Monthly Archives: June 2018

Google Wifi Review – 3 Wi-Fi Point Solution

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Since moving into a new place earlier this year, I’ve been struggling to get consistent Wi-fi signal/performance especially in the master bedroom. The master bedroom is the furthest room from my home office where I was running my TP-Link Archer D7 (AC1750) Dual-Band Wireless router.

After spending some time playing around trying to get better signal, I purchased the TP-Link AC750 Wi-Fi Range Extender and plugged it in in various positions between the master bedroom and the home office.

I eventually settled on the one location where the Range Extender was reporting maximum signal which was around 7m or 22ft from my master bedroom where I have a Samsung 75″ TV, Apple TV, Nintendo Switch, an iPad and two phones, one iPhone 8 and one Samsung Galaxy S9.

Performance was still inconsistent and I ultimately placed the TP-Link router right in the middle of the apartment which I would say helped a little bit, but ultimately did not solve the problem.

Note: My Sonos wireless speakers are not supported when using Range Extenders which is a real design flaw on Sonos’ part and a pain for customers. Ultimately I’m less than impressed with Sonos so I’ve got their Sound Bar and two wireless speakers and a sub sitting around doing nothing.

One of the many reasons for the Wi-Fi performance issues is likely to be the all to common scenario these days of being surrounded by a ton of Access Points. The below is what I see on my Macbook  when looking for networks, so in my case, Interference is likely a significant factor.

WiFiNoise

 

But long story short, I continued to get dropouts and inconsistent speeds so I bit the bullet and purchased the Google Wi-fi 3 pack (shown below).

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First impressions?

It’s nice and small, and uses USB-C for power so the cord is also small. You can see the device below beside my oversized Nutanix coffee mug for scale.

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It’s not a modem, so you’re stuck having multiple devices which is a bit annoying but not the end of the world.

Initial setup was a breeze, step by step instructions after downloading the Google Wifi app, the first device was detected and then verified by scanning a QV code on the base of the device which was cool but also very easy and saved manually entering numbers which saves time and avoids fat-fingering errors.

Now onto the exciting part, getting the “mesh” network setup and tested.

Once you run through the wizard, the app shows you a review of your network including the Wi-Fi name, Password and the Wi-fi points you have and their configuration, in my case, 1 “Primary” and two “Mesh” Wi-Fi points as shown below.

MeshSummary

The app has a cool “Network Check” functionality in the shortcuts menu (shown below).

Screenshot_20180626-164508_Google Wifi.jpg

The network check allows you to test the Internet speed, the connection quality between the access points “mesh” and the one I have found quite useful is the Test Wi-fi to all wireless devices currently connected to the network.

You can run each test individually or start all three as shown below.

Screenshot_20180626-164658_Google Wifi.jpg

Testing the internet speed is a quick and easy way to see how fast your connection is and saves downloading and using another app on your phone which is handy.

Below is how the test results are displayed, and for Australian internet, this is a pretty good result although it would be considered poor in many parts of the world.

Screenshot_20180626-154804_Google Wifi.jpg

Next up we have the “Test Mesh” option which is pretty important so kudos to Google for ensuring this was part of the app as it will avoid not technical people having to bug their I.T friends for help. At this stage I can hear all the I.T professionals all around the world rejoice!

Screenshot_20180626-154811_Google Wifi.jpg

The “Mesh test” is pretty quick and gives you a clear result as shown below.

Screenshot_20180626-154002_Google Wifi.jpg

This was my first “Mesh test” and while the result is not bad, I relocated the Wi-Fi points as it suggested and re-ran the test.

Screenshot_20180626-154845_Google Wifi.jpg

As we can see the result is now “Great” with full green bars which I have to admit I was very happy to see considering how annoying Wi-Fi had been over the past few months.

Next up, the Wi-Fi test for all connected devices. Prior to running the test I went around the apartment and turned on the three TVs, 2 Apple TVs, iPad, Samsung Galaxy Tablet, I also made sure all phones were on Wi-Fi as well as my laptop. 12 Devices in all.

As the test is running it appears to confirm if a device is Idle or not, and then proceeds to drive traffic to it. A couple of things I really like is that it clearly displays what device/s are connected to what Wi-fi point and the speed it was able to achieve.

WifiDevicesTest

One completed the app gives you a summary of the number of devices tested and their network performance as shown below.

12DevicesTested

Back to the main screen of the app, we can see a summary of the network telling us the access point is online and has 12 devices connected as confirms the internet is online.

NetworkSummary

If we go into “Devices” we can also see per device upload and download statistics so it’s quick and easy to identify if one or more devices are hogging the bandwidth.

DevicesRealTimeBandwidth

While I haven’t used the next to features, the app does allow you to setup a Guest Wi-fi network which is handy if you want to keep your devices isolated from guests and/or not give out your password because it matches your internet banking one.. haha!

