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In this post, I’m going to provide a very quick introduction to CoreOS. CoreOS, in case you haven’t heard of it, is a highly streamlined Linux distribution designed with containers, massive server deployments, and distributed systems/applications in mind.

CoreOS is built around a number of key concepts/technologies:

  1. The OS is updated as a whole, not package-by-package. CoreOS uses the Omaha protocol—initially engineered by Google for updating things like the Chrome browser and Chrome OS—to stay up-to-date with new versions. CoreOS also employs an active/passive dual root partition scheme. This dual root partition scheme allows CoreOS to run off one root partition while updating the other; the system then reboots onto the updated partition once an update is complete. If the system fails to boot from the updated partition, then reboot it again and it will revert to the known-good installation on the first partition.
  2. All applications run in containers. CoreOS provides out-of-the-box support for Docker containers. In fact, all applications on CoreOS run in containers. This enables separation of applications from the underlying OS and further streamlines the CoreOS update process (because applications are essentially self-contained).
  3. CoreOS leverages systemd. systemd is not unique to CoreOS; it is the new standard system and service manager for Linux. (Debian has elected to use systemd; Ubuntu will adopt systemd with 14.10, if I understand correctly; and Red Hat and related distributions already use systemd.) In CoreOS, systemd unit files are used not only for system services, but also for running Docker containers.
  4. CoreOS has a distributed key-value data store called etcd. The etcd distributed key-value data store can be used for shared configuration and service discovery. etcd uses a simple REST API (HTTP+JSON) and leverages the Raft consensus protocol. Docker containers on CoreOS are able to access etcd via the loopback interface, and thus can use etcd to do dynamic service registration or discovery, for example. etcd is also configurable via cloud-init, which means it’s friendly to deployment on many cloud platforms including OpenStack. More information on etcd is available via the etcd GitHub site.
  5. CoreOS supports deploying containers across a cluster using fleet. Fleet is another open source project that leverages etcd to deploy Docker containers (written as systemd unit files) across a cluster of CoreOS systems. Fleet leverages both etcd and systemd to support the deployment of containers across a cluster of systems. See this page for more information on clustering with CoreOS and fleet.

Taken individually—the use of a minimal Linux distribution, systemd support, the distributed key-value data store, Docker support, dual root partition w/ recoverable system updates, fleet—these technologies are interesting, but not all that revolutionary. Put them all together, however, and you have (in my opinion) a very interesting solution.

I’m quite intrigued with CoreOS and do plan on spending more time with it in the near future, so stay tuned for additional posts. In the meantime, if you’d like to see something specific about CoreOS or any related technologies, please speak up in the comments. I’ll do my best to satisfy your requests!

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This issue describes a fix I found for an issue I had when booting KVM guest domains on the Ubuntu/KVM hypervisors in my home lab. I’d been struggling with this issue for quite some time now, but only recently found what I believe to be the final fix for the problem.

First, allow me to provide a bit of background. Some time ago—I’d say around August 2012, when I left the vSpecialist team at EMC to join an OpenStack-focused team in another part of EMC—I moved my home lab over completely to Ubuntu 12.04 LTS with the KVM hypervisor. This was an important step in educating myself on Linux, KVM, libvirt, and Open vSwitch (OVS), all of which are critical core components in most installations of OpenStack.

Ever since making that change—particularly after adding some new hardware, a pair of Dell C6100 servers, to my home lab—I would experience intermittent problems booting a KVM guest. The guest would appear to boot properly, but then hang shortly after a message about activating swap space and fsck reporting that the file system was clean. Sometimes, rebooting the guest would work; many times, rebooting the guest didn’t work. Re-installing the guest sometimes worked, but sometimes it didn’t. There didn’t appear to be any consistency with regard to the host (the issue occurred on all hosts) or guest configuration. The only consistency appeared to be with Ubuntu, as virtually (no pun intended) all my KVM guests were running Ubuntu.

Needless to say, this was quite frustrating. I tried all the troubleshooting I could imagine—deleting and recreating swap space, manually checking the file system(s), various different installation routines—and nothing seemed to make any difference.

Finally, just in the last few weeks, I stumbled across this page, which indicated that adding “nomodeset” to the grub command line fixed the problem. This was a standard part of my build (it kept the console from getting too large when using VNC to connect to the guest), but it required that I was able to successfully boot the VM first. I’d noted that once I had been able to successfully boot a guest and add “nomodeset” to the grub configuration, I didn’t have any further issues with that particular guest; however, I explained that away by saying that the intermittent boot issue must have been some sort of first-time boot issue.

In any case, that page linked to this ServerFault entry, which also indicated that the use of “nomodeset” helped fix some (seemingly) random boot problems. The symptoms described there—recovery mode worked fine, booting normally after booting into recovery mode resulted in an “initctl: event failed” error—were consistent with what I’d been seeing as well.

