Note: all the code, Dockerfiles, etc. are archived in a git repo at GitHub.

Container History

Origins

A little history is needed before we jump into building and deploying containers. Docker, which is likely still the largest container technology provider by a longshot, was originally released in 2013. In just 6 years a huge community using containers sprang up around the concept – check out DockerHub for a sense of scale in the community. Preceding the Docker phenomenon, we had LXC (Linux Containers) which had been around since around 2008, but it really never had the sticking power that Docker has enjoyed. Fast-forward to today, and there are a handful of competing container technologies, all with similar features, but some have better ecosystems or support than others.

Where We Are Today

Recently there has a push for a standardization of the container frameworks that makeup these ecosystems. The Open Container Initiative is an open governance body for working towards standardization of the container framework. Many container systems have already adopted this and become OCI compliant or aligned. Docker has also bolstered the community by donating much of its container infrastructure components to the open source world for OCI to utilize.

Goal of the Open Container Initiative

I encourage you to go to the OCI’s website and read their mission documents in whole, but I’d like to provide the brief description here as it relates to our use of containers in embedded systems. The OCI intends to create a standard for representing containers and their basic runtime requirements and interfaces for portability. From their FAQs:

The mission of the Open Container Initiative (OCI) is to promote a set of common, minimal, open standards and specifications around container technology.

What is the mission of the OCI? FAQ (https://www.opencontainers.org/faq#faq1)

This is important to us in embedded engineering because we need this portability and compatibility to fully realize the use cases outlined in the last post. Since the OCI has not reached a sufficiently mature status, I will be sticking with Docker as the container ecosystem in this post.

Docker Containers: A Hands-On Example

Objectives

In the following sections we will accomplish the following:

1. Create a basic container image from a Dockerfile
2. Create a container to build and test a custom application
3. Create a container to host the custom application
4. Export the container
5. Run the container on a different platform

Creating a Basic Container

I’m starting with the assumption that Docker is installed and you’ve been able to run the Docker hello-world image. We will use a Dockerfile to compose our basic image. A Dockerfile is a script of sorts which instructs the engine to construct our container piece by piece. I’ve put a (very) basic Dockerfile below.

##########################################
# File: Dockerfile
# Author: Jacob Calvert <jcalvert@jacobncalvert.com>
# Date: Nov-08-2019
# 
# This is a basic Dockerfile 
#
##########################################

# start from the basic busybox image
FROM busybox

# put a file in our container
COPY hello-world.txt .

What I have in my workspace on my host machine is as follows:

jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/basic $ ls
Dockerfile  hello-world.txt
jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/basic $ cat hello-world.txt
hello, world!

To build the Docker image from the Dockerfile, you run a ‘docker build’ command. The ‘-t’ specifies a tag by which we’ll reference this image, and the ‘.’ specifies the directory for the Dockerfile – in this case, the current directory.

jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/basic $ docker build -t basic .
Sending build context to Docker daemon  3.072kB
Step 1/2 : FROM busybox
 ---> 020584afccce
Step 2/2 : COPY hello-world.txt .
 ---> 11e3eeda8f8e
Successfully built 11e3eeda8f8e
Successfully tagged basic:latest
jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/basic $ 

Let’s view and run our image now.

jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/basic $ docker image ls
REPOSITORY                        TAG                 IMAGE ID            CREATED             SIZE
basic                             latest              11e3eeda8f8e        42 seconds ago      1.22MB
ubuntu                            latest              775349758637        8 days ago          64.2MB
busybox                           latest              020584afccce        9 days ago          1.22MB
hello-world                       latest              f2a91732366c        23 months ago       1.85kB
quantumobject/docker-zoneminder   latest              469615ab191d        24 months ago       1.15GB
mysql/mysql-server                latest              a3ee341faefb        2 years ago         246MB
jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/basic $ docker run -it basic
/ # ls
bin              dev              etc              hello-world.txt  home             proc             root             sys              tmp              usr              var
/ # cat hello-world.txt 
hello, world!
/ # exit
jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/basic $ 

What are we looking at here? First we list our available local images with the docker image ls command. We can see that I have several images on my development machine, including basic which was created only a few moments ago from our build command. Next I run the image we created with docker run -it <image>. The -it flags are for –interactive and –tty. These two together essentially present the container’s console as a pseudo-tty device in your terminal, in interactive mode. It is as if you are sitting in front of another machine.

