Nutanix X-Ray Benchmarking tool – Extended Node Failure Scenario

In the first part of this series, I introduced Nutanix X-Ray benchmarking tool which has been designed very differently to traditional benchmarking tools as the performance of the app is the control and the variable is the platform,not the other way around.

In the second part, I showed how Nutanix AHV & AOS could maintain the performance while utilising snapshots to achieve the type of recovery point objective (RPO) that is expected in production environments, especially with business critical workloads whereas a leading hypervisor and SDS platform could not.

In this part, I will cover the Extended Node Failure Scenario in X-Ray and again compare Nutanix AOS/AHV and a leading hypervisor and SDS platform in another real world scenario.

Let’s start by reviewing what the description of the X-ray Extended node failure scenario.

XrayExtendedNodeFailureScenario

I really like that X-ray has a scenario which shows a simulated node failure as this is bound to happen regardless of the platform you choose, and with hyperconverged platforms the impact of a node failure is arguably higher than traditional 3-tier as the nodes contain some data which needs to be recovered.

As such, it is critical before choosing a HCI platform to understand how it behaves in a failure scenario which is exactly what this scenario demonstrates.

XrayNodeFailureComparison

Here we can see the impact on the performance of the surviving VMs following the power being disconnected via the out of band management interface.

The Nutanix AOS/AHV platform continues to run at a very steady rate, virtually without impact to the VMs. On the other hand we see that after 1 hour the other platform has a high impact with significant degradation.

This clearly shows the Acropolis Distributed Storage Fabric (ADSF) to be a superior platform from a resiliency perspective, which should be a primary consideration when choosing a platform for any production environment.

Back in 2014, I highlighted the Problems with RAID and Object Based Storage for data protection and in a follow up post I discussed how Nutanix Acropolis Distributed Storage Fabric (ADSF) compares with traditional SAN/NAS RAID and hyper-converged solutions using Object storage for data protection.

The above results clearly demonstrate the problems I discussed back in 2014 are still applicable to even the most recent versions of a leading hypervisor and SDS platform. This is because the problem is the underlying architecture and bolting on new features is at best masking the constraints of the original architectural decision which has proven to be significantly flawed.

This scenario clearly demonstrates the criticality of looking beyond peak performance numbers and conducting a thorough evaluation of a platform prior to purchase as well as comprehensive operational verification prior to moving any platform into production.

Related Articles:

Nutanix X-Ray Benchmarking tool Part 1 – Introduction

Nutanix X-Ray Benchmarking tool Part 2 -Snapshot Impact Scenario

Nutanix X-Ray Benchmarking tool – Introduction

I’ve been excited to write about X-ray for a while now, but I’ve not had the time. But the opportunity has presented itself where I could kill two birds with one stone and do some performance comparisons between Nutanix AHV Turbo Mode and other platforms on the same underlying hardware, so what better time to review X-ray as part of this process.

So for those of you who have not heard of X-Ray, it wouldn’t be unreasonable to assume it’s just another benchmarking tool to further muddy the waters when comparing different platforms.

However X-Ray takes a different approach, to quote Paul Updike who is part of Nutanix Technical Marketing Engineering:

Normally performance is your test variable and you measure the effect on the system. X-ray is upside down, performance of an app in a VM is the control and our test variable is the system. We measure the effect on the control.

So if all you want is “hero numbers” you’ve come to the wrong place, although  X-Ray does have a peak performance micro-benchmark test built-in, it’s far from real world in comparison to the other tests within X-ray.

The X-Ray virtual appliance is recommended to be ran on a cluster which is not the target for the testing, such as a management cluster. But for those environments where this additional hardware may not be available, it can also be deployed on VirtualBox or VMware Workstation on your PC or laptop.

Also if you have an Intel NUC, you could deploy Nutanix Community Edition (CE) and run X-Ray on CE which is based on AHV.

In addition to the different approach X-ray takes to benchmarking, I like that X-ray performs fully automated testing across multiple hypervisors including ESXi, AHV as well as different underlying storage. This helps ensure consistent and fair comparisons between platforms, or even comparisons between Nutanix node types if you decide to compare model types before making a purchasing decision.

X-ray has several built in tests which are focused not just on outright performance, but on how a system functions and performs during node failure/s, with snapshots as well as during rolling upgrades.

The reason Nutanix took this approach is because it is much more real world than simply firing up I/O meter with lots of outstanding I/O with a 100% random 4k read. In the real world, customers performance upgrades (hopefully regularly to take advantage of new functionality and performance!), hardware does fail when we can least afford it and using space efficient snapshots as part of an overall backup strategy makes a lot of sense.

