Category Archives: cloud

Docker Virtual Networking with Socketplane.io

Screen Shot 2015-01-16 at 11.03.27 AM

Screen Shot 2015-01-16 at 11.04.21 AMScreen Shot 2015-01-16 at 11.03.50 AM

Screen Shot 2015-01-16 at 11.06.36 AM

ImgCred: http://openvswitch.org/, http://socketplane.io/, https://consul.io/, https://docker.com/

Containers have no doubt been a hyped technology in 2014 and now moving into 2015. Containers have been around for a while now (See my other post on a high-level overview of the timeline) and will be a major technology to think about for the developer as well as within the datacenter moving forward.

Today I want to take the time to go over Socketplane.io’s first preview of the technology they have been working on and since announcing their company in mid-october. Socketplane.io is “driving DevOps Defined Networking by enabling distributed security, application services and orchestration for Docker and Linux containers.” and is backed by some great tech talent, Brent Salisbury, Dave Tucker, Madhu Venugopal, John M. Willis who all bring leading edge network and ops skills into one great effort which is socketplane.io. I have had the pleasure to meet up with Brent and Madhu at ONS last year and have done some work with Brent way back when I was working on Floodlight, and am very excited for the future of Socketplane.io.

What’s behind Socketplane.io and What is the current preview technology?

The current tech preview released on github allows you to get a taste of multi-host networking between Dockerhosts using Open vSwitch and Consul as core enablers by building VXLAN tunnels between hosts to connect docker containers on the same virtual(logical) network with no remote/external SDN controller needed. The flows are programmed via OVSDB into the software switch so the user experience and maintenance is smooth with the least amount of moving parts. Users will interact with a wrapper CLI called “socketplane” for docker that also controls how socketplane’s virtual networks are created, deleted and manipulated. Socketplane’s preview uses this wrapper but if your following Docker’s plugin trend then you know they hope to provide virtual network services this way in the future (keep posted on this). I’d also love to see this tech be portable to other container technologies such as LXD or Rocket in the future. Enough text, lets get into the use of Socketplane.io

Walkthrough

First lets look at the components of what we will be setting up in this post. Below you will see 2 nodes: socketplane node1 and socketplane node2, we will be setting up these using Vagrant and Virtualbox using Socketplane’s included Vagrantfile. In these two nodes, when socketplane starts up we it will install OVS and Docker and start a socketplane container that runs Consul for managing network state. (one socketplane container will be the master, I’ll show more on this later as well). Then we can create networks, create containers and play with some applications. I will cover this in detail as well as show how the hosts are connected via VXLAN and demo a sample web application across hosts.

socketplane-arch

Setup Socketplane.io’s preview.

First install Virtualbox and Vagrant (I dont cover this, but use the links), then lets Checkout the repo

socketplanedemo1

Set an environment variable named SOCKETPLANE_NODES that tells the installation file how many nodes to setup on your local environment. I chose 3. Then run “vagrant up” in the source directory.

socketplanedemo2

After a few or ten minutes you should be all set to test out socketplane thanks to the easy vagrant setup provided by the socketplane guys. (There are also manual install instructions on their github page if you fancy setting this on on bare-metal or something) You should see 3 nodes in virtualbox after this. Or you can run “vagrant status”

socketplanedemo15

Now we can SSH into one of our socketplane nodes. Lets SSH into node1.

socketplamedemo3

Now you SSHed into one of the socketplane nodes. We can issues a “sudo socketplane” command and see the available options the CLI tool gives us.

socketplanedemo5

socketplanedemo4

Some of the commands that are used to run. start, stop, remove etc containers are used via “socketplanerun | start | stop | rm | attach” and these are used just like “docker run | start | stop | rm | attach”

Socketplane sets up a “default” network that (for me) has a 10.1.0.0/16 subnet address and if you run “socketplane network list” you should see this network. To see how we can create virtual networks (vnets) we can issue a command pictures below “socketplane network create foo4 10.6.0.0/16”

Screen Shot 2015-01-16 at 1.27.10 PM

This will create a vnet named foo4 along with a vlan for vxlan and default gateway at the .1 address. Now we can see both our default network and our “foo4” network in the list command.

Screen Shot 2015-01-16 at 1.55.26 PM

If we look at our Open vSwitch configuration now using the “ovs-vsctl show” command we will also see a new port named foo4 that acts as our gateway so we can talk to the rest of the nodes on this virtual network. You should also see the vxlan endpoints that aligns with your eth1 interfaces on the sockeplane node.

Screen Shot 2015-01-16 at 2.07.18 PMGreat, now we are all set up so run some containers that connect over the virtual network we just created. So on socketplane-1 issue a “sudo socketplane run -n foo4 -it ubuntu:14.10 /bin/bash”, this will start a ubuntu container on socketplane-1 and connect it to the foo4 network.