Next, you can see that I have a different prompt. This is the container’s prompt. I type ls and we can see our hello-world.txt is there on the filesystem of our container as we desired via the COPY command in the Dockerfile. Lastly, we type exit which ends the busybox process and exits the container.

Now let’s look at the persistent state parts of Docker.

jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/basic $ docker system info
Client:
 Debug Mode: false

Server:
 Containers: 1
  Running: 0
  Paused: 0
  Stopped: 1
 Images: 6
 Server Version: 19.03.2
 Storage Driver: overlay2
  Backing Filesystem: extfs
  Supports d_type: true
  Native Overlay Diff: true
 Logging Driver: json-file
 Cgroup Driver: cgroupfs
 Plugins:
  Volume: local
  Network: bridge host ipvlan macvlan null overlay
  Log: awslogs fluentd gcplogs gelf journald json-file local logentries splunk syslog
 Swarm: inactive
 Runtimes: runc
 Default Runtime: runc
 Init Binary: docker-init
 containerd version: 894b81a4b802e4eb2a91d1ce216b8817763c29fb
 runc version: 425e105d5a03fabd737a126ad93d62a9eeede87f
 init version: fec3683
 Security Options:
  apparmor
  seccomp
   Profile: default
 Kernel Version: 4.4.0-165-generic
 Operating System: Linux Mint 18
 OSType: linux
 Architecture: x86_64
 CPUs: 4
 Total Memory: 15.55GiB
 Name: jacob-aspire-mint
 ID: Z2GE:5Y2A:K4LP:UC4O:QV3A:4JMR:5RLW:2DHQ:WANN:2RA3:VKJ2:UZMI
 Docker Root Dir: /var/lib/docker
 Debug Mode: false
 Registry: https://index.docker.io/v1/
 Labels:
 Experimental: false
 Insecure Registries:
  127.0.0.0/8
 Live Restore Enabled: false

Using docker system info we can see that there is 1 container and that it is stopped (not running). How do we see what that container is?

jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/basic $ docker ps -a
CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS                     PORTS               NAMES
c76e0de1a530        basic               "sh"                4 minutes ago       Exited (0) 3 minutes ago                       silly_kalam

We can use the ps command to see information about our containers. Notice the STATUS column. Our container’s execution has exited. This begs the question: can we restart a container and pick up where we left off? The answer of course is yes!

jacob@jacob-aspire-mint /media/jacob/jacob/Documents/Workspaces/Blog/virtualization-for-embedded-systems-series/containers-deep-dive/basic $ docker container start -i silly_kalam 
/ # ls
bin              dev              etc              hello-world.txt  home             proc             root             sys              tmp              usr              var
/ # 

Notice the command difference this time around. I am using docker container start -i <name> where the name is a name given to the container at run time. The container can be referenced by its human-friendly name as I have done here, or by its ID (the long UUID). Also note the -i flag; this is to open it as an interactive session again. No -t is needed since the TTY device has already been allocated. So can we work inside this running container like a real machine and see the state persist? Indeed we can as well! Let’s see it in action.

/ # cd home/
/home # mkdir -p user/workspace/
/home # cd user/workspace/
/home/user/workspace # echo "another file!" > another.file
/home/user/workspace # ls
another.file
/home/user/workspace # exit

jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/basic $ docker container start -i silly_kalam 

/ # ls
bin              dev              etc              hello-world.txt  home             proc             root             sys              tmp              usr              var
/ # cd home/user/workspace/
/home/user/workspace # ls
another.file
/home/user/workspace # cat another.file 
another file!
/home/user/workspace # 

It may be a little hard to follow, but here’s what has happened. From inside our previously started container named silly_kalam, I have created a directory /home/user/workspace. Next, I created a file named another.file and filled it with “another file!”. I then exited the container. Next, I started the container again, changed directory to my created directory, and printed out the contents of another.file.

Using this example we can see that the content inside a container is not purely ephemeral, but can persist over many starts/stops. For more persistent storage check out Docker’s Volume subsystem.

Creating a Development Environment Container

Using the knowledge gained through building a basic container, we will now create a container to use as a development environment for our example application.