So, I took one of the VMs that was experiencing this problem, booted it into recovery mode, edited the /etc/default/grub file to include “nomodeset” on the GRUB_CMDLINE_LINUX_DEFAULT line, and rebooted. The KVM guest booted without any issues. Problem fixed (apparently).

Thus far, this has fixed the intermittent boot issue on every KVM guest I’ve tried, so I’m relatively comfortable recommending it as a potential change you should explore if you experience the same problem/symptoms. I can’t guarantee it will work, but it has worked for me so far.

Good luck!

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Welcome to Technology Short Take #42, another installation in my ongoing series of irregularly published collections of news, items, thoughts, rants, raves, and tidbits from around the Internet, with a focus on data center-related technologies. Here’s hoping you find something useful!


  • Anthony Burke’s series on VMware NSX continues with part 5.
  • Aaron Rosen, a Neutron contributor, recently published a post about a Neutron extension called Allowed-Address-Pairs and how you can use it to create high availability instances using VRRP (via keepalived). Very cool stuff, in my opinion.
  • Bob McCouch has a post over at Network Computing (where I’ve recently started blogging as well—see my first post) discussing his view on how software-defined networking (SDN) will trickle down to small and mid-sized businesses. He makes comparisons among server virtualization, 10 Gigabit Ethernet, and SDN, and feels that in order for SDN to really hit this market it needs to be “not a user-facing feature, but rather a means to an end” (his words). I tend to agree—focusing on SDN is focusing on the mechanism, rather than focusing on the problems the mechanism can address.
  • Want or need to use multiple external networks in your OpenStack deployment? Lars Kellogg-Stedman shows you how in this post on multiple external networks with a single L3 agent.


  • There was some noise this past week about Cisco UCS moving into the top x86 blade server spot for North America in Q1 2014. Kevin Houston takes a moment to explore some ideas why Cisco was so successful in this post. I agree that Cisco had some innovative ideas in UCS—integrated management and server profiles come to mind—but my biggest beef with UCS right now is that it is still primarily a north/south (server-to-client) architecture in a world where east/west (server-to-server) traffic is becoming increasingly critical. Can UCS hold on in the face of a fundamental shift like that? I don’t know.


  • Need to scramble some data on a block device? Check out this command. (I love the site. It reminds me that I still have so much yet to learn.)

Cloud Computing/Cloud Management

  • Want to play around with OpenDaylight and OpenStack? Brent Salisbury has a write-up on how to OpenStack Icehouse (via DevStack) together with OpenDaylight.
  • Puppet Labs has released a module that allows users to programmatically (via Puppet) provision and configure Google Compute Platform (GCP) instances. More details are available in the Puppet Labs blog post.
  • I love how developers come up with these themes around certain projects. Case in point: “Heat” is the name of the project for orchestrating resources in OpenStack, HOT is the name for the format of Heat templates, and Flame is the name of a new project to automatically generate Heat templates.

Operating Systems/Applications

  • I can’t imagine that anyone has been immune to the onslaught of information on Docker, but here’s an article that might be helpful if you’re still looking for a quick and practical introduction.
  • Many of you are probably familiar with Razor, the project that former co-workers Nick Weaver and Tom McSweeney created when they were at EMC. Tom has since moved on to CSC (via the vCHS team at VMware) and has launched a “next-generation” version of Razor called Hanlon. Read more about Hanlon and why this is a new/separate project in Tom’s blog post here.
  • Looking for a bit of clarity around CoreOS and Project Atomic? I found this post by Major Hayden to be extremely helpful and informative. Both of these projects are on my radar, though I’ll probably focus on CoreOS first as the (currently) more mature solution.
  • Linux Journal has a nice multi-page write-up on Docker containers that might be useful if you are still looking to understand Docker’s basic building blocks.
  • I really enjoyed Donnie Berkholz’ piece on microservices and the migrating Unix philosophy. It was a great view into how composability can (and does) shift over time. Good stuff, I highly recommend reading it.
  • cURL is an incredibly useful utility, especially in today’s age of HTTP-based REST API. Here’s a list of 9 uses for cURL that are worth knowing. This article on testing REST APIs with cURL is handy, too.
  • And for something entirely different…I know that folks love to beat up AppleScript, but it’s cross-application tasks like this that make it useful.


  • Someone recently brought the open source Open vStorage project to my attention. Open vStorage compares itself to VMware VSAN, but supporting multiple storage backends and supporting multiple hypervisors. Like a lot of other solutions, it’s implemented as a VM that presents NFS back to the hypervisors. If anyone out there has used it, I’d love to hear your feedback.
  • Erik Smith at EMC has published a series of articles on “virtual storage networks.” There’s some interesting content there—I haven’t finished reading all of the posts yet, as I want to be sure to take the time to digest them properly. If you’re interested, I suggest starting out with his introductory post (which, strangely enough, wasn’t the first post in the series), then moving on to part 1, part 2, and part 3.


