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Identifying & Resolving Excessive CPU Overcommitment (vCPU:pCore ratios)

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Help! My performance is terrible and my consultant/vendor says it’s due to high/excessive CPU overcommitment! What do I do next?

Question: “How much CPU overcommitment is ok?”.

The answer is of course “It depends” and there are many factors including but not limited to, workload type, physical CPUs and how complimentary the workloads (other VMs) are.

Other common questions include:

“How much overcommitment do I have now?”

&

“How do I know if overcommitment is causing a performance problem?”.

Let’s start with “How much overcommitment do I have now?”.

With Nutanix this is very easy to work out, first goto the Hardware page in PRISM and click Diagram, then select one of your nodes as shown below.

PRISMHWDiagram

Once you’ve done that you will see below in the “Summary” section the CPU Model, No. of CPU Cores and No. of Sockets as shown below.

HostDetailsPRISMCPUHW

In this case we have 2 sockets and 20 cores total for a total of 10 physical cores per socket.

If you have multiple node types in your cluster, repeat this step for each different node type in your cluster. Then simply add up the total number of physical cores in the cluster.

In my example, I have three nodes, each with 20 cores for a total of 60 physical cores.

Next we need to find out how many vCPUs we’ve provisioned in the cluster. This can be found on the “VMs” page in PRISM as shown below.

ProvisionedvCPUsPRISM

So we have our 3 node cluster with 60 physical cores (pCores) and we have provisioned 130 vCPUs.

Now we can input the details into my vSphere Cluster Sizing Calculator and work out the overcommitment including our desired availability level (in my case, N+1) and we get the following:

ClusterSizingCalc2

The calculator is designed to be conservative and show information assuming the resources (CPU/RAM) required for the configured availability level are removed from the calculation. Put simply, the vCPU:pCore ratio assumes the N+1 host is not in the cluster which is how I personally size environments, especially for business critical applications.

The calculator shows us we have a 3.25:1 vCPU:pCore ratio.

For business critical applications like SQL, Exchange, Oracle, SAP etc, I always recommend sizing without CPU overcommitment (so <= 1:1) and ensuring the VMs are right sized to avoid poor performance and wasted resources.

Now that we know our overcommitment ratio, what’s next?

We need to find out if our overcommitment level is consistent with our original design and assess how the Virtual Machines are performing in the current state. A good design should call out the application requirements and critical performance factors such as CPU overcommitment and VM placement (e.g.: DRS Rules).

“How do I know if overcommitment is causing a performance problem?”.

One of the best ways to measure if a VM has CPU scheduling contention is by looking at “CPU Ready” or “Stolen time” in the AHV (or KVM) world.

CPU ready is basically the delay between time when the VM requests to be scheduled onto CPU cores and the time when it’s actually scheduled. One of the easiest way’s to present this is in a percentage of total time that the VM is waiting to be scheduled.

How Much CPU Ready is OK? My rule of thumb is:

<2.5% CPU Ready
Generally No Problem.

2.5%-5% CPU Ready
Minimal contention that should be monitored during peak times

5%-10% CPU Ready
Significant Contention that should be investigated & addressed

>10% CPU Ready
Serious Contention to be investigated & addressed ASAP!

With that said, the impact of CPU Ready will vary depending on your application so even 1% should not be ignored especially for business critical applications.

As CPU Ready is a critical performance metric, Nutanix decided to display this in PRISM on a per VM basis so customers can easily identify CPU scheduling contention.

Below we see the summary of a VMs performance which can be found on the VM’s page in PRISM after highlighting a VM. At the bottom of the page we see a graph showing CPU Ready.

VMPerformanceNTNXPRISM

CPU Ready of <2.5% is unlikely to be causing major issues for the majority of VMs, but in some latency sensitive applications like databases or video/voice, 2.5% could be causing noticeable issues so never disregard looking into CPU ready in your troubleshooting.

I recommend looking at a VM and if it’s showing even minimal CPU ready is say >1% and it’s a business critical application, follow the troubleshooting steps in this article until CPU Ready is <0.5% and measure the performance difference.

Key Point: If you have applications like SQL Always on availability groups, Oracle RAC or Exchange DAGs, one VM suffering CPU Ready will likely be having a flow on impact to the other VMs trying to communicate (or replicate) to it. So ensure all “dependancies” for your VM/app are not suffering CPU Ready before looking into other areas.

In short, Server A with no CPU Ready can be impacted when trying to communicate to Server B and being delayed because Server B has High CPU Ready.