The Application Specs

We want to build a simple application which demonstrates the portability of containers and also does something we can test. We also want it to be a simple application for demo purposes. With this in mind, our application will be a simple data modem. It will take in data from one source medium and spit it out as another medium. Below is a list of basic requirements codified for this application:

• Translate data from a serial device to UDP
  • Serial will be at 115200/8N1 configuration
  • UDP will listen on a configurable port
  • TTY device will be configurable
• Modem will log messages to container console

To satisfy these requirements we need to be able to build application code for the container. Why use a container to build applications? Why not just use your host machine’s development environment? Repeatability is the answer. Rather than struggle to maintain a development environment across multiple developers who have their own flavors of Linux distro and preferences and so on, we can distribute a “builder” container which has all the dependencies and tools needed to build the application (albeit a simple one in this case) from scratch and is completely independent of the host it runs on.

Building and Using the Container

For our “builder” container we create the following Dockerfile:

##########################################
# File: Dockerfile
# Author: Jacob Calvert <jcalvert@jacobncalvert.com>
# Date: Nov-09-2019
# 
# This Dockerfile creates a development environment for 
# building a sample application
#
##########################################

# start from the basic ubuntu image
FROM ubuntu

# install the needed tools in our container
RUN apt-get update && apt-get install build-essential git net-tools -y

This Dockerfile starts with an Ubuntu base image, and adds the three common packages build-essential, git, and net-tools. I won’t show the entire build process but at the end you should be able to run the new container as we did in the basic use case.

jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/dev-env $ docker run -it dev-env
root@32984d05f73f:/# ifconfig 
eth0: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1500
        inet 172.17.0.2  netmask 255.255.0.0  broadcast 172.17.255.255
        ether 02:42:ac:11:00:02  txqueuelen 0  (Ethernet)
        RX packets 19  bytes 2853 (2.8 KB)
        RX errors 0  dropped 0  overruns 0  frame 0
        TX packets 0  bytes 0 (0.0 B)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0

lo: flags=73<UP,LOOPBACK,RUNNING>  mtu 65536
        inet 127.0.0.1  netmask 255.0.0.0
        loop  txqueuelen 1  (Local Loopback)
        RX packets 0  bytes 0 (0.0 B)
        RX errors 0  dropped 0  overruns 0  frame 0
        TX packets 0  bytes 0 (0.0 B)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0

root@32984d05f73f:/# gcc
gcc: fatal error: no input files
compilation terminated.
root@32984d05f73f:/# 

Now in another tab let’s copy our source into our builder image (note: we could have simply git cloned it as well, but in many areas I work in, network access is a no-go).

jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/dev-env $ docker cp modem.c jovial_rhodes:/

And now we can see this file in our running container.

root@32984d05f73f:/# ls
bin  boot  dev  etc  home  lib  lib64  media  mnt  modem.c  opt  proc  root  run  sbin  srv  sys  tmp  usr  var
root@32984d05f73f:/# 

So let’s build our application, and grab the resulting binary.

root@32984d05f73f:/# gcc modem.c -o modem -lpthread
root@32984d05f73f:/# ls
bin  boot  dev  etc  home  lib  lib64  media  mnt  modem  modem.c  opt  proc  root  run  sbin  srv  sys  tmp  usr  var
root@32984d05f73f:/# 

jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/dev-env $ docker cp jovial_rhodes:/modem ./modem
jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/dev-env $ ls
Dockerfile  modem  modem.c
jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/dev-env $ 

We have successfully used a container to build an image for deployment. The application is a simple one, but it illustrates the point of using a “builder” container for repeatable builds. Now we can move on to deploying our application. Of course there’d be rigorous testing in a real-world application, but we can neglect that for the purposes of demonstration.

Creating a Deployment Environment Container

We want to now create a container which will start our application on boot, and run that application until we exit the container. We will start with our basic Ubuntu, and add a few things as shown below.

##########################################
# File: Dockerfile
# Author: Jacob Calvert <jcalvert@jacobncalvert.com>
# Date: Nov-09-2019
# 
# This Dockerfile creates a deployment environment for 
# the sample application
#
##########################################

# start from the basic ubuntu image
FROM ubuntu

# create a app/bin directory in our container
RUN mkdir -p /app/bin 

# copy in the app and a startup script
COPY modem /app/bin
COPY start-modem.sh /

# set the entry point
ENTRYPOINT /start-modem.sh

Notice a new directive here? The ENTRYPOINT directive is what will be run on startup of the container. Now let’s take a look at the contents of that script.