  • Did you happen to see this write-up on migrating a VMware Fusion VM to VMware’s vCloud Hybrid Service? For now—I believe there are game-changing technologies out there that will alter this landscape—one of the very tangible benefits of vCHS is its strong interoperability with your existing vSphere (and Fusion!) workloads.
  • Need a listing of the IP addresses in use by the VMs on a given Hyper-V host? Ben Armstrong shares a bit of PowerShell code that produces just such a listing. As Ben points out, this can be pretty handy when you’re trying to track down a particular VM.
  • vCenter Log Insight 2.0 was recently announced; Vladan Seget has a decent write-up. I’m thinking of putting this into my home lab soon for gathering event information from VMware NSX, OpenStack, and the underlying hypervisors. I just need more than 24 hours in a day…
  • William Lam has an article on lldpnetmap, a little-known utility for mapping ESXi interfaces to physical switches. As the name implies, this relies on LLDP, so switches that don’t support LLDP or that don’t have LLDP enabled won’t work correctly. Still, a useful utility to have in your toolbox.
  • Technology previews of the next versions of Fusion (Fusion 7) and Workstation (Workstation 11) are available; see Eric Sloof’s articles (here and here for Fusion and Workstation, respectively) for more details.
  • vSphere 4 (and associated pieces) are no longer under general support. Sad face, but time stops for no man (or product).
  • Having some problems with VMware Fusion’s networking? Cody Bunch channels his inner Chuck Norris to kick VMware Fusion networking in the teeth.
  • Want to preview OS X Yosemite? Check out William Lam’s guide to using Fusion or vSphere to preview the new OS X beta release.

I’d better wrap this up now, or it’s going to turn into one of Chad’s posts. (Just kidding, Chad!) Thanks for taking the time to read this far!

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Today marked the start of Dockercon, and Docker, Inc., the commercial entity behind the incredibly popular open source Docker project, is taking this opportunity to make its move. Already the focus of quite a bit of attention (some might say a disproportionate amount of attention), Docker is making several announcements today that indicate an intent to broaden its reach:

  • Docker (also referred to as Docker Engine) 1.0 is released today, signaling the project’s readiness to be officially used in production environments (although many firms were/are already using pre–1.0 releases in production)
  • Docker is introducing a new Enterprise Support program to give enterprises the warm cuddlies that using Docker in production environments is safe. The new program aims to provide (in Docker’s words) “training, expertise, and support necessary to stand-up mission-critical workloads built on the Docker platform.”
  • Docker is announcing a set of ten systems integrator partners to help drive commercial adoption.
  • Docker is also unveiling Docker Hub, a cloud-based service (naturally!) for users, content, and workflows. Docker Hub will offer a registry for a comprehensive list of “Dockerized” applications (taking the place of the Docker Index, I assume), along with a management console, user authentication, an automated build service, the Docker Hub API, and collaboration tools to help users manage and share applications.
  • A key part of Docker Hub is Official Repositories; these are “Dockerized” applications that are maintained, supported, and available to all Docker Hub users. This will initially include applications like MongoDB, MySQL, Nginx, Redis, and WordPress, but it is open to any community group or ISV. ()Obviously, there are some requirements around committing to maintenance of the application on an ongoing basis.)

I’m certainly a fan of using Docker (or other container technologies) where it makes sense. I fear, though, that the intense focus (some might say hype) around Docker will lead more than a few organizations down a path of trying to make Docker containers their “be all end all” solution. If that trend grows too large, that could be as damaging (if not more damaging) to Docker than being ignored. Time will tell. In the meantime, I’d strongly recommend getting a grasp on the basics of Docker so that you can better understand how it might fit into your overall solution. (My introductory post on Docker, while a bit dated, might prove useful in that regard.)

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In an earlier post, I provided an introduction to OpenStack Heat, and provided an example Heat template that launched two instances with a logical network and a logical router. Here I am going to provide another view of a Heat template that does the same thing, but uses YAML and the HOT format instead of JSON and the CFN format.

Here’s the full template (click here if the code box below isn’t showing up):

I won’t walk through the whole template again, but rather just talk briefly about a couple of the differences between this YAML-encoded template and the earlier JSON-encoded template:

  • You’ll note the syntax is much simpler. JSON can trip you up on commas and such if you’re not careful; YAML is simpler and cleaner.
  • You’ll note the built-in functions are different, as I pointed out in my first Heat post. Instead of using Ref to refer to an object defined elsewhere in the template, HOT uses get_resource instead.

Aside from these differences, you’ll note that the resource types and properties match between the two; this is because resource types are separate and independent from the template format.