The reason I bring this up is because it’s important not to get tunnel vision when looking at performance problems.

Now to the fun part, Troubleshooting/Resolutions to CPU Ready!

  1. Right size your VMs

Do NOT ignore this step! Your CPU overcommitment ratio is irrelevant, Right Sizing will always improve the efficiency and performance of your VMs. There is an increasing overhead at the hypervisor layer for scheduling more vCPUs, even with no overcommitment so ensure VMs are not oversized.

A common misconception is that 90% CPU utilisation is a bottleneck, in fact this can be a sign of a right sized VM. We need to ensure vCPUs are sized for peaks but unless a VM is pinned at 100% CPU for long periods of time, a short spike to 100% is not necessarily a problem.

Here is an example of the benefits of VM right sizing.

Once you have right sized your VMs, move onto step 2.

2. Size or place VMs within NUMA boundaries

First what is a NUMA boundary? It’s pretty simple, take the number of cores and divide by the number of sockets and that’s the NUMA boundary and also the maximum number of vCPUs a VM can be if you wish to benefit from maximum memory performance and optimal CPU scheduling.

The total host RAM is also a factor so divide the total RAM by the number of sockets and that’s the maximum RAM a VM can be assigned without breaching the NUMA boundary and paying an approx 30% performance penalty on memory performance.

Example: I had a customer who had MS Exchange running with 12vCPU / 96GB VMs on Nutanix nodes with 12 cores per socket. Exchange was running poorly (in the end due to a MS bug) but they insisted the problem was insufficient CPU. So they forced the customer to increase the VM to 18 vCPUs.

This did not solve the performance problem AND in fact made performance worse as the VM now suffered from very higher CPU Ready as VMs larger than a NUMA boundary can experience much higher CPU ready especially on hosts running other workloads. Moving back to 12 vCPUs relieved the CPU Ready and then Microsoft ultimately resolved the case with a patch.

3. Migrate other VMs off the host running the most critical VM

This is a really easy step to alleviate CPU scheduling contention and allows you to monitor the performance benefit of not having CPU overcommitment.

If the virtual machines performance improves you’ve likely found at least one of the causes of the performance problem. Now comes the harder part. Unless you can afford to have a single VM per host, you now need to identify complimentary workloads to migrate back onto the host.

What’s a complimentary workload? 

I’m glad you asked! Let me give you an example.

Let’s say we have a 10vCPU / 128GB RAM SQL Server VM which is right sized (of course) and our host is the NX-8035-G4 with 2 sockets of 10 cores per socket (20 cores total) and 256GB RAM. Being SQL we’ll also assume it has high IO requirements as it’s the backend for a business critical application.

Being Nutanix we also have a Controller VM using some resources (say 8vCPUs and 32GB RAM). For those who are interested see: Cost vs Reward for the Nutanix Controller VM (CVM)

A complimentary workload would have one or more of the following qualities:

a) Less than 96GB RAM (Host RAM 256GB, minus SQL VM 128GB, minus CVM 32GB = 96GB remaining)

b) vCPU requirements <= 2 (This would mean a 1:1 vCPU:pCore ratio)

c) Low vCPU requirements and/or utilization

d) Low IO requirements

e) Low capacity requirements (this would maximise the amount of SQL data which would remain local to the node for maximum read performance with data locality).

f) A workload which uses CPU/Storage at a different time of the day to the SQL workload.

e.g.: SQL might be busy 8am to 6pm, but workload may drop significantly outside those hours. A VM with high CPU/Storage IO requirements that runs from 7pm to midnight would potentially be a very complimentary workload as it would allow higher overcommitment and with minimal/no performance impact due to the hours of operation of the VMs not overlapping.

4. Migrate the VM onto a node with more physical cores

This might be an obvious one but a node with more physical cores has more CPU scheduling flexibility which can help reduce CPU Ready. Even without increasing the vCPUs on the VM, the VM has a better chance of getting time on the physical cores and therefore should perform better.

5. Migrate the VM onto a node with a higher CPU clock-rate

Another somewhat obvious one but it’s very common for vendors and customers to quote the number of vCPUs as a requirement when a “vCPU” is not a unit of measurement. A vCPU at best with no overcommitment is equal to one physical core and it goes downhill for there. Physical cores also vary in clock-rate (duh!) so a faster clock rate can have a huge impact on performance especially for those pesky single threaded applications.

Note: CPUs with higher clock rates typically have fewer cores, so don’t make the mistake of moving a VM to a node where it exceeds the NUMA boundary!