#!/bin/sh
/app/bin/modem -d $TTY_DEVICE -p $PORT

Simple right? All it does is start our application with some environment variables as parameters.

Testing our Deployment Container

Here’s where the magic of containers will really start to shine.

jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/deploy-env $ sudo docker run -it -e TTY_DEVICE=/dev/ttyACM0 -e PORT=9000 --device=/dev/ttyACM0 deploy-env 

This time we will run our container image with a few extra parameters. First, the -e flag will inject key=value pairs as environment variables. That’s how our script will know what those are at run time. Next we pass through the /dev/ttyACM0 device from the host machine to the docker container. If we modify that line to –device=/dev/ttyACM0:<custom container path> we can give the serial device a specific path in the container, otherwise it just gets identity mapped. Docker sets up a default NAT network on the host on the 172.17.0.0/24 subnet, so we will have access to this container at an IP address in that range once we start it. Also, the device hanging on /dev/ttyACM0 is just printing out a sequence for test data every second.

So what’s it look like when it runs? From the container you see:

 
Device selected is '/dev/ttyACM0'
Port base selected is 9000
UDP TX: '3
'
UDP TX: '0
'
UDP TX: '1
'
UDP TX: '2
'
UDP TX: '3
'
UDP TX: '4
'
UDP TX: '5
'
UDP TX: '6
'
UDP RX: 'hello world!
'
UDP TX: '7
'
UDP TX: '8
'
UDP TX: '9
'
UDP TX: '10
'
UDP TX: '11
'
UDP TX: '12
'
UDP TX: '13
'
UDP RX: 'hello virtualization!
'
UDP TX: '14
'
UDP TX: '15
'
UDP TX: '16
'
UDP TX: '17
'
UDP TX: '18
'
UDP TX: '19
'
UDP TX: '20
'
UDP TX: '21
'
UDP TX: '22
'
'DP TX: '23
UDP TX: '
'
UDP TX: '24

And from the netcat (nc) session on my host:

jacob@jacob-aspire-mint /workspace/virtualization-for-embedded-systems-series/containers-deep-dive/dev-env $ nc -u 172.17.0.2 9000
hello world!
hello virtualization!
14
15
16
17
18
19
20
21
22
23
24
^C

I’ve typed the “hello” statements into the netcat session and it is receiving the increment sequence from the serial device via UDP on the remote end. We have successfully created and deployed a containerized application!

Exporting the Containerized Application

Since we now have a complete application all self-contained in a container, we can export this to run on other systems. In Docker, this is as simple as:

docker save -o deployment-environment.tar deploy-env:latest

We now have a TAR archive of all the layers building up our container. This is a portable format you can move around to deploy on different machines by importing it onto another host and running it.

Running the Containerized Application on Another Machine

I used scp to copy my TAR file to another machine and imported it into Docker using:

jacob@dev-ubuntu:~# docker load < deployment-environment.tar 
bd59016c97ec: Loading layer [==================================================>]  2.048kB/2.048kB
8c558935f47c: Loading layer [==================================================>]   16.9kB/16.9kB
0cc408f84946: Loading layer [==================================================>]  2.048kB/2.048kB
Loaded image: deploy-env:latest
jacob@dev-ubuntu:~# docker image ls
REPOSITORY                TAG                 IMAGE ID            CREATED             SIZE
deploy-env                latest              d427611b8747        About an hour ago   64.2MB

Now we can run the app on a different machine just like our original host machine. (I omitted any parameters, I just want to see the modem binary print out errors).

jacob@dev-ubuntu:~# docker run -it deploy-env 
Device selected is '-p'
Failed to open device '-p'

So what’s the point?

So what’s the point? Couldn’t we just copy our modem binary to the other machine and run it in the native host? Sure, for this application, because it has no special dependencies. But imagine if your application depends on QT5, TensorFlow, and a pre-trained Data Model? Making sure all that stuff gets installed on the target host machine is a nightmare. Having a single TAR file with all dependencies built in, with your application properly parameterized ready to launch makes portability and usabilty a much easier sell, especially for embedded systems.

Said another way: if the only requirement to upgrade your application or add additional applications to a fielded embedded system is that you have the right container infrastructure, it is much easier to rapidly update existing capabilities and to deploy new capabilities to the edge with a high confidence of success.