Feel free to post any questions, corrections, or clarifications in the comments below. Thanks for reading!

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In this post, I’m going to provide a quick introduction to OpenStack Heat, the orchestration service that allows you to spin up multiple instances, logical networks, and other cloud services in an automated fashion. Note that this is only an introductory post—I’m not an expert on Heat, but I did want to share at least some basic information to help others get started as well.

Let’s start with some terminology, so that there is no confusion about the terms later when we start using them in specific examples:

  • Stack: In Heat parlance, a stack is the collection of objects—or resources—that will be created by Heat. This might include instances (VMs), networks, subnets, routers, ports, router interfaces, security groups, security group rules, auto-scaling rules, etc.
  • Template: Heat uses the idea of a template to define a stack. If you wanted to have a stack that created two instances connected by a private network, then your template would contain the definitions for two instances, a network, a subnet, and two network ports. Since templates are central to how Heat operates, I’ll show you examples of templates in this post.
  • Parameters: A Heat template has three major sections, and one of those sections defines the template’s parameters. These are tidbits of information—like a specific image ID, or a particular network ID—that are passed to the Heat template by the user. This allows users to create more generic templates that could potentially use different resources.
  • Resources: Resources are the specific objects that Heat will create and/or modify as part of its operation, and the second of the three major sections in a Heat template.
  • Output: The third and last major section of a Heat template is the output, which is information that is passed to the user, either via OpenStack Dashboard or via the heat stack-list and heat stack-show commands.
  • HOT: Short for Heat Orchestration Template, HOT is one of two template formats used by Heat. HOT is not backwards-compatible with AWS CloudFormation templates and can only be used with OpenStack. Templates in HOT format are typically—but not necessarily required to be—expressed as YAML (more information on YAML here). (I’ll do my best to avoid saying “HOT template,” as that would be redundant, wouldn’t it?)
  • CFN: Short for AWS CloudFormation, this is the second template format that is supported by Heat. CFN-formatted templates are typically expressed in JSON (see here and see my non-programmer’s introduction to JSON for more information on JSON specifically).

OK, that should be enough to get us going. (BTW, the OpenStack Heat documentation actually has a really good glossary. Please note that this link might break as OpenStack development continues.)

Architecturally, Heat has a few major components:

  • The heat-api component implements an OpenStack-native RESTful API. This components processes API requests by sending them to the Heat engine via AMQP.
  • The heat-api-cfn component provides an API compatible with AWS CloudFormation, and also forwards API requests to the Heat engine over AMQP.
  • The heat-engine component provides the main orchestration functionality.

All of these components would typically be installed on an OpenStack “controller” node that also housed the API servers for Nova, Glance, Neutron, etc. As far as I know, though, there is nothing that requires them to be installed on the same system. Like most of the rest of the OpenStack services, Heat uses a back-end database for maintaining state information.

Now that you have an idea about Heat’s architecture, I’ll walk you through an example template that I created and tested on my own OpenStack implementation (running OpenStack Havana on Ubuntu 12.04 with KVM and VMware NSX). Here’s the full template:

(Can’t see the code above? Click here.)

Let’s walk through this template real quick:

  • First, note that I’ve specified the template version as “AWSTemplateFormatVersion”. One thing that confused me at first was the relationship between the template format (CFN vs. HOT) and resource types. It turns out these are independent of one another; you can—as I have done here—use HOT resource types (like OS::Neutron::Net) in a CFN template. Obviously, if you use HOT resources you’re not fully compatible with AWS. Also, as I stated earlier, CFN templates are typically expressed in JSON (as mine is). Heat does support YAML for CFN templates, although again you’d be sacrificing AWS compatibility.
  • You’ll note that my template skips any use of parameters and goes straight to resources. This is perfectly acceptable, although it means that some values (like the shared public provider network to which the logical router uplinks and the security group) have to be hard-coded in the template.
  • One thing that the template format does control is some of the syntax. So, for example, you’ll note the template uses “Resources”, “Type”, and “Properties.” In some of the other template formats, these could be specified lowercase.
  • The first resource defined is a logical network, defined as type OS::Neutron::Net.
  • The next resource is a subnet (of type OS::Neutron::Subnet), which is associated with the previously-defined logical network through the use of the Ref built-in function on line 20. Built-in functions are another thing controlled by the template format, so when you want to refer to another object in a CFN template, you’ll use the Ref function as I did here. This associates the “network_id” property of the subnet with the logical network defined just prior. You’ll also note that the subnet resource has a number of properties associated with it—CIDR, DNS name servers, DHCP, and gateway IP address.
  • The third resource defined is a logical router.
  • After the logical router is defined, the template links the logical router to a pre-existing provider network via the OS::Neutron::RouterGateway type. (This was deprecated in Icehouse in favor of an external_gateway_info property on the logical router.) The UUID listed there is the UUID of a pre-existing provider network. Note the use of the Ref function again to link this resource back to the logical router.
  • Next up the template creates an interface on the logical router, using two Ref instances to link this router interface back to the logical router and the subnet created earlier. This means we are adding an interface to the referenced logical router on the specified subnet (and that interface will assume the IP address specified by the “gateway_ip” property on the subnet).
  • Next the template creates two Neutron ports, and links them to the default security group. Note that if you don’t specify a security group when creating the Neutron port, it will have none—and no traffic will pass.
  • Finally, the Heat template creates two instances (type OS::Nova::Server), using the “m1.xsmall” flavor and a hard-coded Glance image ID. These instances are connected to the Neutron ports created earlier using the Ref function once more.