6. Turn OFF advanced power management on the physical server & use “High Performance” as your policy (in ESXi)

Advanced Power Management settings can save power and in some cases have minimal impact on performance, but when troubleshooting performance problems, especially around business critical applications, I recommend eliminating Power Management as a potential cause and once the performance problem is resolved, test re-enabling it if you desire.

7. Enable Hyperthreading (HT)

Hyperthreads can provide significant CPU scheduling advantages and in many cases improve performance despite a hyperthreading providing generally fairly low overall performance (typically 10%-30%) in CPU benchmarks.

Long story short, a VM in a Ready state is doing NOTHING, so enabling HT can allow it to be doing SOMETHING, which is better than NOTHING!

Also hypervisors are pretty smart, they preferentially schedule vCPUs to pCores so the busy VMs will more often than not be on pCores while the VMs with low vCPU requirements can be scheduled to hyperthreads. Win/Win.

Note: Some vendors recommend turning HT off, such as Microsoft for Exchange. But, this recommendation is really only applicable to Exchange running on physical servers. For virtualization always, always leave HT enabled and size workloads like Exchange with 1:1 vCPU to pCore ratios, then you will achieve consistent, high performance.

For anyone struggling with a vendor (like Microsoft) who is insisting on disabling HT when running business critical apps, here is an Example Architectural Decision on Hyperthreading which may help you.

Example Architectural Decision – Hyperthreading with Business Critical Applications (Exchange 2013)

8. Add additional nodes to the cluster

If you have right sized, migrated VMs to nodes with complimentary workloads, ensured optimal NUMA configurations, ensured critical VMs are running on the highest clock-rate CPUs etc and you’re still having performance problems, it may be time to bite the bullet and add one or more nodes to the cluster.

Additional nodes provide more CPU cores and therefore more CPU scheduling opportunities.

A common question I get is “Why can’t I just use CPU reservations on my critical VMs to guarantee them 100% of their CPU?”

In short, using CPU reservations does not solve CPU ready, I have also written an article on this topic – Common Mistake – Using CPU reservations to solve CPU ready

Wildcard: Add storage only nodes

Wait, what? Why would adding storage only nodes help with CPU contention?

It’s actually pretty simple, lower latency for read/write IO means less CPU WAIT which is the time the CPU is “waiting” for an IO to complete.

e.g.: If an I/O takes 1ms on Nutanix but 5ms on a traditional SAN, then moving the VM to Nutanix will mean 4ms less CPU WAIT for the VM, which means the VM can use it’s assigned vCPUs more efficiently.

Adding storage only nodes (even where the additional capacity is not required) will improve the average read/write latency in the cluster allowing VMs to be scheduled onto a physical core, get the work done, and release the pCore for another VM or to perform other work.

Note: Storage only nodes and the way data is distributed throughput the cluster is a unique capability for Nutanix. See the following article for an example on how performance is improved with storage only nodes with NO modification required to the VMs/Apps.

Scale out performance testing with Nutanix Storage Only Nodes

Summary:

There are a lost of things we can do to address CPU Ready issues, including thinking outside the box and enhancing the underlying storage with things like storage only nodes.

Other articles on CPU Ready

1. VM Right Sizing – An Example of the benefits

2. How Much CPU Ready is OK?

3. Common Mistake – Using CPU Reservations to solve CPU Ready

4. High CPU Ready with Low CPU Utilization

Nutanix gets Microsoft blessing for unique ESRP for a real world MS Exchange ESRP solution on All Flash

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I am pleased to announce that Microsoft have approved Nutanix latest ESRP (Exchange Storage Review Program) submission for a 50,000 user deployment of MS Exchange on Nutanix NX-8150 all flash platform running the next generation hypervisor, AHV!

What’s unique about this you might ask?

  1. It’s the first hyper-converged (HCI) all flash ESRP solution (to compliment Nutanix existing Hybrid ESRP solutions for 24k users on Hyper-V and 30k users on AHV)
  2. The first multiple Exchange VM per node solution!!
  3. The first ESRP to provide MS Exchange Server role requirements calculator solution design
  4. The solution was performance tested/validated with N-1 nodes to simulate performance in the event a node had failed and was not replaced
  5. The solution supports the 1GB mailboxes without any assumed data reduction from compression, deduplication or Erasure Coding (EC-X)

The last point is key. Many vendors/solutions assume high data reduction ratios when sizing which adds risk to a project as I explained in Sizing infrastructure based on vendor Data Reduction assumptions. Nutanix (and me personally) rather give customers a guaranteed business outcome and while our data reduction is very effective especially for MS Exchange data, it can and does vary between customers. An ESRP should be a guaranteed outcome, and that’s what this unique ESRP from Nutanix delivers.

