The Hyperconverged Homelab—Hardware Accelerated Video Transcoding

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There are many uses for a dedicated GPU besides gaming. Potentially the most appealing, for homelab enthusiasts, is providing hardware accelerated transcoding of video files. Recent consumer graphics cards include dedicated hardware modules for both decoding and encoding the most common video compression formats. This allows the heavy lifting of video transcoding to be offloaded from the CPU, freeing up compute resources for other tasks and enabling faster, more efficient video encoding and decoding.

This is essentially the same functionality as Intel’s QuickSync hardware-accelerated video transcoding, but available on consumer and enterprise graphics cards as opposed to being part of the on-chip graphics package. Although I initially hoped to leverage QuickSync on my current processor, I have not been able to get pass-through of the integrated graphics working properly. I’ll cover those issues in another entry, but for now I want to talk about leveraging the dedicated video transcoding functionality of consumer-grade NVIDIA graphics cards.

NVENC On the Cheap

Today, the absolute best bang for your buck in the GPU market is a secondhand GTX 1060. With a Passmark score right around 9000, depending on the variant (8971 for the 3GB, 9095 for the 6GB), these GPUs have adequate performance for last-gen gaming at 1080p. And at a consistent local Craigslist price of around $140 USD, they represent an absolutely phenomenal 64–65 Passmarks per dollar. To illustrate my point, here’s the total Passmark score and Passmark/$ for typical local Craigslist prices for all 10-series GPUs:

As you can see, the 1060 variants lead the pack in terms of value, at more than double the performance per dollar of a 1080 TI. And while they may no longer be suitable for demanding gaming and rendering tasks due to their relatively low total performance, they’re still quite capable. Side note: while the GTX 1050 theoretically includes the hardware we care about, the overall graphics performance is much lower and the RAM is significantly less; the used market prices are not cheap enough to justify these shortcomings.

The GP106 chipset that first-gen GTX 1060 cards are based on is the same as in the Quadro P2000. Like almost all Pascal architecture GPUs, it features dedicated hardware for video encoding and decoding. Looking at the official video codec support matrix, we can also see that this chipset is recent enough to support modern codecs, including up to H.265 4K YUV 4:4:4 encoding and up to H.265 12-bit YUV 4:2:0 decoding. H.265 4:4:4 is still relatively uncommon, and hardware decoding for it isn’t supported on any Pascal architecture card, so we’ll accept that as a reasonable upper limit.

Quadro vs. GTX

So what’s the difference between a Quadro P2000 and a GTX 1060 when it comes to video transcoding? On the hardware side, not much that I’ve been able to find. The memory size and bus width differ, but the decode/encode module appears to be the same. Of course, NVIDIA imposes a number of artificial software-based limitations on their GTX cards designed to force enterprise customers to pay the premium for the Quadro brand. For video encoding, this manifests as an artificial restriction on the number of simultaneous transcoding sessions supported by the driver. Where Quadro cards (from the P2000 and up) are “Unrestricted”, all GTX cards are capped at 2 sessions. What does the potential performance look like if we ignore this restriction?

According to the latest revision of NVIDIA’s technical notes for NVENC and NVDEC, the maximum performance for encoding H.264 is 648 FPS, while decoding is 658 FPS. This is a per-module performance figure, so it doesn’t depend on the GPU chipset generally but only on the number of modules included in a specific GPU’s configuration. The P2000 and GTX 1060 both feature single NVENC and NVDEC modules. Here’s a look at Pascal video coding performance by quality option:

Frames Per Second, single source, higher is better, maximum values in red. Data pulled from NVENC/NVDEC Application Notes as of 2 April 2018.

Doing a little simple math (Encoding FPS ÷ 30 FPS per stream) we can estimate the performance for multiple stream encoding. A single NVENC module should be capable of encoding 21 simultaneous streams in High Performance mode and 11 in High Quality Two-Pass mode. NVDEC performance is similar, at 21 streams regardless of their encoding level. A P2000 or GTX 1060 should be able to perform the theoretical maximum 21 simultaneous 1080p30 transcodes on its single NVENC module, minus some potential overhead for task switching and performance degradation from memory restriction. Of course, this is all hypothetical, but YouTuber Alex over at Sloth Tech TV reports that he was able to achieve 18 streams on a P2000. That seems fairly realistic, and is in the ballpark for previously-published numbers for Pascal NVENC modules (575 FPS, according to a previous version of the technical notes), as well as being approximately what would be expected when accounting for the lower boost clock speed of the P2000 compared to the GTX 1060.