Wrapping Up

In this post, we focused on a practical example of using containers. It’s easy to see how this technology has the ability to be incredibly impactful for embedded systems. In the next post, we will take a look at type-2 hypervisors and how to use them for embedded systems.

I hope you enjoyed the content of this post! If you did, feel free to comment or shoot me a message over at the contact page!

The post Virtualization for Embedded Systems Series: Containers Deep Dive appeared first on Jacob N Calvert.

Containerization

Simplified Management

One of the low hanging fruits of container virtualization is the ability to manage your application images in a straightforward and simplified manner. Because container images carry with them all the dependencies needed to run the application the container is intended for, we eliminate the need to prepare the host environment to deploy that application. In other words, we no longer need to worry if our application is deployed on six system variants with six different hardware configurations running six different host OS revisions. We simply package our app for the container infrastructure, and ship it as a container. Simple.

Rapid and Widespread Deployments

Utilizing the same image management capabilities, we can easily deploy our applications on a multitude of platforms, since we do not need to worry about the underlying host OS too much. For example, you have written an application which provides critical situational-awareness (SA) functionality to the Soldier. Your application simply needs the SA data as an input and it serves a web page with the resulting SA data displayed for fast, efficient dissemination to the necessary parties. This application packaged as a traditional binary would require configuration management to be performed on any deployment platform, and care would need to be taken each time the application was updated to manage dependencies. If deployed as a containerized solution, the container carries its dependencies along with it. We can then deploy in real-time to platforms we may not have directly designed the application for use on, and provide valuable capabilities to the warfighter in a blink.

Type-2 Hypervised Systems

Scratching the Nostalgia Itch

Who remembers the 80’s? Not me! But I did have a Sega as a kid. Gaming platforms these days have become more PC-like than embedded system, but in the not so distant past, gaming systems were 100% embedded systems by design. But what about scratching that itch for Mortal Combat on the Sega in 2019 without searching E-bay endlessly? This is a perfect fit for a type-2 hypervisor to help you out. The RetroPie Project is all about bringing different emulated platforms into the modern age on a Raspberry Pi. Via the hypervised environment (and assuming you have legal rights to the console title you’re emulating) you can spin up a virtual Sega and load up a copy of Mortal Combat to throw down with the boys on a Saturday night. I call that solving problems with technology!

Cross-Platform Development

Frequently in the embedded systems world, we find ourselves developing code on one platform to be deployed on another. Type-2 hypervisors make it easy to test our developed code on something that mimics the real target platform with a high level of fidelity. Using a tool like QEMU, you can quickly deploy ARMv7 code on top of a dual-core Cortex-A9 VM while being hosted on your Ubuntu Linux running an Intel chip. In the same vein, cross platform development can be achieved with a type-2 hypervisor like VirtualBox as well, by running a VM of your deployment platform and developing directly “on-platform.” For a highly flexible and incredibly powerful commercial platform that supports the system simulation paradigm for developing, deploying, testing and more for embedded systems and beyond, check out Wind River’s Simics.

Type-1 Hypervised Systems

An aside: Type-1 hypervisors are, in my opinion, one of the most useful virtualization strategies for embedded systems. For starters, embedded devices often have less processing power and RAM than their datacenter counterparts, so the low overhead factor for type-1 hypervisors makes them ideal for squeezing as much performance as possible out of the hardware platform.

Application Consolidation

Functionality implemented by software has historically been included in embedded systems in a federated manner. This has been especially true for safety critical functions in software. Federated systems have been the standard model for safety critical systems since software began to take a major role in system operation.

In federated systems, each function often had its own “box” in the system. This means for every function or function group there was an entire dedicated hardware and software stack to support it. This model was also reinforced due to limited computing resource availability on single core processors. When a single core chip was maxed out on processing power, the only solution was to add another “box.” Each function belonging to an individual “box” obviously increases the size, weight, and power (SWaP) required for the system.

By consolidating the various applications into one hardware platform we reduce the SWaP required for the system-of-systems, and to do this we can use a type-1 hypervisor. We can accomplish even more if we utilize a multicore processor with a type-1 hypervisor. This application consolidation mechanism is easily seen in the selection of an ARINC653-capable OS for the Boeing 7E7 Common Core System.