A major problem with many, if not most ESRP submissions is that they are not real world solutions, just storage platforms which can deliver high enough IOPS to potentially support a real world solution.

When designing the solution I planned to put forward for ESRP, I used an actual real world design for a Nutanix customer and ensures it was sized to be 100% real world.

For example, from a compute perspective the solution was sized with no CPU overcommitment and within the recommended maximum of 24 CPUs both of which ensure optimal CPU performance.

CPU sizing also ensures Exchange VMs fit within the NUMA node of the Nutanix node which ensures optimal memory performance, which is another key area to ensure optimal Exchange performance.

In addition, The VMs are sized to be under the Microsoft recommended CPU utilization threshold for a “Worst Failure Mode” of ≤ 80 percent.

From a real world perspective, MS Exchange is dependant on Active Directory. As a result the solution is also sized to support all the required Active Directory Global Catalog cores running on the same infrastructure.

From an availability and resiliency perspective, the solution is sized for N+1 at the infrastructure layer to compliment the N+1 at the MS Exchange DAG layer. This delivers customers a solution which has protection from multiple concurrent failures which is essential for Mission Critical applications.

In the real world, things change and having a solution which scales to support more users, more messages per day and greater mailbox capacity is essential.

The Nutanix NX-8150 All Flash ESRP discusses a scalable and repeatable model where the solution can be increased in size from supporting 1 GB mailboxes to >2 GB simply by choosing (configure to order) 3.84 TB drives vs. the 1.92 TB drives tested for this solution.

Another option is when the storage capacity is reaching a high threshold such as 80%+, customers can non disruptively add storage nodes to expand capacity. This can be done without any change at the OS or MS Exchange application layer and new capacity (and performance!) is available instantly.

Did you know Nutanix allows mixing all-flash & hybrid? This means the most active data (e.g.: Most recent email) is running in an all flash configuration and older mail is automatically and transparently migrated to the lower cost hybrid nodes.

From a storage performance perspective, the solution was tested with in-line compression enabled which is Nutanix official recommendation for MS Exchange as it provides excellent data reduction with no significant overheads.

Another focus are for Nutanix in the real world is reducing CAPEX and OPEX. A great example of this is the entire solution (excluding networking) uses just 10 rack units (RUs) per datacenter. While other vendors storage ESRPs will claim lower RU requirements, they excluding the physical servers required for the solution. Nutanix is advising the requirements for the compute and storage for the solution to be totally transparent.

This means the solution does not require a large investment in your datacenter or co-location and is cost effective to power and cool making the solution environmentally friendly as well.

From a performance perspective, the Nutanix solution was tested in an N-1 configuration to show the performance which can be achieved after the failure of a node within the cluster.

Even with a failed node, the solution achieves excellent performance with average database read and log write latency in the low 1ms range sustained for the 24 stress test required for ESRP submissions.

A few performance highlights:

  1. Nutanix achieved an average of 5172 IOPS per MS Exchange Jetstress instance with just 4 threads!
  2. Database read latency avg of just 1.05ms
  3. Log write latency avg of just 1.21ms
  4. Database backup performance of 215MB/sec per database which equates to more than 1.7GBps per node!

While the achieved performance vastly exceeds the requirements for Exchange, the key factor is the reduced CPU WAIT time achieved which results in much greater CPU efficiency than a physical Exchange server with JBOD storage. Meaning a virtualised exchange server on Nutanix (even hybrid systems) is more efficient than Microsoft Preferred architecture using physical servers and JBOD storage.

You may be asking yourself, why does this matter? The answer is simple. MS Exchange becomes inefficient when scaled up beyond 24 cores so the more efficient the usage of those cores, the more users, messages per day and better user experience can be achieved without scaling up or adding more servers.

So without further delay, I have provided the direct link to the document below for you convenience.

Nutanix ESRP – NX-8150-G5 All Flash 50,000 Users

ntnxallflashesrp

The truth about storage benchmarking

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Recently I was asked to review some performance testing done by an external party and my initial impression was the performance was well below what I expected.

So over the weekend I setup a block in my lab to reproduce the tests to see if the results were firstly repeatable, and if so, what performance would I get with and without tuning.