Hardware Transcoding, Un-Capped

What does this look like in the real world? Thanks to some clever reverse-engineering of the NVIDIA drivers, it is now possible to un-cap consumer GTX cards for unrestricted video transcoding. This is the same basic technique used by DifferentSLIAuto, the utility for enabling SLI on unsupported hardware configurations: we simply patch some new data into the right memory address in the driver blob. Fortunately, others have already gone to the trouble of verifying this modification and have even put together a simple shell script that will safely do it for you.

Unfortunately, this driver requirement means that it’s not possible to do this under FreeNAS, so I’ve spooled up a fresh Ubuntu Server 18.04 VM for testing purposes. First things first, we’ve enabled PCI Passthrough on the GPU in the Host’s hardware configuration. Then we simply add the card (and its associated audio device, even though we won’t be using it) to the VM as a regular PCI device, making sure to reserve all guest memory:

Then we hide the hypervisor from the guest OS so the NVIDIA drivers will load and not simply refuse to work. Add hypervisor.cpuid.v0 = FALSE under the guest’s Advanced Configuration:

Finally, although it was necessary for functionality under Windows 10, I have not found any trouble with Message Signaled Interrupts under Ubuntu 18.04. If the GPU is flaky, it may be necessary to configure pciPassthroughX.MSIEnabled = FALSE for each device; I’ll update this entry if extended testing surfaces any stability problems.

Now we can boot up the VM and install the NVIDIA drivers. The caveat here is that you need a version of the driver which is supported by the patcher. A list is maintained in the README; for Ubuntu 18.04 I was able to use the community PPA to install a version 415 driver, which was using a compatible point release supported by the patcher:

Once this is done, reboot the system and check functionality with nvidia-smi. You should see your card(s) listed alongside some performance stats:

From here, instructions proceed as documented in the patch README. Clone the repo then simply run bash ./ The script will automatically validate the driver version, locate the blob, and apply the patch bytes. Reboot to complete the process.

Full Transcoding With Plex

In order to test out our new hardware configuration in a common real-world scenario, I’ve selected the personal cloud streaming software Plex. It runs on pretty much anything, and will allow us to verify both encoding and decoding functionality without having to manually invoke something like ffmpeg a bunch of times and then do napkin math to figure out what that means for other workloads. This will allow me to test multiple simultaneous real-time transcoding sessions in a realistic environment.

There are a couple caveats to hardware transcoding with Plex. The first, of course, is that you need a Plex Pass subscription to enable the currently-beta Hardware Accelerated Transcoding feature. The second is that you actually need to be running one of the latest beta releases if you want to enable hardware accelerated decoding on NVIDIA chips. Although QuickSync/VAAPI hardware decoding has been available for a while, NVENC support too, NVDEC support is a recent addition.

According to Plex Forums user AnonymousRetard, the main change in Plex appears to have been updating the version of ffmpeg they use for transcoding to a more current release that supports NVDEC. However, this change is so recent that it’s not even unofficially supported in the beta releases, so some slight modification is needed to inject a flag to the encoder to enable support.

User revr3nd on the Plex Forums has put together a handy guide and convenient little script for this part of the process. Essentially all you need to do is get a copy of Plex version or later and then patch the Plex Transcoder with a shim to conditionally inject the NVDEC argument when invoked on supported codecs. Find it on GitHub; I did have to submit a PR with a couple fixes, though, so your mileage may vary. Since NVDEC only works on certain codecs, it’s important that Plex doesn’t attempt to use it when it won’t work. The codecs supported vary by architecture, chipset, and specific card version. Although Pascal generally supports MPEG-1, MPEG-2, VC-1, H.264, and H.265 4:2:0, support for VP8 and VP9 10- and 12-bit is only found on some cards.

Confirm which codecs are supported by your GPU (check the NVDEC support matrix) and invoke the script accordingly. In our case, running a GTX 1060 (GP106) under Ubuntu, the command is:

Note: you will need to re-run this command every time Plex is updated, until NVDEC is officially supported, since upgrades will overwrite the shim.


Finally, the moment of truth, that magic “(hw)” on the Dashboard:

To confirm that hardware accelerated decoding is active, check the “dec” output column of nvidia-smi dmon -s u:

Utilization will spike when you initiate a new stream, as Plex transcodes a buffer, and will then settle out to low values as the transcode keeps up with playback.