Security Risk Mitigation

One of the glaring issues of fielding embedded systems is that often, once fielded, the option to update them does not exist. When embedded systems are expected to work 24/7/365 from the time of production until replacement, this “long tail” needs to be considered. Whenever security holes are found in a fielded embedded system, it can be difficult to design a mitigation that can be applied without rearchitecting the entire system. Take for example the following scenario: a safety-critical embedded device has been deployed with an custom home-grown RTOS. It was recently discovered that this home-grown code has a major security flaw in the TCP/IP stack, despite the fact that the application only uses the serial interface. The RTOS is inflexible and it would cause too much rework to update the RTOS and application and get an ATO again. Instead of modifying the RTOS and application, you simply run a type-1 hypervisor underneath the RTOS and application, and use the type-1 hypervisor to restrict which hardware can be accessed by the RTOS and application. By simply virtualizing the application, we’ve moved it “up the stack” so that we can better manage the resources it has access to, and which resources have access to it – in this way, we can patch a large number of security issues simply by using a VM.

Legacy Migration

Since embedded systems often have long lived deployments as identified in the Security Risk Mitigation example, the addition of new features and the migration of legacy code is also a difficult task many times. Again, having a type-1 hypervisor to move your application “up the stack,” can ease the difficulty in this migration. For example, you deployed an application on Linux and it has been fielded for seven years. Recently you have new requirements for the application which requires some functionality to be moved to an RTOS to be responsive and keep up with demand. Rather than making a sweeping change to port all functionality to an RTOS, using a type-1 hypervisor can allow you to retain most of the functionality in Linux in one VM and port just the necessary functions over to an RTOS in another VM. All this while still supporting the same hardware platform.

More Examples

Here are a few more examples that don’t necessarily fall under the umbrella of embedded systems, but help round out the picture.

Scalable Services by Design

Previously I noted that Docker is a popular container engine or daemon for managing containers on a given host machine. I also mentioned Kubernetes as an orchestration engine to deploy and manage these containers hosted by Docker. Pairing these two technologies, we are able to design services that are scalable in nature right from the start.

But what exactly is meant by scalable services though? Imagine the following example. You’ve created a web application and are using Docker for the various services that make up your application. You start with less than 1000 daily users and are spinning up additional containers manually to manage the load. Suddenly, your app is featured on Slashdot and you begin experiencing the Slashdot Effect. Your running containers are overloaded and your users are denied access – not good for a fledgling web app! Now, consider if your application’s containers were being managed and monitored by Kubernetes. The same Slashdot Effect begins to occur, but the Kubernetes engine reacts by spinning up additional containers to handle the spike in load. No crash for your app, and tons of happy new users. Thanks to this type of virtualization, you can be infinitely flexible to support your users’ demand, on-demand.

Network Function Virtualization

Network function virtualization as a concept has grown in popularity rather rapidly over the past several years. It straddles the line between IT-grade virtualization and embedded systems because it takes what would have traditionally been a dedicated embedded device performing one function or group of functions and has virtualized it so that it can sit as a virtual appliance or virtual machine on enterprise grade computing platforms. This is typically deployed as a hypervised environment with management and orchestration software running on top to handle the chaining of functions. For more info on NFV, check out the Wikipedia article.

Computing Power On Demand

My website is hosted by DigitalOcean on a Droplet. A Droplet is DigitalOceans’ term for a Virtual Private Server (VPS). They have many physical behemoth servers in datacenters all over the world, and using virtualization, are able to divvy up the resources on those servers into virtual machines called Droplets. I can log into my Droplet as if it is a physical machine and do with it as I please. What’s really neat about DigitalOcean (and many other providers like it) is that they provide a way to spin up these VPS units on demand via a web API. We could actually use this API to spin up a swarm of computing power long enough for it to crunch some data for us and then turn them back off, releasing the hardware to another user! In fact, DigitalOcean uses QEMU and libvirt to accomplish their virtualization and provide this on demand compute to consumers like me. Pretty cool!

Wrapping Up

In this post, we looked at how the various types of virtualization technology can be applied in a variety of ways. In the next couple of posts, I’ll be taking a look at software platforms that enable these virtualization mechanisms, and building up example systems with mostly FOSS pieces to show what things we can accomplish and to demonstrate some of the concepts I noted in this post.

