Tag Archives: FCoE

Virtualizing Exchange on vSphere with NFS backed storage?

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For many years, customers have been realising the benefits of file based storage from one or more of the many storage vendors offering NFS.

NFS makes a ton of sense for virtualization, and virtualizing Business Critical applications such as Exchange, along with the rest of a company’s servers, can be a great way to reduce complexity and save on CAPEX/OPEX.

However, some vendors, have licensing or support statements which make this more difficult than it needs to be.

One such vendor is Microsoft.

Microsoft currently don’t support Exchange running inside a VMDK on an NFS datastore, even though the VMDK is a virtual SCSI device and acts/performs the same as if it was on a block based LUN, such as FC/FCoE or iSCSI.

I decided to reach out to a bunch of great guys in the virtualization community to try and get some awareness of this issue, and get Microsoft to update the outdated and technically invalid support statement.

As a result, the following TechNet forum article has been posted

Support for Exchange Databases running within VMDKs on NFS datastores

There is also a suggestion in the Microsoft Product improvement forum on the same topic, which as a result of the communities efforts in the past few weeks, have seen it sky rocket to the #1 improvement suggestion to microsoft.

The post and voting can be found here.

Support storing Exchange datat on VMDKs on File shares (NFS/SMB)

So please check out these two articles, and vote and leave your comments in support of this issue. Supporting Exchange in VMDKs on NFS is a No lose situation for customers, and that is what it is all about!

Related Articles:

Integrity of Write I/O for VMs on NFS Datastores Series

Part 1 – Emulation of the SCSI Protocol
Part 2 – Forced Unit Access (FUA) & Write Through
Part 3 – Write Ordering
Part 4 – Torn Writes
Part 5 – Data Corruption

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

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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

Data Locality & Why is important for vSphere DRS clusters

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I have had a lot of people reach out to me since VMworld SFO, where I was interviewed by Eric Sloof (@esloof) on VMworldTV (interview can be seen here) about Nutanix.

So I thought I would expand on the topic of Data Locality and why it is so important for vSphere DRS clusters to maintain consistent high performance and low latency.

So first, the below diagram shows three (3) Nutanix nodes, and one (1) Guest VM.

NutanixLocalRead

The guest VM is reading data from the local storage in the Nutanix node and as a result this read access is very fast. The read I/O will be served from one of 4 places.

1. Extent Cache (DRAM – For “Active Working Set”)
2. Local SSD (For “Active Working Set”)
3. Local SATA (Only for “Cold” data)

and the forth we will discuss is a moment.

So as a result for Read I/O

1. There is no dependency on a Storage Area Network (FCoE, IP, FC etc)
2. Read I/O from one node does not contend with another node
3. Read I/O is very low latency as it does not leave the ESXi host
4. More Network bandwidth is available for Virtual Machine traffic, ESXi Mgmt, vMotion , FT etc

But wait, the what happens if DRS (or a vSphere admin) vMotion’s a VM to another node? – I’m glad you asked!

The below shows what happens immediately after a vMotion

NutanixAftervmotion

As you can see, only the Purple data is local to the new node, so transparently to the virtual machine, if/when remote data is required by the VM (ie: The VMs “Active Working Set”) the Nutanix controller VM (CVM) will get the requested data over the 10GB Network in 1MB extents. (It does not do a bulk movement or “Storage vMotion” type movement of all the VMs data EVER!)

And, all future Write I/O will be written local, so future Read I/O will all be local by default.

So, the worst case scenario for a read I/O in a Nutanix environment, is that the required data is not available locally and the CVM will access the data over a 10GB network.

This scenario will only occur in situations where

1. Maintenance is occurring and hosts are rebooted
2. A Host Failure (HA restarts VM on another node)
3. Following a vMotion

Generally in BAU (Business as Usual) operation Read I/O should be local in the high 90% range.

So the worst case scenario for Read I/O on a vSphere Cluster running on Nutanix, is actually the Best case scenario for a traditional storage array, because in a traditional array all data is accessed over some form of storage network and generally via a small number of controllers.

It is important to note, the Nutanix DFS (Distributed File System) only accesses data over the network when its required by the VM at a granular (1MB extent) level. So only the “Active Working Set” will be accessed over the 10Gb network, before being copied locally, again in 1MB extents. So if the data is not “Active” having it remotely does not impact performance at all so moving the data would create an overhead on the environment for no benefit.

In the event 90% of a VMs data is on a remote node, but the “Active Working Set” is local, read performance will all be at local speeds, again from Extent Cache (DRAM), Local SSD or Local SATA (for “cold” data).

Now some vendors are working on or have some local caching capabilities which in my experience are not widely deployed and have various caveats such as Operating System version, and in guest drivers, but for the vast majority of environments today, these technologies are not deployed.

The Nutanix DFS has data locality built in, it works with any hypervisor , Guest OS and does not require any configuration.

So now you know why ensuring the Active Working Set (data) is as close to the VM is essential for consistent high performance and low latency.

Related Articles

1. Write I/O Performance & High Availability in a scale-out Distributed File System