Over a short four minute test interval running six transcodes from H.264 1080p ~17Mbps to the 10Mbps preset, I measured 24.5% encode utilization and 21.1% decode utilization average from nvidia-smi. CPU utilization, only for container and audio codec transcoding, was minimal, at a little under 10% per stream (note that percentage is per-core: total system capacity is 400% utilization). However, reported CPU usage from the ESXi host was rather high, with total combined CPU use from the FreeNAS and Plex VMs averaging around 85% of total capacity during this stress test. This was largely due to file transfer overhead of SMB, which should be improved by switching to NFS or iSCSI.

As much as I would like to further test this setup, I don’t have enough playback devices at hand. I intend a more thorough performance report when I am able to borrow additional playback devices.

That’s it for now.

The Hyperconverged Homelab—Windows VM Gaming

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Shows "NVIDIA GeForce GTX 1060" in the Windows 10 Device Manager alongside the "VMWare SVGA 3D" display device.
An NVIDIA GeForce GTX 1060 3GB successfully passed through to a Windows 10 VM under ESXi 6.7.

With the last couple of major revision to VMWare’s enterprise virtualization platform, ESXi, it has become relatively easy to robustly pass through consumer NVIDIA GPUs for use in virtualized gaming and other consumer/enthusiast configurations. However, although information on how to correctly configure this may be widely available, it’s usually poorly explained. After successfully configuring NVIDIA passthrough and driver support on a Windows 10 client, here’s my rundown of the requirements, process, and potential issues.

What You Need

  • A virtualization server with an available PCIe slot.
  • An NVIDIA consumer graphics card. This should work on any card that’s still supported by NVIDIA’s drivers, without requiring the use of a specific driver version or unsigned driver hack.
  • ESXi 6.5 or later.
  • A Windows 10 VM. If you don’t have one, I’ll be covering cheap legitimate Windows 10 licenses in a future post.

How to Proceed

Put the ESXi host into Maintenance Mode, shut down the server, and install the GPU. We’ll have to reboot the ESXi host fully at least once, so set Maintenance Mode to prevent any VMs from automatically booting.

Reboot the server and configure the BIOS. If you’re using a server motherboard, you probably don’t have to change anything. However, those using a consumer desktop motherboard may need to change their default display device settings if they’ve been using the onboard graphics of their CPU. Note the following requirements: Intel Virtualization Technology (vT-d) must be enabled to support PCIe device passthrough, the GPU must be enabled as the primary display device, and a display may need to be connected during boot. If you’ve made any changes, save and reboot the server. Caveats: I have not yet been successful at simultaneously passing through both the integrated graphics on my Intel CPU and any external GPU. I believe that this is an issue with my consumer motherboard BIOS only allowing one of the graphics devices to be enabled at a time, but I don’t have a second PCIe GPU to test this theory with. I have confirmed passthrough of the Skylake onboard Intel HD Graphics 530.

Configure PCIe device passthrough. Now that you’ve fully booted the server, open up the ESXi web client. Select Host→Manage→Hardware→PCI Devices. You should see your GPU in the device list. As such:

If not, shut down the server and ensure that the card is fully seated and that you’ve correctly configured your BIOS. Select the GPU in the Devices list and click “Toggle Passthrough”. For NVIDIA GTX series with HDMI audio, you’ll also see an onboard high definition audio device, which may also be selected when you select the GPU itself. These should be treated as a single device, always passed through to the same VM. Ensure that you enable passthrough on both.

Reboot the server. A reboot is necessary to enable PCI Device passthrough. If you’re using a consumer motherboard with the GPU selected as your primary display, you will lose access to the hardware console part-way through the boot process. This will occur at “dma_mapper_iommu loaded successfully”, which is the PCI device passthrough module. When ESXi loads this module with a configuration for passthrough, it will take control of the passed-through device so that it can be made available to VMs. If the passed-through device is set as your primary display when ESXi boots, it will simply cut out the display at this point. However, ESXi will still boot (barring any other errors) and run just fine.

You can now remove the ESXi host from Maintenance Mode.

Configure PCI Device Passthrough. While it is powered down, open up the settings for your Windows 10 VM. First, add the GPU and its associated audio device under Virtual Hardware→Add other device→PCI Device. You must also allocate all of your VM’s RAM at this point, since PCIe RAM mapping doesn’t work properly with dynamically allocated RAM. Ensure that you have reserved all guest memory. It shouldn’t be necessary to lock it, but it won’t hurt. Save and close the settings window to repopulate the Advanced Settings dialog.

