Tag Archives: SSD

Scale Out Shared Nothing Architecture Resiliency by Nutanix

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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.

Nutanix5NodeCluster

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”.

Nutanix5nodecluster1failed

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.

5NodeCluster1FailedRebuild

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.

Nutanix5NodeCluster2failures1copydata

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.

Nutanix5NodeCluster2Failures

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

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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.

50kIOPS

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.

33KIOPS

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.

25KIOPS

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.

20KIOPS

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.

20KIOPSAddDiskShelf

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.

ScaingSolution

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

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Following on from my recent post titled “Data Locality & Why is important for vSphere DRS clusters” I would like to discuss at a high level how Write I/O works in the Nutanix Distributed File System, how the solution ensures high availability in the event of a node failure and what impact a failure has on performance.

Lets start with a typical Write operation.

The below diagram shows a three (3) node Nutanix cluster with a Guest VM starting to perform write I/O, this is represented in a simplistic manor by the three (3) Diamonds (Red, Yellow and Purple)

NutanixWriteIOstart

The write I/O is written to the local SSD tier (as is every Write in a Nutanix environment) as shown below.

NutanixWriteDataWrittenLocal

Before acknowledging the write the Nutanix Controller VM (CVM) then replicates a copy of the data across the Nutanix Distributed File System.

The below diagram illustrates what this looks like in a three node cluster.

NutanixWriteSyncToOtherNodes

Once the data in successfully written to other nodes within the cluster, the Write acknowledgement is given. This ensures data is consistent and always protected.

In a Nutanix cluster, as Controllers (Nutanix CVMs) are scaled linearly with the ESXi hosts, Write I/O is then spread over more controllers, reducing the chance of contention in the environment at both a storage controller and network layer as each controller shares 2 x 10Gb connections per node.

In the event of a node failure, in a vSphere cluster, HA will restart the failed VM/s onto a surviving node in the cluster.

The VM will start-up and operate as normal and where data is not local to the node (as discussed in detail in my post  “Data Locality & Why is important for vSphere DRS clusters“) the data will initially be accessed over 10Gb before being replicated locally for future reads.

NutanixHAAfterWithDataAccess

All future writes for the VM/s which have been restarted by HA on different nodes will perform at a similar rate (if not the same rate) as they did before the failure depending on how many nodes are in the cluster. Where the Network is not a bottleneck, there should be minimal/no difference in write performance after a node failure.

The Nutanix cluster will also detect a node has failed, and ensure two copies of all data are available, and in the above example where only one copy of the data exists, the cluster will replicate the required data to ensure High Availability (“Replication Factor” of 2) is maintained.

As this replication is done across multiple controllers and nodes, it is much faster and lower impact than a traditional RAID rebuild which most of us will be familiar with.

The end state of this process looks like this.

NutanixHAEndState

So in conclusion using a “scale-out” storage controller solution like Nutanix ensures consistent high write performance even immediately following a node failure by eliminating the requirement for RAID style rebuilds which are disk intensive and can lead to “Double Disk Failures” and data loss.

The replication of data being distributed across all nodes in the cluster ensures minimal impact to each Nutanix controller, ESXi host and the network while ensuring the data is re-protected as soon as possible.

Related Articles

1. Data Locality & Why is important for vSphere DRS clusters