Tag Archives: IOPS

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

Scaling to 1 Million IOPS and beyond linearly!

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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.

http://www.youtube.com/watch?v=B-RBDtKgQTo&feature=youtu.be

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.

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