Configure Advanced Settings. Reopen the “Edit settings” dialog and select VM Options→Advanced→Edit Configuration. There are a couple flags which need to be added. For the GPU and its audio device, we have to disable Message Signaled Interrupts by setting pciPassthruX.MSIEnabled=FALSE for both devices, where X is the device passthrough number (zero-indexed, usually 0 and 1 if they’re the only PCI devices passed through to the VM). This requirement and procedure is documented by NVIDIA, who claim that it only applies to ESXi 5.0 and 5.5. However, in my configuration, this issue persists in 6.5 and 6.7, and results in lost interrupts which cause the display driver to flicker and crash, and the audio driver to stutter. This may be affected by BIOS PCI device setting for firmware mode being set to legacy (instead of EFI), but I don’t know.

Next, configure hypervisor masking by setting hypervisor.cpuid.v0=FALSE. This prevents the VM OS from detecting that it’s being run under virtualization, which is necessary to prevent the NVIDIA driver from crashing.

Now you should be able to boot the Windows VM and simply install the official NVIDIA drivers as normal! Behold your success, the NVIDIA driver running under ESXi!

An NVIDIA GeForce GTX 1060 3GB successfully passed through to a Windows 10 VM under ESXi 6.7.

In my configuration, I am able to run 3DMark Time Spy surprisingly well, with a tolerable 25–30fps throughout most of both graphics tests at 1080p, giving a graphics score of 4021. However, performance is dramatically CPU-constrained due to the i5-6500T’s low base clock and failure to properly Turbo in my current configuration. The physics test reflects this with an appalling framerate of around 4 fps and a CPU score of 1220. Overall, leading to a score of 2990.

An overall mediocre result due to appalling CPU performance.

We’ll see if some improvement cannot be achieved with a little judicious CPU overclocking.

Update: 26 Feb 2019

After extensive fiddling with various BIOS settings, I am still unable to get ESXi to play nice with passing through both the NVIDIA GPU and the onboard Intel GPU. I have, however, recovered the ESXi boot/console display, which no longer hangs at mmio initialization after I set the PCIe device option rom execution to EFI. This behavior reverts if I set “Video option ROM” to any value other than “Legacy”. Anyways, back to GPU troubles:

I have confirmed that this is not a hardware compatibility/functionality or configuration issue by booting Ubuntu 18.10 on the server, which is able to utilize both display devices by default. There is no problem whatsoever in having both GPUs enabled in the BIOS, which are then utilized by Ubuntu. It also respects the BIOS setting for Primary Display, and which is set as primary in BIOS has no impact on functionality.

I therefore conclude that there’s an issue with ESXi when using multiple GPUs. Although both GPUs show up correctly in the hardware listing, and can be set to passthrough, the NVIDIA GPU cannot be used if the Intel GPU is enabled in the BIOS (for example, if Intel GPU is also enabled in BIOS then I get Error 43 under Windows VM with NVIDIA GPU passed-through; disable Intel GPU and the NVIDIA card works fine). I have other PCIe devices connected and passed through which don’t seem to make any difference.

However, I note something strange: when I change the Intel GPU enabled/disabled in the BIOS, one of my other PCIe cards (LSI2008 SAS HBA) gets “lost”. It still shows up in the Host hardware list, but the VM hardware configuration line is blank. I believe it may be changing PCIe address, which is odd.

Can anyone report success getting two GPUs of any variety to pass through in ESXi? Leave a comment on the Reddit thread. I don’t have another PCIe graphics card on hand to test with, and due to hardware constraints would have to run one card at only x4, which is not ideal. And people want way too much money for their crummy old graphics cards.

Next test, which I will do some other day because I am tired of power cycling my system, will be switching which PCIe slot the GPU is in, on the off chance that having it in a non-primary slot will make some difference in the PCIe device tree that makes things behave better for ESXi.

The Hyperconverged Homelab—Upgrades

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Thanks to the magic of Craigslist and eBay.

After two years of trouble-free service running FreeNAS and Ubiquiti’s UniFi Controller under an Ubuntu Server 18.04 VM, it was finally time for some upgrades. Although I was able to expand my storage capacity by growing a vDev of old, small drives, I wanted to take this opportunity to future-proof and expand my capabilities.


  • GPU for passthrough. My system has more capacity than needed for its primary tasks, so I want to try out VM gaming.
  • Better network monitoring and control.
  • Full-size motherboard. Although this project originally started life as a mini-ITX build, my needs have changed, and I am no longer size-constrained on my case.
  • More SATA devices. Using slow consumer spinning platters means I can put a large number of drives on a single HBA before exceeding the available bandwidth and creating a performance bottleneck.

