an entire NFS mount, the larger it is, the longer it may take.

For Nutanix, by default, NFS is used to present the Nutanix Distributed File System (NDFS) to vSphere, however the key difference between Nutanix and traditional shared storage is every controller in the Nutanix cluster, can and does Actively serve IO to any datastore in the cluster concurrently.

So the limit from a performance perspective is gone thanks to Nutanix scale out, shared nothing architecture, with one virtual storage controller (CVM) per Nutanix node. The number of nodes that’s can be scaled too, is also unlimited. An example of Nutanix ability to scale can be found here – Scaling to 1 million IOPS and beyond, Linearly!

Next what about the RPO/RTO issue? Well, Nutanix does not rely on LUNs or NFS mounts for our data protection (or snapshots), this is all done at a VM layer so your RPO/RTO is now per VM, which gives you much more flexibility.

With Nutanix, you can literally run hundreds or even thousands of VMs per NFS datastore, without performance or RPO/RTO problems thanks to scale out, shared nothing architecture and the Nutanix Distributed File System.

There are some reasons why you may choose to have multiple NFS datastores even in a Nutanix environment, these include, if you want to enable Compression and/or De-duplication which are enabled/disabled on a per container (or datastore) level. As some workloads don’t compress or dedupe well, these types of workloads should be excluded to reduce the overhead on the cluster.

It is important to note, Nutanix uses a concept called a “Storage Pool” which contains all the storage for the Nutanix cluster. On top of a “Storage Pool” you create “Containers” (or datastores). This means regardless of if you have 1 or 100 datastores, they all still sit on top of the one “Storage Pool” which means you still have access to the same amount of storage capacity, with no silos for maximum capacity utilization (and performance!).

Lastly, Nutanix does not suffer from the same availability concerns as traditional shared storage where a single LUN could potentially be lost. This is due to the distributed architecture of the Nutanix solution. For more information on how Nutanix is more highly available than traditional shared storage, check out “Scale out, Shared Nothing Architecture Resiliency by Nutanix”

Check out a screen shot of one cluster with ~800 VMs on a single datastore. Note: The sub millisecond latency and 14K IOPS w/ ~900MBps throughput. Not bad!

800VMsonDatastore

Example Architectural Decision – Datastore (LUN) Sizing with Block Based Storage

Posted on by
2

Problem Statement

In a vSphere environment, What is the most suitable Datastore (LUN) sizing to use for to support both production & development workloads to ensure minimum storage overhead and optimal performance?

Requirements

1. RTO 4hrs
2. RPO 12hrs
3. Support Production and Test & Development Workloads
4. Ensure optimal storage capacity utilization
5. Ensure storage performance is both consistent & maximized
6. Ensure the solution is fully supported
7. Minimize BAU effort (Monitoring)

Assumptions

1. Business critical applications are excluded
2. Block based storage
3. VAAI is supported and enabled
4. VADP backups are being utilized
5. vSphere 5.0 or later
6. Storage DRS will not be used
7. SRM is in use
8. LUNs & VMs will be thin provisioned
9. Average size VM will be 100GB and be 50% utilized
10. Virtual machine snapshot will be used but not for > 24 hours
11. Change rate of average VM is <= 15% per 24 hour period
12. Average VM has 4GB Ram
13. No Memory reservations are being used
14. Storage I/O Control (SOIC) is not being used
15. Under normal circumstances storage will not be over committed at the storage array level.
16. The average maximum IOPS per VMs is 125 (16Kb) (MBps per VM <=2)
17. The underlying storage has sufficient performance to cater for the average maximum IOPS per VM
18. A separate swap file datastore will be configured per cluster

Constraints

1. Must used existing storage solution (Block Based Storage)

Motivation

1. Increase flexibility
2. Ensure physical disk space is not unnecessarily wasted
3. Create a Scalable solution
4. Ensure high performance
5. Ensure high utilization of storage resources by reducing “islands” of unused capacity
6. Provide flexibility in the unit size of partial SRM failovers

Architectural Decision

The standard datastore size will be 3TB and contain up to 25 standard virtual machines.

This is based on the following

25 VMs per datastore X 100GB (Assumes no over commitment) = 2500GB

25 VMs w/ 4GB RAM = 100GB minus 0Gb reservation = 100GB vswap space to be stored on the swap file datastore

25 VMs w/ Snapshots of up to 15% =  375GB

Total = 2500GB + 375GB = 2875GB

Average capacity used per VM = 115GB

Justification

1. In worst case scenario where every VM has used 100% of its VMDK capacity and has 4GB RAM with no memory reservation and a snapshot of up to 15% of its size the 3TB datastore will still have 197GB remaining, as such it will not run out of space.
2. The Queue depth is on a per datastore (LUN) basis, as such, having 25 VMs per LUNs allows for a minimum of 1.28 concurrent I/O operations per VM based on the standard queue depth of 32 although it is unlikely all VMs will have concurrent I/O so the average will be much higher.
3. Thin Provisioning minimizes the impact of situations where customers demand a lot of disk space up front when they only end up using a small portion of the available disk space
4. Using Thin provisioning for VMs increases flexibility as all unused capacity of virtual machines remains available on the Datastore (LUN).
5. VAAI automatically raises an alarm in vSphere if a Thin Provisioned datastore usage is at >= 75% of its capacity
6. The impact of SCSI reservations causing performance issues (increased latency) when thin provisioned virtual machines (VMDKs) grow is unlikely to be an issue for 25 low I/O VMs and with VAAI is no longer an issue as the Atomic Test & Set (ATS) primitive alleviates the issue of SCSI reservations.
7. As the VMs are low I/O it is unlikely that there will be any significant contention for the queue depth with only 25 VMs per datastore
8. The VAAI UNMAP primitive provides automated space reclamation to reduce wasted space from files or VMs being deleted
9. Virtual machines will be Thin provisioned for flexibility, however they can also be made Thick provisioned as the sizing of the datastore (LUN) caters for worst case scenario of 100% utilization while maintaining free space.
10. Having <=25 VMs per datastore (LUN) allows for more granular SRM fail-over (datastore groups)