The only significant difference between my hardware and the hardware used by the 3rd party was that I used old dual socket Ivy Bridge E5-2670 2.6Ghz 8c processors and the 3rd party had a much newer dual Broadwell E5-2640 v4 2.4Ghz processors.

If we compare the two processors using CPUBoss.com we see the following:

CPUboss1

Not surprisingly the Broadwell E5-2640 v4 processor is faster, but possibly less than you would expect with a 16.28% better PassMark per core, and in my opinion, the per core value quite importaint especially when considering business critical applications.

None the less, a 16.28% performance improvement per core will be a significant factor for a benchmark with Nutanix as the Controller VM (CVM) is powered by the CPU of the host.

I thought I would whip up a quick post about performance benchmarking to show how different performance results can be on the same hardware depending on just a few factors and why storage benchmarking, especially competitive benchmarking, cannot and should not be trusted when making purchasing decisions.

This test was for a 10k user MS Exchange deployment and the hardware used for testing was performed on in both cases was 1 x 1.92TB SSD and 3 x 4TB SATA drives and they were both tested on the same GA Nutanix AOS build.

The required (or Target) IOPS was just 216 per MS Exchange instance (VM) as shown below by the Jetstress report.

TargetIO

This target is calculated by Jetstress when using the “Exchange Mailbox Profile” test scenario with the following configuration:

MailboxProfile2

The resulting SSD vs SATA ratio makes this test largely about the limitations of SATA performance as >87% of data is being read from the SATA tier.

TierUsage

Test 1: The Jetstress dataset was created and then the performance test was immediately ran for 2hrs with no pre-warming of the metadata or read cache.

Achieved Transactional I/O: 200.663
Avg Log Write Latency: 1.06ms
Avg DB Write Latency: 1.4ms
Avg DB Read Latency: 14ms

This result was 15.61% lower than the 3rd parties result and interestingly if we correct for CPU core performance, it’s less than 1% difference. As this was in line with my expectation knowing the importance of CPU clock speed, I would say for this testing that the baseline results were comparable.

Test 2: The Nutanix tiering was tuned to suit large working sets (which vastly exceed the SSD tier) and the Jetstress dataset was created and the performance test was immediately ran for 2hrs again with no pre-warming of the metadata or read cache.

Before we get to the results, I want to point out that Jetstress is in some ways is very good but in other ways a very unrealistic benchmarking tool as the entire dataset is “active” which is not the case in the real world. However, in one way this is a good thing because a passing Jetstress result in my experience means the production deployment performs very well from a storage perspective especially when using tiered storage which is built around the assumption not all data is active. As a result, a Jetstress test could be considered a “worse case scenario” style test for intelligent tiered storage.

Achieved Transactional I/O: 249.623
Avg Log Write Latency: 0.99ms
Avg DB Write Latency: 1.5ms
Avg DB Read Latency: 12ms

Test 3: I then setup Jetstress as per Nutanix MS Exchange best practices and ran the test again with no pre-warming of the metadata or read cache.

Achieved Transactional I/O: 389.753
Avg Log Write Latency: 0.95ms
Avg DB Write Latency: 2.0ms
Avg DB Read Latency: 17ms

Test 4: I then lowered the Jetstress thread count to the lowest value (roughly 33% lower) which I estimated would achieve the target IOPS (this is to simulate real world requirements) and ran the test again with no pre-warming of the metadata or read cache.

Achieved Transactional I/O: 300.254
Avg Log Write Latency: 0.94ms
Avg DB Write Latency: 1.5ms
Avg DB Read Latency: 12ms

Note: Test 4 achieved the highest I/O per thread.

Test 5: The same configuration as Test 4 but with pre-warming of the metadata cache.

Achieved Transactional I/O: 334
Avg Log Write Latency: 0.98ms
Avg DB Write Latency: 1.9ms
Avg DB Read Latency: 12.4ms

Some of you might be asking, how did test 4 achieve higher transactional I/O and with lower read and write latency than Test 1 & 2 with less threads. Shouldn’t a higher thread count achieve higher IOPS?

The reasons is because the original thread count was pushing the SATA drives past their capabilities, leading to excessive latency. Lowering the thread count allowed the SATA drives to operate at somewhere around their most efficiency range leading to lower latency.

Test 6: The same configuration as Test 5 but with tuned extent cache (RAM read cache) and 100% medadata cached.

Achieved Transactional I/O: 362.729ms
Avg Log Write Latency: 0.92ms
Avg DB Write Latency: 1.7ms
Avg DB Read Latency: 12ms

As we can see from Test 1 through to Test 6, the performance differs by up to 81% depending on how the platform is configured.