As pictured, clockwise from upper left:

  • EVGA Superclocked GeForce GTX 1060 3GB—Craigslist, $140 with 2 years mfr. warranty, used/like-new
  • Ubiquiti UniFi 8-port Gigabit Managed Switch with 4 PoE (US-8-60W)—eBay, $109 shipped, new/open box
  • SuperMicro C7Z170-OCE-O LGA 1151 ATX Intel Motherboard—eBay, $165 shipped, new/old stock
  • Intel RES2CV240 24-Ports SAS / SATA 6.0Gbps RAID Expander Card—eBay, $149 shipped, new/open box

The GPU was selected by crawling Craigslist for every local listing with “GeForce”. I then constructed a spreadsheet of the listings and calculated the PassMark/$ score to find the best value. At 64 PassMarks per dollar, this EVGA card was one of the best value outside of 1080 and 1080TI models previously used for cryptocurrency mining. Crypto mining is to GPUs like drifting is to cars: you can do it safely, if you’re careful, but when buying second-hand the deals aren’t worth the potential headache of having a unit that’s been thrashed. The 1060 class cards also have enough performance to run current-gen games at decent settings.

I purchased the Unifi Switch because I was experiencing some bizarre network performance issues with the server. After way too much mucking around on the software side of things I discovered that it was just the consumer-grade Intel NIC on the server motherboard dying (as they are known to). I switched to the other, unused NIC (an Atheros unit) and my connectivity problems went away. It was too late to cancel the order, and I figured that it would be nice to have a managed switch with PoE anyways. Unfortunately it turns out my UAP-AC-LR was cheap for a reason: despite packaging to the contrary, it predates that model’s support for 802.3af standard PoE and requires 24V passive… Oh well, no real loss.

The motherboard was chosen by chance, as I was browsing Newegg for compatible models and was surprised to see that SuperMicro made a desktop gaming-oriented motherboard. A quick trip to eBay surprised me even more with this inexpensive new old stock unit, which I quickly purchased. Single 1GbE, no WiFi/BT, but with Thunderbolt-capable USB 3.1 module. Interesting possibilities abound.

The Intel SAS Expander I found by recommendation on one of the forums, either FreeNAS, ZFS, or ServeTheHome (I can’t recall). This model is particularly desirable not only for its performance to cost ratio, but because it supports dual uplink configuration. Two SAS ports can be used for transparent uplink to the HBA, doubling the throughput available compared to using a single channel uplink. Since 6Gb SAS transfers approximately 600MB/s, splitting it in half leaves 300MB/s available for each hard drive channel in simultaneous utilization. That’s enough to saturate my slow 5k drives. Without this dual uplink, I would be limited to 120MB/s per drive at full utilization, which would be a performance bottleneck.

Aside from the SAS expander, cable management went quite well.

Breaking down the server, I took the opportunity to perform some much-needed cleaning. Although not particularly old, this device has had to live in some fairly awful conditions, including the dustiest room in the dustiest house in the dustiest neighborhood I have ever lived in. Unfortunately, I don’t have filters for the HDD bays, which serve as the primary system intake (the top radiator is the primary exhaust). They also live at ground level. Addressing this enclosure shortcoming is on my to-do list.

As you can see the case fits my components fairly well, and I’ve used the back panel cable management for everything except the SAS cables. This includes fans, pump, front IO, boot disk, and even the motherboard and CPU power supply cables. It’s quite tidy without the SAS cables. Unfortunately there is no way to terminate the SAS cables myself to custom length (the plug ends are actually PCBs), and the cables really don’t like to be bent and do not hold their shape at all, so they get to be spaghetti.

The SAS expander is simply suspended by its Molex 4-pin power connector (don’t crucify me: it doesn’t weigh a lot, this system doesn’t move, and it’s only temporary while I sort out a new case) and held in position by the fairly stiff SAS cables.

The GPU has been mounted in the primary PCIe slot. The IBM M1015, mounted below it, is half-height and so does not obscure the GPU intake fan too badly.

The case will soon be replaced with a Rosewill RSV-R4000 or RSV-L4500, to be rack mounted. This unit is plug and play with my existing hot swap cages, provides plenty of room for my GPU and water cooling loop, is extremely cheap, and even has a front panel intake filter.

Next time, the trials of GPU passthrough.