Screenshot_20180626-161535_Google Wifi.jpg

Google also allows you to “pause” the internet for specific devices, such as your teenage child/ren and pause it on a set schedule if you choose which I think is a good addition for a home network.

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Performance when streaming multiple Ultra HD 4k shows on Netflix?

Moving on from the ease of setup and cool app functionality, let’s test how the network performs with 3 TVs streaming Ultra HD 4k (Netflix), my Samsung Tablet streaming YouTube Premium (4k HD) and my laptop streaming HD video (UFC Fight Pass).

Streaming4KHDon3TVsPlusLaptopandTablet

Above we can see some of the per device stats and I am pleased to report I am yet to observe any of the dropouts or buffering which were common on the previous setup.

Summary:

If you have a large house or apartment, and you’re having trouble with Wi-Fi consistency and dropouts, I would recommend the Google Wi-Fi solution for several reasons.

  • Price? $389 AUD or approx $287USD based on the Exchange rate at the time of writing.

Price wise, I think it’s pretty reasonable. If you consider it’s 3 Wi-Fi points, that’s $129 AUD each which isn’t “cheap” but it’s also not expensive. Comparable high end consumer grade Wireless routers are in the $200-300 range.

Leading onto my next point, Because the Google Wi-Fi is scalable, and therefore somewhat future proofed, I believe the price is justified.

  • It’s a scalable Wi-Fi solution 

You can start with one Wi-Fi point and scale out from there. This is important so you don’t need to buy 3 up front, just start with one (or two in a large house) and scale as required after performing Mesh and Device Wi-Fi testing to see how the Mesh is performing.

  • Setup is easy and the app helps you optimise the position of the Wi-Fi points

This is really cool, especially for non-technical people who may not understand how Wireless access points work and the best place to position them etc. It’s easy to run a few tests and re-position, re-test and in my case, get to a scenario where I have “Great” signal/connectivity between all 3 Wi-Fi points.

  • The Google Wi-Fi app has lots of useful features for everyday use

The ability to quickly troubleshoot if required using device, mesh and internet speed testing is great, again especially for non tech savvy folk.

  • Wi-Fi Range/Performance

The difference in Wi-Fi range and performance in my apartment is night and day compared to my previous setup even with the Wi-Fi Range Extender. Performance is now consistent despite the fact I am in a building with a lot of Wi-Fi access points within strong/medium range of the Google Wi-Fi mesh.

Rating:

As for a rating, I’m giving the Google Wi-Fi solution a 9 out of 10.

Nutanix Scalability – Part 5 – Scaling Storage Performance for Physical Machines

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Part 3 and Part 4 has taught us that Nutanix provides excellent scalability for Virtual Machines and provides ABS and Volume Group Load Balancer (VG LB) for niche workloads which may require more performance than a single node can provide.

Now that we’ve learned how to scale a Virtual machines performance, let’s see how the same rules apply to physical servers.

So you’ve got your physical server and a Nutanix cluster, now what?

As Part 3 and Part 4 explained, more virtual disks increase the storage performance for virtual machine, the same is true for physical servers using ABS.

Virtual disks will be presented to the physical server via iSCSI (ABS), for optimal performance you should have at least one virtual disk per node in your cluster. The reason for this is each vDisk is managed by a stargate (Nutanix IO engine) instance which runs in every Controller VM (CVM).

If you have a four node cluster, you need to use at least four virtual disks to utilise the four node cluster optimally. For an eight node cluster, eight or more virtual disks is required to ensure all CVMs (stargate instances) can actively provide a boost in performance.

The following tweet shows how the pathing increased from four on the four node cluster and when an additional fours node were added the pathing dynamically changed to use all eight nodes.

Therefore when using ABS for physical workloads, especially those high end database servers, I recommend using a minimum of 8 vDisks however if your cluster size is greater than 8, match the number of vDisks with the cluster size as your starting point.

If you have an 8 node cluster, you could for example use 32 vDisks and these will spread evenly across the nodes, resulting in four per stargate instance which is perfectly fine.

Using more vDisks than your current cluster size also means when additional nodes are added, ABS can dynamically load balance the vDisks across the new and existing nodes to automatically scale your performance.

Let’s cover the same MS Exchange and MS SQL examples covered for Virtual Machines in Parts 3 and 4 but now specifically for physical servers using ABS.

Let’s say we have an MS Exchange server with 20 databases, the performance requirements for each database is typically in the range of hundred of IOPS, in which case I would recommend one virtual disk (e.g.: VMDK) per database and another for the logs.

In the case of a large MS SQL server which may require tens or hundreds of thousands of IOPS to a single database, I recommend using multiple vDisks per database which involves Splitting SQL datafiles across multiple VMDKs to optimise VM physical server performance.

Sound familiar? The above two paragraphs are literally a copy/paste from Part 3 because the exact same rules apply to physical servers and virtual machines. Simple right!