Side note and future looking statement. Many of the optimisations I performed above wont be required for long as many of the areas these optimisations help improve are being addressed in upcoming code. In saying that, for a business critical application like Exchange, I don’t think it’s a problem doing some optimisation as long as 90% of the workloads run well by default and we’re only tuning for the 10% (vBCA) workloads.

But out of interest, what would happen if we enabled data reduction? How much of a performance hit would that take?

Test 7: The same configuration as Test 6 but with In-line compression enabled.

Achieved Transactional I/O: 751.275
Avg Log Write Latency: 0.97ms
Avg DB Write Latency: 3.4ms
Avg DB Read Latency: 5.9ms

That’s a 107.46% increase in transactional IO and with in-line compression! Log write latency remained sub ms and read latency has almost halved.

Note: As Jetstress data is highly compressible, (Nutanix achieves 8:1 or higher with non default settings), I tuned the compression slice size to give a more realistic data reduction ratio. The ratio for this test was 3.99:1 and the ratio of SSD to SATA was almost exactly 50% as shown below.

TierUsageAfterCompression

Why did performance improve so much with In-Line compression? Well there is two main reasons:

  1. More data is being served from the SSD tier as compression allows more effective SSD tier capacity.
  2. Reads from SATA are faster as less physical data needs to be read to service an I/O due to it being compressed. The higher the compression ratio, the more this can improve.

As we can see, the results varied significantly and had I wanted to optimise the test further, I could have achieved even higher performance but there was no need. The requirements for the solution were already achieved and in the case of Test 7, the requirements were exceeded by 247% meaning the solution had heaps of headroom.

Nutanix best practice is to enable In-line compression for MS Exchange and other databases such as Oracle and SQL as per my tweet below.

This testing was performed on Nutanix Acropolis Hypervisor (AHV) but was not using the upcoming Turbo mode, which will further improve performance and lower overheads.

This is a key point many people forget when benchmarking. If we assume the platforms in question are scalable (e.g.: Like Nutanix), it doesn’t matter if one platform does 100k IOPS and another does 200k IOPS if your requirements are 20k IOPS. Both platforms capabilities vastly exceed the requirement (10k IOPS) from a performance perspective, so performance is not longer a significant factor in your purchasing decision.

Question: Are the above performance results genuine?

All of the above results could be argued to be genuine results, at the same time none of the above represent the best performance that could be achieved, yet the results could be used to try and create FUD if they are improperly represented (which is almost always the case with competitive comparisons whether intentional or otherwise).

Let’s say this was your proof of concept, What should be the take away from benchmarking results like this?

Simple: The solution meets/exceeds your performance requirements.

Now for the point of this article: The truth about storage benchmarking is that there are so many variables that can affect the results that unless you’re truely experienced in benchmarking your applications AND an expert in the platforms you’re benchmarking, your results are unlikely to be indicative of the platforms capabilities and therefore of very little value.

If you’re benchmarking Vendor A vs Vendor B, it’s a waste of time doing “Like for Like” benchmarking because the Virtual machine and application settings which are optimal for one vendor, will likely be different for the other vendor. e.g.: SAN vs HCI.

On the other hand, a more valid test would be vendor A’s best practices vs vendor B best practices, but again if one vendor Jetstress achieves 500 and the other achieves 400, that 20% higher performance is all but irrelevant if your requirements are say, 216 like in this case.

A very good example of invalid “like for like” benchmarking would be to size the active working set (i.e.: The capacity of the data you plan to benchmark against) to fit within the cache/SSD tier of one platform, but exceed the cache/SSD capacity of the other platform. The results will be vastly different and will not be indicative of real world performance. This is what vendors do when competitive benchmarking and it’s likely one of the main reasons we see End User License Agreements (EULA) from most if not all storage vendors preventing publishing benchmark results without written agreement.

So the (unpopular) truth about storage benchmarking is it’s not as easy and building a VM and running I/O meter with the same profile on multiple system like some vendors and even 3rd party storage analysts would have you believe. The vast majority of people (customers, analysts and even vendors) doing benchmarking don’t have the skill/experience to produce repeatable or meaningful results, especially on multiple platforms.

In fact it’s unrealistic/unreasonable to expect a person (customer, vendor, consultant) to be an expert in multiple platforms, and very few people are!

Related Articles:

  1. Peak Performance vs Real World Performance
  2. The Key to performance is Consistency