(In case it wasn’t obvious already, you can’t just copy-and-paste this Heat template and use it in your own environment, as it references UUIDs for objects in my environment that won’t be the same.)

If you are going to use JSON (as I have here), then I’d recommend bookmarking a JSON validation site, such as

Once you have your Heat template defined, you can then use this template to create a stack, either via the heat CLI client or via the OpenStack Dashboard. I’ll attach a screenshot from a stack that I deployed via the Dashboard so that you can see what it looks like (click the image for a larger version):

A deployed Heat stack in OpenStack Dashboard

Kinda nifty, don’t you think? Anyway, I hope this brief introduction to OpenStack Heat has proven useful. I do plan on covering some additional topics with OpenStack Heat in the near future, so stay tuned. In the meantime, if you have any questions, corrections, or clarifications, I invite you to add them to the comments below.

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Crossing the Threshold

Last week while attending the CloudStack Collaboration Conference in my home city of Denver, I had a bit of a realization. I wanted to share it here in the hopes that it might serve as an encouragement for others out there.

Long-time readers know that one of my projects over the last couple of years has been to become more fluent in Linux (refer back to my 2012 project list and my 2013 project list). I gave myself a B+ for my efforts last year, feeling that I had made good progress over the course of the year. Even so, I still felt like there was still so much that I needed to learn. As so many of us are inclined to do, I was more focused on what I still hadn’t learned instead of taking a look at what I had learned.

This is where last week comes in. Before the conference started, I participated in a couple of “mini boot camps” focused on CloudStack and related tools/clients/libraries. (You may have seen some of my tweets about tools like cloudmonkey, Apache libcloud, and awscli/ec2stack.) As I worked through the boot camps, I could hear the questions that other attendees were asking as well as the tasks with which others were struggling. Folks were wrestling with what I thought were pretty simple tasks; these were not, after all, very complex exercises. So the lab guide wasn’t complete or correct; you should be able to figure it out, right?

Then it hit me. I’m a Linux guy now.

That’s right—I had crossed the threshold between “working on being a Linux guy” and “being a Linux guy.” It’s not that I know everything there is to know (far from it!), but that the base level of knowledge had finally accrued to a level where—upon closer inspection—I realized that I was fluent enough that I could perform most common tasks without a great deal of effort. I knew enough to know what to do when something didn’t work, or wasn’t configured properly, and the general direction in which to look when trying to determine exactly what was going on.

At this point you might be wondering, “What does that have to do with encouraging me?” That’s a fair question.

As IT professionals—especially those on the individual contributor (IC) track instead of the management track—we are tasked with having to constantly learn new products, new technologies, and new methodologies. Because the learning never stops (and that isn’t a bad thing, in my humble opinion), we tend to focus on what we haven’t mastered. We forget to look at what we have learned, at the progress that we have made. Maybe, like me, you’re on a journey of learning and education to move from being a specialist in one type of technology to a practitioner of another type. If that’s the case, perhaps it’s time you stop saying “I will be a <new technology> person” and say “I am a <new technology> person.” Perhaps it’s time for you to cross the threshold.

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Some time ago, I introduced you to the idea of Linux network namespaces, and provided an overview of some of the commands needed to interact with network namespaces. In this post, I’ll follow up on that post with some additional information on using network namespaces with other types of network interfaces.

In the previous network namespaces article, I mentioned (incorrectly, it turns out) that you had to use virtual Ethernet (veth) interfaces in order to connect a namespace to the physical network:

It turns out you can only assign virtual Ethernet (veth) interfaces to a network namespace.

I say “incorrectly” because you are able to assign more than just virtual Ethernet interfaces to a Linux network namespace. I’m not sure why I arrived at that conclusion, because subsequent testing—using Ubuntu 12.04 LTS as with the original testing—showed no problem assigning physical interfaces to a particular network namespace. The VMs I used for the subsequent testing are using a different kernel (the 3.11 kernel instead of whatever I used in the previous testing), so it’s entirely possible that’s the difference. If I get the opportunity, I’ll try with earlier kernel builds to see if that makes any difference.