The Hyperconverged Homelab—Growing RAIDZ vDevs

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Quickly approaching 85% utilization of my pool, I found myself in need of more storage capacity. Since the first revision of this project’s hardware was scrounged together on a small budget and utilized some already-owned drives, one of my vDevs ended up being a RAIDZ1 vDev of only 3x2TB. Adding more vDevs to my pool would require either an additional HBA (not possible with my now-undersized motherboard’s single PCIe slot) or a SAS expander. In either case, I would need the drives themselves. I figured that this was a good opportunity to experience growing a ZFS pool by increasing the size of a vDev’s disks.

ZFS does not support “growing” of arrays by adding disks (yet!), unlike some other RAID and RAID-like products. The only way to increase the size of a pool (think of it as pooling the capacity of a bunch of individual RAID arrays) is to add vDevs (the individual RAID arrays in this example), or to replace every single disk in a vDev with a larger capacity. vDevs can be constructed out of mixed-size disks, but are limited to the maximum capacity of the smallest disk. For example, a ZFS vDev containing 2x 2TB and 1x 1TB disks has the same usable capacity as one containing 3x 1TB disks: the “extra” is ignored and unused. Replace the lone undersized disk, however, and ZFS can grow the vDev to the full available size.

Expanding vDevs is a replace-in-place strategy that essentially works the same as rebuilding (“resilvering”) after a disk failure. Recent versions of ZFS support manually replacing a disk without first failing it out of the vDev, which means that on single-parity (RAIDZ1) vDevs this process can be accomplished safely, without losing fault-tolerance. The FreeNAS documentation provides more information and instructions.

Growing by “too much” is not recommended and will result in poor performance, as some metadata will be an non-optimal size for the new disk size. As far as I have read (unfortunately I can’t find a link for this), it’s definitely “too much” around an order of magnitude, although aiming for no more than a factor of five is probably wise. For my case, as an example, we’re growing from 2TB disks to 6TB disks, which is only a factor of 3. This should be perfectly fine.

Speaking of 6TB drives… Hard drives may be cheap in historical terms, but there’s still value in being thrifty. For my use-case, which currently includes read-oriented archival storage, grown mostly write-only and used for backups and media storage, accessed by 1Gb network links, the performance requirements are rather low. The data is (mostly) replaceable, so single redundancy is adequate. This means that I can safely use the cheapest hard drives possible, which are currently found in Seagate Backup Plus Hub 8TB carried by Costco for only $129. (At the time I purchased, the last of their stock of the 6TB variant was being cleared for even cheaper.)

These drives are Seagate Baracuda ST8000DM005, which are an SMR drive. This technology, which has been used to great effect to increase the size of cheap consumer drives, essentially by overlapping the data on the platters, is only really suitable for write-once use and is known to be rather failure-prone. However, these have plenty of cache and perform just fine for reading, and adequately for writing, so are perfectly acceptable for my use-case.

Growing the target vDev was fairly straightforward. I had extra drive bays unused so simply shucked the drives from their plastic enclosures and proceeded one at a time. After formatting each disk for FreeNAS, I initiated the resilvering process. This took somewhere between 36–48 hours to resilver 1.7TB of data per drive. I found this performance rather poor, but was not able to locate an obvious bottleneck at the time. In hindsight, inadequate RAM was likely the cause. After resilvering I removed the old drive to make room for the next replacement. Although my drive bays are hot-swap (and this is supported by both my HBA and FreeNAS), I didn’t label the drive bays when I installed them initially and had some difficulty identifying the unused drives. The best solution I found was to leverage the per-disk activity light of the Rosewill hotswap cages.

A lovely sight.

With capacity to spare, I can finally test out some new backup strategies to support, such as Time Machine over SMB.

The Hyperocnverged Homelab—Configuration c.2018

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Although my original use-case included virtualizing a router/firewall, it was only beneficial for a couple months while I was still living in accommodation with a shared network. I ran OpenWRT for simplicity of configuration and had two separate vSwitches configured in ESXi, one for each NIC. This allowed me to connect to the shared network while retaining control over my own subnet and not leaking device access or mDNS. I had hoped to pass through the motherboard’s 802.11ac WiFi NIC (which worked fine), but was stymied by OpenWRT’s glacial upgrade cycle. They were running an absolutely ancient version of the Linux kernel which predated support for my WiFi chipset. I considered working around this by creating a virtual Access Point using a VM of Ubuntu Server or other lightweight Linux which would support the WiFi chipset, but it just wasn’t worth the trouble.

