Category Archives: Storage

VMware Host Isolation Response in a Nutanix Environment #NoSAN

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I was recently discussing the Nutanix solution with a friend of mine and fellow VCDX, Michael Webster (@vcdxnz001) and he asked what the recommended Host Isolation Response is for Nutanix.

At this stage I must advise there is no formal recommendation, but an Official vSphere on Nutanix Best Practice guide is in the works and will be released asap.

Back to my conversation with Michael, Being that Nutanix is an IP Storage solution, my initial feeling is that Host isolation Response should be set to “Shutdown”, but I didn’t go into any more detail with Michael, so I thought it best to post a quick explanation.

This post also assumes basic knowledge of vSphere as well as the Nutanix platform, for those of you who are not familiar with Nutanix please review the following links prior to reading the remainder of this post.

About Nutanix | How Nutanix Works | 8 Strategies for a Modern Datacenter

So back on topic, in other posts I have written for IP Storage, such as (Example Architectural Decision – Host Isolation Response for IP Storage) I have concluded that “Shutdown” was the most suitable setting and recommended specifying isolation addresses of the NAS controllers.

But as Nutanix changes the game and has one virtual storage controller per ESXi host, so does this change the recommendation?

In short, No, but for those who are interested, here is why.

If we leave the default isolation address, (being the default gateway for ESXi Management), in the event the gateway is down, it will trigger an isolation response even if the rest of the network is operating fine, thus an unnecessary outage would occur.

If we configure das.isolationaddress1 & 2 with the Management IP address of any two Nutanix Controller VMs (192.168.1.x , 192.168.1.y in my below diagram) then an isolation response will only be triggered if both Nutanix Controller VMs (CVMs) are not responding, in which case, the VMs should be Shutdown as the Nutanix cluster may not be function properly with two Controllers offline concurrently as its configured by default for N+1 (or replication factor of “2” in Nutanix speak).

The below is a high level example of the above configuration.

NutanixHostIsolation

Related Articles

1. Example Architectural Decision – Host Isolation Response for a Nutanix Environment

2. Storage DRS and Nutanix – To use, or not to use, that is the question?

3. VMware HA and IP Storage

Storage DRS and Nutanix – To use, or not to use, that is the question?

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Storage DRS (SDRS) is an excellent feature which was released with vSphere 5.0 in late 2011. For those of you who are not familiar with SDRS I recommend reading the following article prior to reading the rest of this post as SDRS knowledge is assumed from now on.

Understanding VMware vSphere 5.1 Storage DRS

This post also assumes basic knowledge of the Nutanix platform, for those of you who are not familiar with Nutanix please review the following links prior to reading the remainder of this post.

About Nutanix | How Nutanix Works | 8 Strategies for a Modern Datacenter

Storage DRS & Nutanix – To use, or not to use, that is the question?

With Storage DRS (SDRS), both capacity and performance can be managed, but what should SDRS manage in a Nutanix environment?

Lets start with performance. SDRS can help ensure optimal performance of virtual machines by enabling the I/O metric for SDRS recommendations as shown in the screen shot below.

SDRSsettingsIOmetricCircledSmall

Once this is done, SDRS will evaluate I/O every 8 hours (by default) and where the configured latency threshold is exceeded, perform a cost/benefit analysis before deciding to make a migration recommendation or do nothing.

So the question is, does SDRS add value in a Nutanix environment from a performance perspective?

The Nutanix solution adopts the “Scale-out” methodology by having one (1) Nutanix Controller VM (CVM) per Nutanix Node (ESXi Host) and then presents NFS datastore/s to the vSphere cluster which are serviced by all CVMs. The CVMs use intelligent auto-tiering to ensure optimal performance. The way this works at a high level, is as follows.

Data is written to an SSD tier (either PCIe SSD such as Fusion-io or SATA SSD) before being migrated off to a SATA tier once the blocks are determined to be “Cold” and if/when required, promoted back the an SSD tier when they become “Hot” again for improved read performance.

As with other vendor storage solutions with auto tiering technologies (such as FAST-VP , FlashPools etc) the same recommendation around SDRS and the I/O metric is true for Nutanix, leave it disabled.

So, at this point we have concluded the I/O metric will be “Disabled”, lets move onto Capacity management.

The Nutanix solution presents large NFS datastore/s to the ESXi hosts (Nutanix nodes) which are shared across all ESXi hosts in one or more vSphere clusters.

When using SDRS, it can manage initial placement of a new Virtual machine based on the configured “Utilized Space” metric (shown below) to ensure there is not a capacity imbalance between the datastores in a datastore cluster, as well as move virtual machines around when new machines are provisioned to ensure the balance is maintained.

UtilizedSpaceSDRS

So this is a really good feature which I have and do recommend in several scenarios, however the Nutanix solution presents typical a small number of large NFS datastores to the vSphere cluster (or clusters) which are serviced by all Controller VMs (CVMs) in the Nutanix cluster. Using SDRS for initial placement does not add much (if any) value as the initial placement will almost always be on the same large NFS datastore.

Where actual physical capacity becomes an issue, space saving technologies such as compression can be enabled, or the environment can be granularly scaled by adding just a single additional Nutanix node which linearly scales the solution from both a capacity and performance perspective.

The only real choice is when you choose to present two (or more) datastores where one datastore leverage’s the Nutanix compression technology. This is a very easy scenario for a vSphere admin to choose the placement of a VM and is the same amount of administrative effort as choosing a datastore cluster which would be a collection of datastores either using compression, or not depending on the workloads.

As a result there is no advantage to using SDRS to manage utilized space.

In conclusion, Storage DRS is a great feature when used with storage arrays where performance does not scale linearly or provide intelligent tiering to address I/O bottlenecks and/or where your environment has large numbers of datastores where you need to actively manage capacity.

As performance and capacity management are intelligently managed natively by the Nutanix solution, the requirement (or benefit) provided by SDRS is negated, as a result there is no requirement or benefit for using SDRS with a Nutanix solution.

Related Articles

1. Example Architectural Decision – VMware DRS automation level for a Nutanix environment

 

 

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

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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

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