Tag Archives: flash

What’s .NEXT 2016 – All Flash Everywhere!

Posted on by
Reply

I am pleased to say Nutanix and our OEMs are now offering even more flexibility with our “Configure To Order” option (a.k.a CTO) by allowing any node type, yes ANY node type to be configured with all flash.

Why is this so cool, well Nutanix and our OEMs (Dell XC & Lenovo HX) have a wide range of models which customers can choose from and for customers who require large usable capacity of high performance storage, this is a simple way to get a pre-certified solution with all the flexibility of build your own without the risks.

AllFlashEverywhere

With this increased level of flexibility, the argument for BYO/HCL is all but moot in my opinion.

So let’s think about what this means.

The NX-8150, a 1 node per 2RU product (which I was heavily involved in the design of) will now support 24 x SSDs!

Even with the currently supported SSDs (1.92TB each), this would mean >46TB of RAW SSD capacity along with dual Broadwell CPUs and up to 768GB RAM.

Note: Higher capacity SSDs are coming soon to provide even more capacity!

Now with 24 x SSDs that is some serious power!

What’s also exciting is this doesn’t just mean higher flash capacity, it also means higher performance. This is because Nutanix persistent write buffer (OpLog) is striped across all SSDs in a node, this means the write performance can benefit from all SSDs in the node, in the case of that’s NX8150 that’s 24 drives!

Combine this with the fact Nutanix now supports any node as storage only, and this gives customers near unlimited flexibility without the risk/complexity of BYO/HCL options.

After all, the hardware is commodity, all the value is in the software so who cares what HW it runs on as long as its reliable.

Summary:

  • Configure to Order (CTO) now allows any node type to be configured with All Flash
  • All Flash nodes can also be Storage Only nodes
  • Write Performance takes advantage of all SSDs in a node
  • Nutanix Configure to Order (CTO) option makes the argument for BYO/HCL options all but moot.

Related .NEXT 2016 Posts

Scaling problems with traditional shared storage

Posted on by
12

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

Data Locality & Why is important for vSphere DRS clusters

Posted on by
5

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