After spending a couple months abroad with the server powered down I returned home and found a new apartment. I was able to get CenturyLink’s symmetric Gigabit offering installed, and running their provided router eliminated the need for a virtual router appliance. The OpenWRT VM was quickly mothballed and replaced with an Ubuntu Server 18.04 VM to run Ubiquiti’s UniFi Controller.

The current (Dec. 2018) software configuration is fairly simple:

  • ESXi Server 6.5
    • FreeNAS 9.10
      • 12GB RAM, 4vCPU, 8GB boot disk
      • IBM M1015 IT Mode via PCIe passthrough
      • 2x RAIDZ1 vDevs of 3 disks (consumer 2 and 5TB drives)
      • Jails for utilities benefiting from direct pool access
    • Ubuntu Server 18.04
      • 2GB RAM, 2vCPU, 8GB boot disk
      • Ubiquiti UniFi Controller
      • DIY Linode dynamic dns

The Hyperconverged HomeLab—Introduction

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Now in its second relatively trouble-free year, it’s finally time to get some upgrades on my hyperconverged homelab. First, however, a long-overdue introduction!

The current case configuration: a modified Cooler Master Centurion 590 mid-tower case.

This project started out as a compact, low-power, ultra-quiet NAS build. However, I quickly decided that I wanted to virtualize and give myself more power and flexibility. At the very least, being able to run pfSense or another router/firewall appliance on the same device represented a significant benefit in terms of portability: the ability to plug into basically any network without making the NAS available on it was a huge potential benefit.

I decided to use a 35W Intel desktop processor and consumer motherboard. They’re economical and readily available, with plenty of products available for performance and cooling enhancement. At the time, Skylake (6th Gen.) was mature and Kaby Lake didn’t have an official release date, so I chose the i5-6500T. The $100 premium on MSRP and near total lack of single unit availability prevented me from choosing an i7-6700T.

For motherboard I chose Gigabyte’s GA-H87N-WIFI (rev. 2.0), a mini-ITX motherboard from their well-regarded UltraDurable line. The primary driver of this decision was the onboard dual 1GBase-T and M.2 802.11a/b/g/n plus Bluetooth 4.0 via M.2 card. Dual LAN was critical for the device’s potential use as a router, as virtualizing my NAS would require utilizing the single available PCIe slot for an HBA or RAID card.

RAM was sourced as 2x16GB G.Skill Aegis modules (still the cheapest DDR4-2133 2x16GB kit on the market), providing a solid starting point while leaving two DIMMs free for later expansion to the motherboard and processor’s max supported 64GB. I sourced a Seasonic SS460FL2 a 460W fanless modular PSU, a cheap SanDisk 240GB SSD for a boot drive, and Corsair’s H115i all-in-one liquid cooling loop.

At this time I was still case-less, and waffling on the purchase of a U-NAS NSC-800 hot-swap enclosure, when I discovered Rosewill’s 4-in-3 hot swap cages. I quickly located the Cooler Master Centurion 590 on local Craigslist, which represented a decent compromise on size and offered 9 5.25″ drive bays.

The final piece of the puzzle was the HBA, an IBM M1015 RAID card which I cross-flashed to LSI generic IT Mode firmware. See this other post for details. With that, the build was hardware-complete and went together (fairly) smoothly. Only minor case modification was required to fit the ridiculously over-sized water cooling radiator, which had to be mounted on the top of the case with the fans inside, since the case was not designed for water cooling and here was inadequate clearance above the motherboard.

I installed ESXi on the boot disk and then installed FreeNAS into a VM. (Yes, I should have drive redundancy for my VM datastore.) After flashing the M1015 everything was relatively plug-and-play, set-and-forget, with the only notable downside being that the motherboard refused to POST without detecting an attached display. That issue was solved when I discovered that an HDMI VGA adapter I purchased acted as a display simulator. This system served me well for the last couple years, but recently I’ve wanted to expand my capabilities. Having a single PCIe slot is somewhat limiting, especially since I didn’t end up buying a mini-ITX sized case…

Smart TVs Enable Creepy Ads That Follow You

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Since as early as 2013, the misleadingly-named, San Francisco-based Free Stream Media Corp. has touted smart TV software capable of detecting what you’re watching. Initially marketed as a social tool to drive viewer engagement, the software has morphed into an Orwellian advertising spy machine. Called “Samba TV” since its debut at CES in 2013, the software comes pre-installed on select Smart TV sets from a dozen manufacturers, including Sharp, Toshiba, Sony, and Philips. Claiming to provide consumers who opt in with “recommendations based on the content you love”, the software in fact monitors everything displayed on the TV to identify not only broadcast advertisements but also streaming services and even video games and internet videos.