Now let’s take a look at the X-Ray interface starting with an overview:

XrayOverview

X-Ray is designed to be similar to PRISM to keep that great Nutanix look and feel. The tool is very simple to use with three sections being Tests, Analyses and Targets.

To get started is very quick/easy, just open the “Targets” view (shown below) and select “New Target”.

XrayTargets

In the “Create Target” popup, you simply, provide a name for the target e.g.: “Nutanix NX-3460 Cluster AHV”, select the Manager type, being either vCenter for ESXi environments or PRISM for AHV.

Then select the cluster type, being Nutanix (i.e.: A Nutanix NX, Dell XC, Lenovo HX or HPE/Cisco software only) OR “Non-Nutanix” which is for comparisons with platforms not running Nutanix AOS such as VMware vSAN.

XrayCreateTarget

For VMware environments, you then provide the vCenter details and regardless of the hardware type or platform, you supply the out of band management (e.g.: IPMI) details. The out of band management details allow X-ray to perform simulated hardware failure tests which are critical to any product evaluation and pre-production operational verification testing.

X-Ray then allows you to select the cluster, container (or datastore) and networking (e.g.: Port Group) to be used for the testing.

XrayCreateTarget_Cluster

X-ray then discovers the nodes (e.g.: ESXi Hosts) and allows you to add nodes and confirm the IPMI type to ensure maximum compatibility.

XrayCreateTarget_Node

Now hit “Save” and you’re good to go! Pretty simple right?

Now to run a test, simply click the test you want to run and select “Add to Queue”.

Xray_RunTestVDISim

The beauty of this is X-ray allows you to queue as many tests as you want and leave the system to run the tests, say overnight or over a weekend without requiring you to monitor them and start tests one by one.

In between tests the target systems are cleaned up (i.e.: data and VMs deleted) to ensure consistent / fair results even when running test packages one after another.

Once a test has been ran, you can view the results in the X-Ray GUI (as shown below):

XrayTestsOverview

You can also generate a PDF report for individual tests or perform analysis between two tests including of different platforms:

XrayAnalyses

The above results show and overlay between two platforms, the first being AHV (although it’s incorrectly named Turbo mode when it was ran using non Turbo mode AOS version 5.1.1). As we can see, AHV even without turbo mode was more consistent than the other platform.

To create a PDF report, simply use the “Actions” drop down menu and select “Create Report”.XrayCreateReport

The report will create a report which covers off details about X-ray, the Target cluster/s, the scenario being tested and the test results.

XrayTOCReport

It will show simple results such as if the test passed (i.e.: Completed the required tasks) and things like test duration as shown below:

XrayReportTargetOverview

X-Ray also provides built-in tests for mixed workloads, which is much more realistic than testing peak performance for point (or siloed) solutions which are become more and more rare these days. XrayMixedWorkloads

X-Ray’s built in tests are also auto scaling based on the cluster size of the target and allow tuning of the scenario. For example, in the VDI simulator scenario, Task, Knowledge or Power Users can be selected.

XRayVDISimulator
Summary:

X-Ray provides a tool which is free of charge, multi-hypervisor, multi-platform (including non-HCI) which is easy to use for proof of concepts, product comparisons as well as real world, operational verification.

I am working with the X-ray team to develop new built in test scenarios to simulate real world scenarios for business critical applications as well as to allow customers and 3rd parties to validate the benefits of functionality such as data locality.

Stay tuned for a series of posts covering Nutanix AHV Turbo Mode performance/functionality comparisons with other products.

It’s 2017, let’s review Thick vs Thin Provisioning

For a long time, it has been widely considered that thick provisioning is required to achieve maximum storage performance and for many years this was a good rule of thumb.

Before we get into details, what are Thick and Thin provisioning?

Thick provisioning is where storage allocated to a LUN, NFS mount or Virtual Disk (such as a VMDK in ESXi, VHDX in Hyper-V or vDisk in AHV) is zeroed out and/or fully reserved regardless of how much capacity is actually used.

Thick provisioning avoids a storage subsystem from having to zero out a block before writing new data which is one of the reasons higher performance could be achieved on many storage platforms.

Thin provisioning on the other hand is where storage allocated to a LUN or Virtual Disk is zeroed as data is written and allows physical capacity to be overcommitted.

The advantages of Thick provisioning included easier capacity management, or simply put a “What you see is what you get” as well as maximum performance on most platforms. But by maximum performance, even on older storage platforms the advantage was rarely significant as people would claim.

