Using Apple File System (APFS) with your virtualized Mac

Apple has just released the macOS High Sierra with new features, one of them is the brand-new Apple File System (APFS) that is optimized for flash storage which newer Macs enjoy. If you happen to be using macOS in a virtualized way, e.g. with VMware, you may have trouble getting the new OS to work as the upgrade forces conversion of the boot partition to APFS which the VMware UEFI does not support.

To solve the problem, we need to let the VMware UEFI know APFS and luckily the APFS driver can be extracted from the High Sierra installer as a UEFI driver executable. We can then slip the driver to the UEFI BIOS that bundles with VMware Player itself and everything should work.

Getting Started

We’ll need 3 things before modifying the VMware UEFI BIOS. They are listed below:

To simplify things, you can download my modified UEFI BIOS (tested on VMware Workstation Pro 14, may work for other versions too). If that ROM doesn’t work for you, go after these steps to get a modified BIOS with APFS support.

Use UEFITool to open EFI64.rom located at [VMware Installation Folder]/x64/, select File > Search and choose GUID tab. Type in 961578FE-B6B7-44C3-AF35-6BC705CD2B1F and double click the result inside Message section. Leave this screen for now.

Extract the FFS tool to the same directory as the APFS driver file. Open your command prompt, change directory to that place and run this command:  GenMod apfs.efi .

 

Go back to UEFITool, right-click the selected item and choose Insert After, then select apfs.ffs from the FFS directory. The screen should look like this.

 

Save the modified ROM with the name efi64_apfs.rom to your VM directory.

Applying the new UEFI BIOS

To get the modified UEFI BIOS to work, use a text editor to open the VMX file. Ensure the file contains the following lines.

Save the VMX file and start your VM, your macOS High Sierra will now boot as expected with an APFS volume. Voila!

Installing NVENC SDK and CUDA SDK on Ubuntu 14.04

After I set up my streaming server, there are some problems brought by the design. Using CPU to process the streams will consume lots of CPU cycles and if the streaming server have lots of connections, resource to handle them will run low if the machine itself does not have strong CPUs. NVIDIA’s NVENC is a way of offload the transcoding to GPUs that is dedicated to such processing and leaves much more CPU cycles for other purposes. However, installing NVIDIA’s driver is a nightmare, which is why I decided to write it down for future reference.

Then the below installs NVENC SDK’s header into your system.

You can then now compile programs that uses NVIDIA’s NVENC to speed up video processing, including ffmpeg.

Setting Up Adaptive Streaming with Nginx

Recently, I’m working out a system to smoothly stream live events for an organization. That is pretty new to me and, after a bunch of research, found that Nginx with the RTMP module seems to be a good choice. There are many difficulties when setting all this up and after several days of testing, I found a good setting that is worth a post.

Setup Nginx and RTMP module

First, let’s get Nginx set up. In order to use the RTMP module, we need to compile that as an Nginx module. It would look something like this:

After all things are done, check whether nginx is compiled properly.

Capture

If you can see that Nginx RTMP is included, you can go to the next step. Before we proceed to configuring Nginx for live streaming, we should confirm what kind of resolution we should provide for live streams and how much hardware power you have.

Prerequisites

For converting live streams into several streams for adaptive streaming, you need to make sure your server have enough CPU for such workload. Otherwise, the live stream will suffer from continuous delays and/or server becomes unresponsive. I have spawn some EC2 c3.large and c3.xlarge instances, test with them and I found out their optimized CPU can handle such workload with ease. Something that also worth mention is about the I/O limits of the disks. If possible, store the HLS fragments generated to an high-speed SSD helps maintain smooth streaming experiences.

ec2CPU Usage when using an EC2 c3.xlarge instance.

Then, you also need to think about what kind of resolutions you will be offering for adaptive streaming. Generally about 4-5 variants are good enough to provide great loading speeds for different network speeds and devices. Here’s my recommended list of variants used for live streaming:

  1. 240p Low Definition stream at 288kbps
  2. 480p Standard Definition stream at 448kbps
  3. 540p Standard Definition stream at 1152kbps
  4. 720p High Definition stream at 2048kbps
  5. Source resolution, source bitrate

Configuring nginx for live streaming

Here is my own nginx.conf with comments that you can have references on.

Then, configure your live encoder to use these settings to stream into the server:
  • RTMP Endpoint: rtmp://yourserver/live/
  • RTMP Stream Name: [Whatever name you like]
Finally, configure your player for live playback. The HLS URL would look like this:
http://yourserver/hls/[The stream name above].m3u8

Recommended encoder settings for live events

If you can adjust the encoder, the following settings can help to gain better experiences.

  • Full HD Resolution (1920×1080) is recommended
  • H.264 Main profile, with target bitrate of 4000Kbps, maximum 6000Kbps
  • 25fps, 2 second keyframe interval
  • AAC audio at 128Kbps, 44.1kHz sample rate

And that’s all! I hope you can enjoy doing live events with these techniques.

Optimizing Nginx for (large) file delivery

Some times ago, I have a need to host some big files for open download. At first, I think Nginx will perform pretty well without muck configuration. In reality, there are complaints about slow and interrupted downloads which is quite annoying.

I ended up using Xender for PC to transfer the files, but after digging the Nginx docs, I did find some nice changes that can fix these problems and produce a high throughput. Here’s my tweaks made to the nginx.conf file:

  1. Turn off sendfileThe Linux sendfile call is known to have throughput degradation when in high load. Disabling it helps to keep a higher throughput at high load. Also, when serving large files with sendfile, there are no ways to control readahead.
  2. Enable TCP nopushTCP nopush fills the TCP packet to its maximum size before sending. This can help increase throughput if you’re serving large files.
  3. Use Nginx’s directio to load fileUsing directio can help improving performance by skipping a bunch of steps happened in the kernel when reading files, thus speed up the throughput.
  4. Enable the use of libaio for optimal performancelibaio allows asynchronous I/O to be done in kernel, which results in faster read and write speed. However, it needs libaio to be installed and re-compiling your Nginx in order to have it supported. I used the following flow to recompiling Nginx with aio support.

The complete nginx.conf should look like this:

There are also some lower-level tewaks like mounting your disks with noatime flags and use ext4/xfs when serving files.

Merry Christmas!

Hi! It’s almost Christmas, and it’s time to say Merry Christmas to everyone out here. This year, I really want to thank so many people for their help and support. Although some of my friends are going elsewhere to study and my mother’s having some illness, with the help of different people, I’m feeling much better now.

Finally, I recorded a nice song (“Joy to the World”) for this merciful moment, enjoy! And, Merry Christmas to everyone and have a nice new year!