In any case, assigning other types of network interfaces to a network namespace is just like assigning veth interfaces. First, you create the network namespace:

ip netns add <new namespace name>

Then, you’d assign the interface to the namespace:

ip link set <device name> netns <namespace name>

For example, if you wanted to assign eth1 to the “blue” namespace, you’d run this:

ip link set eth1 netns blue

Please note that I haven’t found a way to unassign an interface from a network namespace other than deleting the namespace entirely.

If you want to assign a VLAN interface to a namespace, the process is slightly different. You’ll have to create the namespace first, as with physical and veth interfaces, but you’ll also have to create the VLAN interface in the “default” namespace first, then move it over to the desired namespace.

For example, first you’d create the namespace “red”:

ip netns add red

Then you’d create the VLAN interface for VLAN 100 on physical interface eth1:

ip link add link eth1 name eth1.100 type vlan id 100

The generic form of this command is this:

ip link add link <physical device> name <VLAN device name> type vlan id <VLAN ID>

Note that you can’t use ip netns exec to run the command to create the VLAN interface in the network namespace directly; it won’t work because the parent interface upon which the VLAN interface is based doesn’t exist in the namespace. So you’ll create the VLAN interface in the default namespace first, then move it over.

Once the VLAN interface is created, then move it to the target namespace:

ip link set eth1.100 netns red

One (sort of) interesting thing I noted in my testing was that link status and IP addresses don’t move between namespaces. Therefore, don’t bother assigning an IP address or setting the link state of an interface before you move it to the final namespace, because you’ll just have to do it again.

To make the interface functional inside the target namespace, you’ll use ip netns exec to target the specific configuration commands against the desired namespace. For example, if the VLAN interface eth1.100 exists in the namespace “blue”, you’d run these commands:

ip netns exec blue ip addr add dev eth1.100

That adds the IP address to the interface in the namespace; then you’d use this command to set the link status to up:

ip netns exec blue ip link set eth1.100 up

Then you can test network connectivity with our good friend ping like this:

ip netns exec blue ping -c 4

(Obviously, you’d want to substitute an appropriate IP address there for your specific configuration and environment.)

I hope this additional information on working with Linux network namespaces is useful. As always, I invite and encourage any questions, thoughts, or corrections in the comments below. All courteous comments are welcome!

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In this post, I’m going to talk about a way to modify QCOW2-based images without having to boot up a full-blown virtual machine based on the image. I found this process while trying to change the behavior of cloud-init on the official Ubuntu cloud images from Canonical.

While I had successfully built my own Ubuntu 12.04 LTS image for my OpenStack-based home lab (and it worked just fine), I was keen to use Canonical’s official cloud images as they were much smaller (around 250MB versus 1.5GB for my home-built image). Given that disk space is at a bit of a premium in my home lab, using the smaller images would be quite beneficial. The problem was that cloud-init wasn’t configured to operate in a way that worked for my home lab environment.

The question, of course, was…how do I do this? I tried a couple different approaches:

  1. I installed VirtualBox on one of my OS X systems, because I knew VirtualBox supported QCOW2 images. Unfortunately, trying to launch a VM using one of the official cloud images as a root disk failed. (I presume this is because the cloud images assume the use of the virtio block driver, which VirtualBox doesn’t support.)

  2. Next, I installed a plain jane Ubuntu VM in VirtualBox and attached the official cloud image QCOW2 file as a second hard disk. That didn’t work either; the Ubuntu VM refused to recognize the filesystem(s) on the QCOW2 image.

I was about at my wits’ end when I found this post. Aha! I’d heard of guestfish before, but I hadn’t thought to try it in this instance. Guestfish is part of the larger libguestfs project.

So I tried the instructions in the post I’d found. Unfortunately, it failed the first time I tried, but I later—thanks to qemu-img check—found that the Ubuntu 12.04 cloud image I’d downloaded was somehow damaged/corrupted. Using a new download of the image was successful.

Here are the steps I used to modify the contents of a QCOW2 disk image using guestfish:

  1. Starting from a vanilla Ubuntu 12.04 LTS install, use apt-get update and apt-get install guestfish to install guestfish. (You’ll probably need/want to use sudo with both of these commands.)

  2. During the installation of guestfish, you can select to create a “virtual appliance” that guestfish uses. If you choose not to do this during installation, you can always do it later with sudo update-guestfs-appliance.

  3. Once guestfish is installed and the virtual appliance is created/updated, launch guestfish itself with sudo guestfish. Note that if you don’t have root privileges or don’t use sudo, guestfish will still launch and act like everything is working—but it will fail. Once guestfish is up and running, you’ll be sitting at a “> <fs>” prompt.