This data is then distributed to advertisers in real time. The result: creepy targeted ads that know what you’re watching.

Christine DiLandro, a marketing director at Citi, joined Mr. Navin at an industry event at the end of 2015. In a video of the event, Ms. DiLandro described the ability to target people with digital ads after the company’s TV commercials aired as “a little magical.”

This accomplishment is a result of Samba’s “device map”, which appears to utilize a combination of local network exploration and mobile device fingerprinting to identify smartphones, tables, and other computers in the same household as an enabled Smart TV. This allows the company to target ads to other devices based on what’s on TV.

Presumably they’re also building a profile of your viewing habits to sell to advertisers as well. Yikes.

US Cell Phone Carriers Sell Your Location, Without Permission

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In May, the New York Times reported on a private company that purchased bulk user location data from US cellular carriers and then re-sold individual location data to law enforcement in a blatant violation of customer privacy and legal due process:

The service can find the whereabouts of almost any cellphone in the country within seconds. It does this by going through a system typically used by marketers and other companies to get location data from major cellphone carriers, including AT&T, Sprint, T-Mobile and Verizon, documents show.

US Sen. Ron Wyden (D-Ore.) took action the next day, calling on carriers to discontinue selling subscriber data to so-called “location aggregators”. So far AT&T, Verizon, Sprint, and T-Mobile have responded, issuing statements of intent to cut ties with location middlemen. Whether they will continue to share subscriber location data without explicit and affirmative consent remains to be seen. Congressional Republicans show no interest in preventing them:

“Chairman Pai’s total abandonment of his responsibility to protect Americans’ security shows that he can’t be trusted to oversee an investigation into the shady companies that he used to represent,” Wyden said. “If your location information falls into the wrong hands, you—or your children—can be vulnerable to predators, thieves, and a whole host of people who would use that knowledge to malicious ends.”

FCC Chairman Ajit Pai represented Securus in 2012. More information from ArsTechnica, who report that Obama-era regulations were blocked by Congress that would have prevented this kind of behavior.

Tapplock Is Basically Worthless

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Recently-kickstarted Tapplock touts a Bluetooth-enabled smart lock that uses a fingerprint sensor. The company came under fire from tech-savvy commentators when popular YouTuber JerryRigEverything completely disassembled and defeated in a matter of minutes using a screwdriver and adhesive pad. This attack appears to be related to a quality control problem with the specific unit he used; a spring-loaded shear pin is supposed to prevent the back from rotating. It’s unclear whether that pin can be easily snapped or retracted, for example with a string magnet, but it turns out that doesn’t matter. UK-based security researchers PenTestPartners:

The only thing we need to unlock the lock is to know the BLE MAC address. The BLE MAC address that is broadcast by the lock.

The security credentials used to control the lock are derived from the device’s publicly broadcast identifier. This means that every single lock is vulnerable to an attack that can be carried out with a smartphone app:

I scripted the attack up to scan for Tapplocks and unlock them. You can just walk up to any Tapplock and unlock it in under 2s. It requires no skill or knowledge to do this.

Can it get worse? Yes, it can. Responding to the researcher’s security disclosure, Tapplock reportedly said:

“Thanks for your note. We are well aware of these notes.”

Be wary of Internet of Things (IoT) “smart” security devices. The are neither smart nor secure.

Enable Searching of SMB Shares on Freenas under macOS

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One frustrating shortcoming of accessing SMB shares from macOS is the default failure of directory indexing for file searching. You simply can’t use the normal Finder “Search” field to do anything. This makes it particularly tedious to interact with large SMB shares when you don’t know exactly where the files you want are located.

The solution at the link is simple, if obscure: select the fruit object from the available VFS Objects under the Advanced configuration of the share in question. Thanks to Spiceworks user David_CSG for dropping a hint about vfs_fruit that led me to this solution.

Edit: turns out that this doesn’t actually work. The current state of enabling SMB server-side indexing under FreeBSD appears to involve running Gnome Tracker. These instructions apparently work under FreeBSD Jail with the addition of devel/dconf dependency. iXsystems development stance is currently “Nope”. I might take a look at this and see whether the installation can be pared down; with any luck it should be possible to exclude metadata indexing components with the largest dependency footprint.

The Blog of Philip and Dakota Schneider