VMware conducted a Performance Study of VMware vStorage Thin Provisioning back in the ESXi 4.0 days (~2009) which I will briefly summarise.

On page 6 of the performance study the following graph shows the different in performance between Thin and Thick VMDKs during zeroing and post-zeroing.

As you can see the performance is almost identical.

The disadvantages though were and remain significant to this day which include an inability to overcommit storage, meaning physical free space has to be maintained at multiple layers such as RAID group, LUN, Virtual Disk layers, leading to inefficiency.

The advantages of Thin provisioning include the ability to overcommit storage which results in more flexibility when sizing LUNs & Virtual Disks and less wasted space. The only real downsides were potentially increased capacity management complexity and lower performance.

I have previously written two example architectural decisions regarding using “Thin on Thin“, meaning thin provisioned virtual disks on a thin provisioned LUN or NFS mount as well as “Thin on Thick” meaning thin provisioned virtual disks on a thick provisioned LUN or NFS mount. These two examples cover off many of the traditional pros and cons between thick and think, so I won’t repeat myself here.

I never wrote an example design decision for Thick on Thick, but this was common practice when provisioning storage was time consuming, difficult and involved lengthly delays to engage subject matter experts.

In early 2015, I wrote a two part blog series where I explained it’s not as simple as you might think to calculate usable capacity where I compared SAN/NAS verses Nutanix. In part 1, I highlight that the LUN Provisioning Type is one area which can greatly impact the usable capacity of a traditional storage platform.

But fast forward into the era of hyper-converged platforms like Nutanix and some modern storage arrays and the major downsides of thin provisioning, being complexity of capacity management and reduced performance have not only been reduced, but at least in the case of Nutanix, have been eliminated all together.

Let’s address Capacity management w/ Nutanix:

Storage utilisation only needs to be monitored in ONE place, the storage summary which lives on the home screen of the Nutanix HTML 5 UI.

NutanixStorageSummary

No matter how many nodes in your cluster, number of containers (which translate to datastores in a VMware environment), virtual machines & virtual disks or physical servers connecting via ABS, this is the only place you need to monitor capacity.

There are no RAID groups, Disk Groups, Aggregates, LUNs etc where capacity needs to be managed. All nodes in a cluster contributed to the capacity of the cluster and even when one or more virtual machines use more capacity than a the node they run on, Nutanix Acropolis Distributed Storage Fabric (ADSF) takes care of it.

So issue #1, Capacity management, is solved. Now it’s onto the issue of performance.

Thin Provisioning Performance w/ Nutanix:

When running ESXi, Nutanix runs NFS datastores and supports thick provisioning via the VAAI-NAS Space reservation primitive as discussed in this post. This allows the creation of thick provisioned (Eager Zero or Lazy Zero Thick) VMDKs when traditionally NFS datastores did not support it.

However this was only required for Oracle RAC and VMware Fault Tolerance and was not a performance requirement.

However from a performance perspective, Thin provisioning actually outperforms thick on intelligent storage such as Nutanix. In the specific case of Nutanix, random write I/O is serviced by the fastest tier available (e.g.: SSD) and via the operations log (OPLOG) which takes the random writes commits them to persistent media, and then coalesces them into sequential IO to then commit to SSD before tiering it off to lower cost storage in the case of hybrid nodes.

This means the write penalty for overwriting or zeroing blocks before writing new I/O is eliminated.

In fact if you configure thick provisioned virtual disks, as the zeros (or whitespace) is being written by the hypervisor, the Nutanix storage fabric acknowledges every I/O and discards the zeros in favour of storing metadata and simply reserving the capacity. In simple terms, this just means Nutanix has to acknowledge a whole bunch of nothing and the thick provisioning is achieve with a simple reservation as opposed to zeroing out many GBs or TBs of storage.

This means thick provisioning is actually lower performance than thin provisioning on Nutanix.

With modern, intelligent storage, there is limited if any benefits to using thick provisioning, the only example I can think of is to artificially inflate the deduplication ratio as thick provisioned virtual disks tend to have a lot of zeros all of which dedupe. I wrote an article titled: “Deduplication ratios – What should be included in the reported ratio?” which covers off this point in detail but in short, don’t create unnessasary data (in this case, zeros) just to inflate your dedupe ratio, it just wastes storage controller resources and achieves no additional benefits.

The following is a comprehensive list of the real world advantages of using thick provisioning on Nutanix.

This space is intentionally left blank

Summary:

For the best efficiency and performance when deploying virtual machines or storage for physical servers via ABS on Nutanix, use thin provisioning!