  4. Type add <QCOW2 image filename> (obviously substituting the full path and correct filename for the QCOW2 image you’d like to modify).

  5. Type run and press Enter. You’ll see a progress bar that updates the status of the guestfs virtual appliance. Once it’s complete, you’ll be returned to the “> <fs>” prompt.

  6. Enter list-filesystems. Guestfish should return a list of filesystems on the QCOW2 disk image. In the case of the official Canonical cloud images, only “/dev/vda1″ is returned.

  7. To gain access to the filesystem in the QCOW2 image, enter mount <device specification> <guestfish mount point>. For example, when I was modifying the Canonical cloud images, I used mount /dev/vda1 /. Keep in mind you’re not mounting to the system on which guestfish is running; you’re mounting to the guestfish virtual appliance.

  8. Now just edit the files in the QCOW2 image using whatever method you prefer. In my case, I needed to edit /etc/cloud/cloud.cfg, so I just used vi to edit the file and make whatever changes were needed.

  9. When you’re done, type exit to quit guestfish and commit changes to the QCOW2 image.

And that’s it! Using this technique I was able to quickly and easily modify the Ubuntu cloud images so that cloud-init behaved in the way that I wanted.

Hopefully this information helps someone else. If you have any questions or corrections, I invite you to share in the comments below.

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If you’ve been following trends in the “cloud” world, you’ve probably heard of Docker. In this post I’m going to provide a quick introduction to Docker, which might be helpful if you’re wondering what Docker is and why it’s garnered so much attention.

The best way to describe Docker is to use the phrase from the Docker web site—Docker is “an open source project to pack, ship and run any application as a lightweight container.” The idea is to provide a comprehensive abstraction layer that allows developers to “containerize” or “package” any application and have it run on any infrastructure. The use of container here refers more to the consistent, standard packaging of applications rather than referring to any underlying technology (a distinction that will be important in a moment). The most common analogy used to help people understand Docker is saying that Docker containers are like shipping containers: they provide a standard, consistent way of shipping just about anything. Docker containers provide a standard, consistent way of packaging just about any application. (At least, that’s the idea.)

So what are the underlying technologies that make up Docker?

  • Docker leverages LXC (Linux Containers), which encompasses Linux features like cgroups and namespaces for strong process isolation and resource control. If you’re not familiar with LXC, I suggest having a look at my introductory post on LXC. Docker’s use of LXC is why I mentioned earlier that Docker’s use of container is more about consistent/standard packaging than any specific technology. While Docker currently leverages LXC, the Docker architecture isn’t necessarily limited to LXC. In theory, Docker could leverage KVM to do the same things it does today with LXC. (Update: While in the process of finishing this post, Docker released version 0.9, which does offer the ability to replace LXC with a different back-end “engine” called libcontainer.)
  • Docker leverages a copy-on-write filesystem (currently AUFS, but other filesystems are being investigated). This allows Docker to instantiate containers (using that term to refer to Docker’s construct, not the LXC construct) very quickly—instead of having to make full copies of whatever files comprise a container, Docker can basically use “pointers” back to existing files. It seems to me this is a big part of what makes Docker so useful.
  • Perhaps due in part to the use of a copy-on-write file system, Docker containers are easily “linked” to other containers. Some people refer to this as stacking containers (continuing the shipping container analogy); to me, it makes more sense to talk about layering one container on top of another. For example, you might create a container that is based on a base Ubuntu image, and then in turn create another container that is based on the first container.
  • Docker uses a “plain text” configuration language (I suppose you could think of it as a domain-specific language, or DSL) to control the configuration of an application container, such as what files should (or should not) be included, what network ports should be open, and what processes/applications should be running inside the container. (I’ll provide an example of a Docker file in just a few moments.)

Now that you have an idea of the basics, let’s take a deeper look. For the purposes of this article, I’m using Ubuntu 12.04 LTS as the Linux platform. Please keep in mind that different distributions may use different commands.

Installing Docker

To install Docker on Ubuntu, it’s a pretty straightforward process.

  1. First, you’ll want to make sure you are running at least the 3.8 kernel. (Newer versions of 12.04 LTS, such as 12.04.4, appear to be installing the 3.11 kernel by default, so you may not have to do this step.) To install the 3.8 kernel (in case you’re running something older), just run these two commands and reboot:
sudo apt-get update
sudo apt-get install linux-image-generic-lts-raring \
  1. Next, add the Docker repository key to your local Apt keychain:
sudo apt-key adv --keyserver \
--recv-keys 36A1D7869245C8950F966E92D8576A8BA88D21E9
  1. Add the Docker repository to your list of repositories:
sudo sh -c "echo deb docker main \
> /etc/apt/sources.list.d/docker.list"
  1. Finally, install Docker with an apt-get combo:
sudo apt-get update
sudo apt-get install lxc-docker

Note that this automatically installs LXC, but an “older” version (0.7.5). I haven’t tested to see if Docker will work with the newer alpha release of LXC 1.0.0 that is available in the precise-backports repository (see this post for more information).