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Terms and Definitions

Right up front I want to define the terms I’ll be using throughout the post for clarification’s sake. A core is a single processing element, irrespective of the cache, RAM, peripherals, etc. which could be connected to it. A processor is a chip consisting of 1 to N cores. When talking about an application on a core of a multiprocessor, I mean any piece of code. That could be a simple Hello World, to a complex bare-metal piece of software, to a full-up OS.

Asymmetric Multiprocessing (AMP) Operation

One of the most straightforward ways to utilize more cores on the same processor is to treat each core as an individual single core processor all to its own. This mode is called asymmetric multiprocessing or AMP. This mode of operation allows you to run N applications on the N cores and treat them like N separate processors. One of the key advantages of this mode is that you can run different applications on each core, giving you greater flexibility as to your use of the multicore chip. There are some caveats to this mode of operation however. Most multicore processors do not include N copies of the supporting hardware units to make the multicore processor operate as N truly independent processors. Take for example, the NXP QorIQ P4080. It has 8 cores, but only 2 DRAM controllers. So even running in an AMP mode, you must share the DRAM controllers among the 8 cores. This couples the cores together so that they are not truly independent. This is a typical configuration for multicore systems wherein N cores share memory controller and data paths to peripherals and can present some planning challenges when using an AMP mode of operation.

Symmetric Multiprocessing (SMP) Operation

This is the mode of operation most people think of when envisioning a multicore processor. This mode of operation combines all the cores into a set of co-equal processing elements. The application is responsible for assigning work to each of the cores in the set and all hardware belongs to or is owned by the application. This is how most OSes work on multicore processors. Take for example Linux. Linux manages the workload spread across the N cores via the scheduler. The scheduler decides what process’s threads are ready to run, and divvies them up across the cores available. The Linux kernel also handles which threads of execution can access which hardware pieces, but this is a discussion for another day! The big benefit to SMP is that you have a “supervisor application” (usually the OS) managing all the processing resources and the software developer doesn’t have to put much thought into which core his software will run on. However, this abstraction can also introduce some latency into the system when a cache flush is required to move a thread of execution from one core to another.

Simultaneous Multithreading (SMT) Operation

This mode of operation is less well understood in general than its precursor SMP, although the concept is the same in operation. SMT applies to processors which support higher levels of utilization of the hardware units on the chip and requires a little bit of understanding of how a processor works. The short and sweet is that in any given processor, there are some common elements such as an ALU, instruction fetch unit, instruction decode unit, memory interface components, etc. When a processor is executing a given instruction, not all of those elements are active at the same time, even with a pipelined design. SMT allows software to view the single core in a multicore system as itself having more than one processing element available. In the parlance this is said as having a “N cores, M threads”. From the software’s point of view, after SMT is enabled, the processor simply has M cores. Most Intel processors support SMT and plenty of others do as well, like the NXP QorIQ T2080.

Bound Multiprocessing (BMP) Operation

This is a concept closely related to SMP in theory again, however with a little bit of a twist. In a BMP system, a single application owns the whole set of cores, but the application can decide to bind certain threads of execution to certain cores rather than floating them around the cores as needed. If a system supports SMP, it can easily be modified to support BMP as well (and most already have this baked in, see processor affinity settings in your favorite OS). The major benefit of BMP over SMP is that you don’t have threads of execution floating from one core to another, and so you have tighter-grained control over the execution. You do however lose some throughput capability as you may end up waiting on a bound thread to run on another core before your software can do its work, and so careful thought must be given to this mode of operation.

Mixed Multiprocessing Operation

This is not an officially recognized term, but it’s one that I think fits the bill. There are some systems that will allow you to use both AMP and SMP in the same processor. For example, when using a hypervised environment like Wind River’s Helix Virtualization Platform, you may sometimes want a configuration like the following:

When a system supports both AMP and SMP in the same chip complex, I consider this a mixed multiprocessing environment. For more information on virtualization and how it can achieve these mixed modes operation, check out my series on Virtualization in Embedded Systems.

Going Further

I hope this gave a quick intro to the different modes a multicore system can be in, but if you want more detail still, see this excellent article from NXP on these modes. It leaves out SMT, but covers AMP, SMP, and BMP pretty thoroughly.