Alternatives

1.  Use larger Datastores (LUNs) with more VMs per datastore
2.  Use smaller Datastores (LUNs) with less VMs per datastore

Implications

1. When performing a SRM fail over, the most granular fail over unit is a single datastore which may contain up to 25 Virtual machines.

2. The solution (day 1) does not provide CapEx saving on disk capacity but will allow (if desired) over commitment in the future

Thanks to James Wirth (VCDX#83) @JimmyWally81 for his contributions to this example decision.

Related Articles

1. Datastore (LUN) and Virtual Disk Provisioning (Thin on Thick)

2. Datastore (LUN) and Virtual Disk Provisioning (Thin on Thin)

3. Virtual Machine vSwap Location

CloudXClogo

 

Example Architectural Decision – Datastore (LUN) and Virtual Disk Provisioning (Thin on Thin)

Posted on by
2

Problem Statement

In a vSphere environment, What is the most suitable disk provisioning type to use for the LUN and the virtual machines to ensure minimum storage overhead and optimal performance?

Requirements

1. Ensure optimal storage capacity utilization
2. Ensure storage performance is both consistent & maximized

Assumptions

1. vSphere 5.0 or later
2. VAAI is supported and enabled
3. The time frame to order new hardware (eg: New Disk Shelves) is <= 4 weeks
4. The storage solution has tools for fast/easy capacity management

Constraints

1. Block Based Storage

Motivation

1. Increase flexibility
2. Ensure physical disk space is not unnecessarily wasted

Architectural Decision

“Thin Provision” the LUN at the Storage layer and “Thin Provision” the virtual machines at the VMware layer

(Optional) Do not present more LUNs (capacity) than you have underlying physical storage (Only over-commitment happens at the vSphere layer)

Justification

1. Capacity management can be easily managed by using storage vendor tools such eg: Netapp VSC / EMC VSI / Nutanix Command Center
2. Thin Provisioning minimizes the impact of situations where customers demand a lot of disk space up front when they only end up using a small portion of the available disk space
3. Increases flexibility as all unused capacity of all datastores and the underlying physical storage remains available
4. Creating VMs with “Thick Provisioned – Eager Zeroed” disks would unnessasarilly increase the provisioning time for new VMs
5. Creating VMs as “Thick Provisioned” (Eager or Lazy Zeroed) does not provide any significant benefit (ie: Performance) but adds a serious capacity penalty
6. Using Thin Provisioned LUNs increases the flexibility at the storage layer
7. VAAI automatically raises an alarm in vSphere if a Thin Provisioned datastore usage is at >= 75% of its capacity
8. The impact of SCSI reservations causing performance issues (increased latency) when thin provisioned virtual machines (VMDKs) grow is no longer an issue as the VAAI Atomic Test & Set (ATS) primitive alleviates the issue of SCSI reservations.
9. Thin provisioned VMs reduce the overhead for Storage vMotion , Cloning and Snapshot activities. Eg: For Storage vMotion it eliminates the requirement for Storage vMotion (or the array when offloaded by VAAI XCOPY Primitive) to relocate “White space”
10. Thin provisioning leaves maximum available free space on the physical spindles which should improve performance of the storage as a whole
11. Where there is a real or perceved issue with performance, any VM can be converted to Thick Provisioned using Storage vMotion not disruptivley.
12. Using Thin Provisioned LUNs with no actual over-commitment at the storage layer reduces any risk of out of space conditions while maintaining the flexibility and efficiency with significantly reduce risk and dependency on monitoring.
13. The VAAI UNMAP primitive provides automated space reclamation to reduce wasted space from files or VMs being deleted

Alternatives

1.  Thin Provision the LUN and thick provision virtual machine disks (VMDKs)
2.  Thick provision the LUN and thick provision virtual machine disks (VMDKs)
3.  Thick provision the LUN and thin provision virtual machine disks (VMDKs)

Implications

1. If the storage at the vSphere and array level is not properly monitored, out of space conditions may occur which will lead to downtime of VMs requiring disk space although VMs not requiring additional disk space can continue to operate even where there is no available space on the datastore
2. The storage may need to be monitored in multiple locations increasing BAU effort
3. It is possible for the vSphere layer to report sufficient free space when the underlying physical capacity is close to or entirely used
4. When migrating VMs from one thin provisioned datastore to another (ie: Storage vMotion), the storage vMotion will utilize additional space on the destination datastore (and underlying storage) while leaving the source thin provisioned datastore inflated even after successful completion of the storage vMotion.
5.While the VAAI UNMAP primitive provides automated space reclamation this is a post-process, as such you still need to maintain sufficient available capacity for VMs to grow prior to UNMAP reclaiming the dead space

Related Articles

1. Datastore (LUN) and Virtual Disk Provisioning (Thin on Thick)CloudXClogo