Still need more performance?

Again, the exact same rules apply to physical servers with ABS as they do to virtual machines. In no particular order, as we’ve learned from Part 3 & 4:

  • Increase the vCPU of the Nutanix Controller VM (CVM)
  • Increase the vRAM of the Nutanix Controller VM (CVM)
  • Add storage only nodes

Can’t get much easier than that!

Summary:

From Parts 3, 4 and 5 we have learned that Nutanix provides the ability to scale the performance of individual servers, be it physical or virtual using the same simple strategies of adding virtual disks, storage only nodes or Controller VM (CVM) resources and how doing so increases performance to meet virtually (pun intended) any performance requirements.

Is there any reason you couldn’t confidently say Nutanix is doing 3 tier better than the SAN vendors? I’d love to hear if you have any corner cases.

Back to the Scalability, Resiliency and Performance Index.

Nutanix Scalability – Part 4 – Storage Performance for Monster VMs with AHV!

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In Part 3 we learned a number of ways to scale storage performance for a single VM including but not limited too:

  • Using multiple PVSCSI controllers
  • Using multiple virtual disks
  • Spreading large workloads (like databases) across multiple vDisks/Controllers
  • Increasing the CVMs vCPUs and/or vRAM
  • Adding storage only nodes
  • Using Acropolis Block Services (ABS)

Now here at Nutanix, especially in the Solutions/Performance engineering team we’re never satisfied and we’re always pushing for more efficiency which leads to greater performance.

A colleague of mine, Michael Webster (NPX#007 and VCDX#66) was a key part of the team who designed and developed what is now known as “Volume Group Load Balancer” or VG LB for short.

Volume Group Load Balancer is an Acropolis Hypervisor (AHV) only capability which combines the IO path efficiencies of AHV Turbo Mode with the benefits of the Acropolis Distributed Storage Fabric (ADSF) to create a more simplified and dynamic version of Acropolis Block Services (ABS).

One major advantage of VG LB over ABS is it’s simplicity.

There is no requirement for in-guest iSCSI which removes the potential for driver and configuration issues and VG LB is configured through PRISM UI by using the update VM option making it a breeze to setup.

UpdateVMwVG

The only complexity with VG LB currently is to enable the load balancing functionality, it needs to be applied at the Acropolis CLI (acli) using the following command:

acli vg.update Insert_vg_name_here load_balance_vm_attachments=true

In the event you do not wish all Controller VMs to provide IO for VG LB, one or more CVMs can be excluded from load balancing. However I recommend leaving the cluster to sort itself out as the Acropolis Dynamic Scheduler (ADS) will move virtual disk sessions if CVM contention is discovered.

iSCSI sessions are also dynamically balanced as workload on individual CVMs exceed 85% to ensure hot spots are quickly alleviated which is another reason why CVMs should not be excluded as you are likely constraining performance for the VG LB VM unnecessarily.

VG LB is how Nutanix has achieved >1 MILLION 8k random read IOPS at just 0.11ms latency from a single VM as shown below.

This was achieved using just a 10 node cluster, imagine what can be achieved when you scale out the cluster further.

A Frequently asked question relating to high performance VMs is what happens when you vMotion?

The link above shows this in detail including a YouTube demonstration, but in short the IO dropped below 1 million IOPS for approx 3 seconds during the vMotion with the lowest value recorded at 956k IOPS. I’d say an approx 10% drop for 3 seconds is pretty reasonable as the performance drop is caused by the migration stunning the VM and not by the underlying storage.

The next question is “What about mixed read/write workloads?”

Again the link above shows this in detail including a YouTube demonstration, but at this stage you’re probably not surprised that this result shows a maximum starting baseline of 436K random read and 187k random write IOPS and immediately following the migration performance reduced to 359k read and 164k write IOPS before achieving greater performance than the original baseline @ 446k read and 192k IOPS within a few seconds.

So not only can Nutanix VG LB achieve fantastic performance, it can do so during normal day to day operations such as VM live migrations.

The VG LB capability is unique to Nutanix and is only achievable thanks to the true Distributed Storage Fabric.

With Nutanix highly scalable software defined storage and the unique capabilities like storage only nodes, AHV Turbo and VG LB, the question “Why?” seriously needs to be asked of anyone recommending a SAN.

I’d appreciate any constructive questions/comments on use cases which you believe Nutanix cannot handle and I’ll follow up with a blog post explaining how it can be done, or I’ll confirm if it’s not currently supported/recommended.

Summary:

Part 3 has taught us that Nutanix provides excellent scalability for Virtual Machines and provides ABS for niche workloads which may require more performance than a single node can offer while Part 4 explains how Nutanix’ next generation hypervisor (AHV) provides further enhanced and simplified performance for monster VMs with Volume Group Load Balancing leveraging Turbo Mode.

Back to the Scalability, Resiliency and Performance Index.