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The world of computing as we know it today only began in the last century or so, and really only since the invention of the bipolar transistor. Fast-forward to the modern era of computing wherein we have unfathomable computing power surrounding us daily. Computing platforms and the related technology is driving advancement in every industry across the globe. From research to reality, computing platforms have been a benefactor to society time and time again. The largest benefit of modern computing platforms is that they do the heavy lifting for us – the complex calculations, the ugly mathematics, the trial and error search for a solution – without tying up a real human-being’s time and energy. Simply put, a computing platform generally is the most useful and powerful when it requires the least human interaction to complete its tasks. The ability to monitor and manage these computer platforms is as important as the jobs they perform. Additionally, as computing platforms pack more power and capability into tighter size, weight, and power constrained units, the need to easily monitor and manage these high-powered machines becomes increasingly evident.

Exploration in the Defense Industry

The Defense Industry has wholeheartedly embraced the concept of COTS (commercial off-the-shelf) tech insertion for speeding up the development and deployment of advanced capabilities to protect and defend the national interest. The wide use of COTS VPX and VME backplane technology in the Defense Industry has driven the need for a dependable platform management solution. Since many of these computing platforms are deployed in rugged and harsh operating environments, it is desirable and necessary to monitor the health of each unit in a system and manage fail-over situations should they arise. Operators are not always co-located to these systems and are not able to physically monitor and mitigate these issues. This implies that a platform management solution should be robust enough to manage fail-over situations, remotely, with limited human interaction.

High Level Solution

In keeping with the theme of COTS solutions for Defense Industry problems, the natural progression for implementing a platform management solution is to use the COTS product already in use by other industries. The Intelligent Platform Management Interface (IPMI), is the industry standard for platform management. The IPMI architecture “defines standardized, abstracted interfaces to the platform management subsystems” [1]. The IPMI specification describes a message based request/response protocol in which commands are grouped by functional sets using their Network Function Code or NetFn for short.

This protocol can be initiated over a wide variety of interfaces such as a serial port, a Keyboard Controller Style (KCS) interface, or an Intelligent Platform Management Bus (IPMB). The serial port, KCS interface and other interfaces comprise a class of interfaces known as System Interfaces. System Interfaces are used by the unit itself to communicate with its onboard IPMI controller. The IPMB belongs to a class of interfaces which are used to communicate with a unit remotely; i.e., not from within an application level software on the unit itself.

These commands over an IPMB are used to monitor and manage the individual units in a larger system. In IPMI terms, each unit would be called a Field Replaceable Unit or FRU. An example of a FRU might be one Single Board Computer (SBC) in a VPX chassis. Network Function Codes exist for managing sensor data, reading and writing FRU data, configuring and acknowledging events and alerts, powering on or off a FRU, and more.

The IPMI spec also allows for extensions to its standard yet robust set of commands. One extension is the VITA Standards Organization’s VITA46.11 Specification [2]. The VITA46.11 spec implements a set of states and commands on top of the IPMI specification which allow for more granular control of the FRU by defining FRU states, as well as a set of standard logical sensors which summarize the FRU health.

A device implementing these standards on a FRU is called an Intelligent Platform Management Controller or IPMC. Baseboard Management Controller (BMC) is another term used to describe an IPMC. The VITA46.11 spec divides implementations into Tier I implementations and Tier II categories. A Tier I IPMC implements a subset of the standard, whereas a Tier II IPMC implements the entirety of the standard.

Pairing the IPMI standard commands with VITA46.11 additions, an IPMC can be used to monitor and manage a FRU in a wide variety of situations. The real value in having an IPMC onboard a FRU, is that when multiple FRUs exists in a chassis together, an IPMI compliant Chassis Manager has access to an incredible wealth of information about what is occurring in the system. For example, in a system with multiple FRUs which contain IPMC v2.0 compliant IPMCs, the Chassis Manager can instruct each FRU to monitor certain voltages or temperatures and send an alert known as a Platform Event Message if an out-of-range event occurs on the FRU. Not only can the FRU be instructed to monitor itself, but the Chassis Manager can request any and all sensor data at any time from any FRU. This is a valuable asset in the view of system management.

Conclusion

The need to monitor and manage computing platforms grows with the increasing complexity and power of those platforms. In the Defense Industry, COTS products are a solution to quick and standards compliant tech insertion with respect to computing platforms, and platform management is no different. Using standards compliant IPMCs will reduce or remove the need for custom platform management solutions and ease system management greatly.

References

[1] IPMI v2.0

[2] VITA 46.11 

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