Scale Out Shared Nothing Architecture Resiliency by Nutanix

At VMware vForum Sydney this week I presented “Taking vSphere to the next level with converged infrastructure”.

Firstly, I wanted to thank everyone who attended the session, it was a great turnout and during the Q&A there were a ton of great questions.

I got a lot of feedback at the session and when meeting people at vForum about how the Nutanix scale out shared nothing architecture tolerates failures.

I thought I would summarize this capability as I believe its quite impressive and should put everyone’s mind at ease when moving to this kind of architecture.

So lets take a look at a 5 node Nutanix cluster, and for this example, we have one running VM. The VM has all its data locally, represented by the “A” , “B” and “C” and this data is also distributed across the Nutanix cluster to provide data protection / resiliency etc.


So, what happens when an ESXi host failure, which results in the Nutanix Controller VM (CVM) going offline and the storage which is locally connected to the Nutanix CVM being unavailable?

Firstly, VMware HA restarts the VM onto another ESXi host in the vSphere Cluster and it runs as normal, accessing data both locally where it is available (in this case, the “A” data is local) and remotely (if required) to get data “B” and “C”.


Secondly, when data which is not local (in this example “B” and “C”) is accessed via other Nutanix CVMs in the cluster, it will be “localized” onto the host where the VM resides for faster future access.

It is importaint to note, if data which is not local is not accessed by the VM, it will remain remote, as there is no benefit in relocating it and this reduces the workload on the network and cluster.

The end result is the VM restarts the same as it would using traditional storage, then the Nutanix cluster “curator” detects if any data only has one copy, and replicates the required data throughout the cluster to ensure full resiliency.

The cluster will then look like a fully functioning 4 node cluster as show below.


The process of repairing the cluster from a failure is commonly incorrectly compared to a RAID pack rebuild. With a raid rebuild, a small number of disks, say 8, are under heavy load re striping data across a hot spare or a replacement drive. During this time the performance of everything on the RAID pack is significantly impacted.

With Nutanix, the data is distributed across the entire cluster, which even with a 5 node cluster will be at least 20 SATA drives, but with all data being written to SSD then sequentially offloaded to SATA.

The impact of this process is much less than a RAID rebuild as all Nutanix controllers in the cluster participate and take a portion of the workload as a result the impact per disk, per controller ,per node and importantly for production VMs running in the cluster, is greatly reduced.

Essentially, the larger the cluster, the faster the cluster can repair itself, and the lower the impact on production workloads.

Now lets talk about a subsequent ESXi host failure, now we have two failed nodes, and three surviving nodes, and only one copy of data “A” , “B” and “C” as shown below.


Now the Nutanix “Curator” detects only one copy of data “A”, “B” and “C” exists and starts to replicate copies of “A”, “B” and “C” across the cluster. This results in the below which is a fully functional and redundant cluster, capable of surviving yet another failure as shown below.


Even in this scenario, where two ESXi hosts are lost, the environment still has 60% of its storage controllers (and performance), as compared to a typical traditional storage product where the loss of just two (2) controllers can have your environment completely offline, and even if you only lost a single controller, you would only have 50% of the storage controllers (and performance) available.

I think this really highlights what VMware and players like Google, Facebook & Twitter have been saying for a long time, scaling out not up, and shared nothing architecture is the way of the future. The only question is who will be dominant in bringing this technology to the mass market, and I think you know who I have my money on.

Scaling problems with traditional shared storage

At VMware vForum Sydney this week I presented “Taking vSphere to the next level with converged infrastructure”.

Firstly, I wanted to thank everyone who attended the session, it was a great turnout and during the Q&A there were a ton of great questions.

One part of the presentation I got a lot of feedback on was when I spoke about Performance and Scaling and how this is a major issue with traditional shared storage.

So for those who couldn’t attend the session, I decided to create this post.

So lets start with a traditional environment with two VMware ESXi hosts, connected via FC or IP to a Storage array. In this example the storage controllers have a combined capability of 100K IOPS.


As we have two (2) ESXi hosts, if we divide the performance capabilities of the storage controllers between the two hosts we get 50K IOPS per node.

This is an example of what I have typically seen in customer sites, and day 1, and performance normally meets the customers requirements.

As environments tend to grow over time, the most common thing to expand is the compute layer, so the below shows what happens when a third ESXi host is added to the cluster, and connected to the SAN.


The 100K IOPS is now divided by 3, and each ESXi host now has 33K IOPS.

This isn’t really what customers expect when they add additional servers to an environment, but in reality, the storage performance is further divided between ESXi hosts and results in less IOPS per host in the best case scenario. Worst case scenario is the additional workloads on the third host create contention, and each host may have even less IOPS available to it.

But wait, there’s more!

What happens when we add a forth host? We further reduce the storage performance per ESXi host to 25K IOPS as shown below, which is HALF the original performance.


At this stage, the customers performance is generally significantly impacted, and there is no easy or cost effective resolution to the problem.

….. and when we add a fifth host? We continue to reduce the storage performance per ESXi host to 20K IOPS which is less than half its original performance.


So at this stage, some of you may be thinking, “yeah yeah, but I would also scale my storage by adding disk shelves.”

So lets add a disk shelf and see what happens.


We still only have 100K IOPS capable storage controllers, so we don’t get any additional IOPS to our ESXi hosts, the result of adding the additional disk shelf is REDUCED performance per GB!

Make sure when your looking at implementing, upgrading or replacing your storage solution that it can actually scale both performance (IOPS/throughput) AND capacity in a linear fashion,otherwise your environment will to some extent be impacted by what I have explained above. The only ways to avoid the above is to oversize your storage day 1, but even if you do this, over time your environment will appear to become slower (and your CAPEX will be very high).

Also, consider the scaling increments, as a solutions ability to scale should not require you to replace controllers or disks, or have a maximum number of controllers in the cluster. it also should scale in both small, medium and large increments depending on the requirements of the customer.

This is why I believe scale out shared nothing architecture will be the architecture of the future and it has already been proven by the likes of Google, Facebook and Twitter, and now brought to market by Nutanix.

Traditional storage, no matter how intelligent does not scale linearly or granularly enough. This results in complexity in architecture of storage solutions for environments which grow over time and lead to customers spending more money up front when the investment may not be realised for 2-5 years.

I’d prefer to be able to Start small with as little as 3 nodes, and scale one node at a time (regardless of node model ie: NX1000 , NX3000 , NX6000) to meet my customers requirements and never have to replace hardware just to get more performance or capacity.

Here is a summary of the Nutanix scaling capabilities, where you can scale Compute heavy, storage heavy or a mix of both as required.


Scaling to 1 Million IOPS and beyond linearly!

The below video shows how you can start (very) small with Nutanix, and scale to 1 Million IOPS and beyond in a linear fashion, in one node, or one block (4 node) increments and enjoy linear scalability.

So next time your looking to buy storage, why not buy what you need today, and not what you might need in 3 or 5 years time, and scale incrementally as required without the need for controller head swaps, or throwing out any equipment.