Launching a Simple Docker Container

Now that Docker is installed, you can launch a quick Docker container like this:

sudo docker run -i -t ubuntu /bin/bash

If everything is working, the first time you run this command it will automatically download an Ubuntu image (as specified by “ubuntu” on that command line) and create a simple Docker container that just runs the bash shell. You’ll get dropped into an odd prompt that will look something like this (the numbers will change from system to system):


You’re now running in an Ubuntu environment, but it’s a clean environment—there are no daemons running, nothing listening on various network ports, nothing else except the bash shell. If you run ifconfig, you’ll see that eth0 has an IP address, but that’s it. When you type exit, the container will terminate and you’ll be returned to the prompt for your host system.

Now, let’s take a look at what happened as a result of running that command. If you run sudo docker images, you will see a list of available Docker images that were downloaded to your local system. They’ll all be listed as “ubuntu” in the first column, and it’s the second column that will differentiate them—you’ll see listings for 12.04, 12.10, 13.04, 13.10, etc. You could run a specific Ubuntu image with a command like this:

sudo docker run -i -t ubuntu:13.10 /bin/bash

If you run this command, you’ll note that you’re launched into this new container very quickly. This is one of the strengths of Docker: once an image has been downloaded, launching containers based on that image is very fast.

One other thing you’ll note about Docker is that the containers you start up are ephemeral—meaning that changes you make to the container aren’t persistent. Try this: start up a container (you can use a simple container like the examples I’ve provided already), create a file, and then exit the container. Start the container again, and you’ll see that the file you created previously no longer exists. There are workarounds for this, but for the sake of keeping this post to a manageable size I’ll save discussing those workarounds for another time.

Launching More Complex Docker Containers

So what if you need a Docker container that does need to have a daemon running and listening on a network port? It’s great that you can run a container for the bash shell, but that’s not particularly useful. What if you wanted to run a MySQL container, an SSH container, or a web server container?

To do something like that, you’ll need to utilize a Dockerfile. A Dockerfile is a plain text file that tells Docker exactly how to construct a Docker image. (I mentioned earlier that Docker had its own DSL; this is what I’m talking about.) Here’s an example Dockerfile:

FROM ubuntu:12.04
MAINTAINER Joe Shmoe "[email protected]"
RUN echo "deb precise main universe" > /etc/apt/sources.list
RUN apt-get update
RUN apt-get install -y nginx
CMD ["nginx"]

The syntax for a Dockerfile is INSTRUCTION arguments, where “INSTRUCTION” refers to commands like FROM, RUN, or CMD. Here are a few quick notes:

  • The FROM command is what enables Docker containers to be based on other Docker containers. In this case, the Docker container is based on the Ubuntu image; specifically, the Ubuntu 12.04 image.
  • The RUN commands execute various commands within the new container. You’d use RUN instructions to install packages, modify files, etc.
  • The EXPOSE command exposes a listening port on a Docker container.
  • The CMD instruction tells what command/daemon to run when the container launches.

I don’t have room here for a full Dockerfile tutorial, but Docker themselves has a great one. Suffice it to say that if you want anything more complex than a simple bash shell, you’re probably going to need to use a Dockerfile.

Once you have a working Dockerfile (just as a heads-up, the above example doesn’t actually work—sorry), you can create a Docker image from that Dockerfile using the docker build command. Once you have a working image, you can then base other Docker containers on that image (using the FROM instruction in a subsequent Dockerfile).

Exploring Other Docker Commands

There are also some other Docker commands you’ll find useful:

  • The docker images command lists all the Docker images available on the system. This includes images that you’ve downloaded from elsewhere as well as images you’ve created.
  • As I mentioned earlier, docker build allows you to create your own images from a Dockerfile.
  • To see which Docker containers are running, use docker ps.
  • The docker rmi command removes Docker images.
  • Finally, just run docker help if you need to see what other commands are available.

Trying Out Docker

You don’t need a lot to try out Docker; a simple Ubuntu 12.04 LTS VM running on VirtualBox, VMware Fusion, or VMware Workstation will work fine. Nested virtualization support isn’t required when using LXC, and the Docker containers themselves are pretty lightweight. To try out Docker, you only need to create a VM and follow the instructions provided earlier for installing Docker. Once you’ve got that, you’re ready to explore it for yourself.

For additional information or another perspective on Docker, I recommend taking a look at this presentation on SlideShare by James Turnbull (formerly with Puppet Labs, now with Docker).

As always, all courteous questions, thoughts, suggestions, or corrections are welcome.

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