Screen Shot 2015-01-16 at 2.12.09 PM

You can Ctrl-Q + Ctrl-P to exit the container and leave the tty open. If you issue a ovs-vsctl show command again you will see a ovs<uuid> port added to the docker0-ovs bridge. This connects the container to the bridge allowing it to communicate over the vnet. Lets create another container, but this time on our socketplane-2 host. So exit out and ssh into socketplane-2 and issue the same command. We should then be able to ping between our two containers on different hosts using ths same vnet.

Screen Shot 2015-01-16 at 2.18.18 PM

Awsome, we can ping out first container from our second without having to setup any network information on the second host. This is because the network state it propagated across the cluster so when we reference “foo4” on any of the nodes it will use the same network information. If you Ctrl-Q + Ctrl-P while running ping, we can also see the flows that are in our switch. We just need to use appctl and reference our docker0-ovs integration bridge.

Screen Shot 2015-01-16 at 2.22.05 PM

As we can see our flows indicate the VXLAN flows thatheader and forward it to the destination vxlan endpoint and pop (action:pop_vlan) the vlan off the encap in ingress to our containers.

To show a more useful example we can start a simple web application on socketplane-2 and access it over our vnet on socketplane-1 without having to use the Dockerhost IP or NAT. See blow.

First start an image named tutum/hello-world and add it to the foo4 network and expose port 80 at runtime and give it a name “Web”. Use the “web” name with the socketplane info command to get the IP Address.

Screen Shot 2015-01-16 at 2.28.10 PM

Next, logout and SSH to socketplane-1 and run an image called tutm/curl (simple curl tool) and run a curl <IP-Address> and you should get back a response from the simple “Web” container we just setup.

Screen Shot 2015-01-16 at 2.31.20 PM

This is great! No more accessing pages based on host addresses and NAT. Although a simple use-case, this shows some of the benefit of running a virtual network across many docker hosts.

A little extra

So i mentioned before that socketplane runs Consul in a separate container, you can see the logs of consul by issuing “sudo socketplane agent logs” on any node. But for some more fun and to poke around at some things we are going to use nsenter. First find the socketplane docker container, then follow the commands to get into the socketplane container.

Screen Shot 2015-01-16 at 2.41.32 PM

Now your in the socketplane container, we can issue an ip link see that socketplane uses HOST networking to attach consul and get consul running on the host network so the Consul cluster can communicate. We can confirm this by looking at the script used to start the service container.Screen Shot 2015-01-16 at 2.41.49 PM

See line:5 of this snippet, docker runs socketplane with host networking.

socketplane in container with host networking

You can issue this command on the socketplane-* hosts or in the socketplane container and you should receive a response back from Consul showing you that is listening on 8500.

Screen Shot 2015-01-16 at 2.42.40 PM

You can issue “consul members” on the socketplane hosts to see the cluster as well.

Screen Shot 2015-01-16 at 2.46.08 PM

You can also play around with consul via the python-consul library to see information stored in Consul.

Screen Shot 2015-01-16 at 2.53.38 PM

Conclusion

Overall this a great upgrade to the docker ecosystem, we have seen other software products like Weave, Flannel, Flocker and others i’m probably missing address a clustered Docker setup with some type of networking overlay or communications proxy to connect multi-hosted containers. Socketplane’s preview is completely opensource and is developed on github, if your interested in walking through the code, working on bugs or possibly suggesting or adding features visit the git page. Overall I like the OVS integration a lot mainly because I am a proponent of the software switch and pushing intelligence to the edge. I’d love to see some optional DPDK integration for performance in the near future as well as more features that enable fire-walling between vnets and others. I’m sure its probably on the horizon and am eagerly looking forward to see what Socketplane.io has for containers in the future.

cheers!

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Docker Remote Host Management with Openstack

Untitled
Screen Shot 2012-07-26 at 6.34.17 PM

So I decided to participate in #DockerGlobalHackday and oh boy was it a learning experience. First off, the hackday started off with great presentations from some of the hackers and docker contributors. One that caught my eye was Host Management (https://www.youtube.com/watch?v=lZGmvGw-mWc) and (https://github.com/docker/docker/issues/8681)

Ben Firshman and contributors thought up and created this feature for Docker that lets you provision remote daemons on demand given cloud providers. It had me thinking that maybe I should hack on a driver for a Local Openstack Deployment. So I did, and this is my DockerHackDayHack.

https://github.com/wallnerryan/docker/tree/host-managment-openstack 

https://github.com/bfirsh/docker/pull/13

*Note, the code is raw, very raw, I haven’t coded in Go until this Hackday 🙂 Which is what I guess it is good for.

*Note this code was developed using Devstack with Flat Network orignaly, so there is some rough edged code for supporting out of the box devstack with nova network but it probably won’t work 🙂 I’ll make an update on this soon.

*Note the working example was testing on Openstack Icehouse with Neutron Networking. Neutron has one public and one private network. The public network is where the floating ip comes from for the docker daemon.

Here are the options now for host-management:

./bundles/1.3.0-dev/binary/docker-1.3.0-dev hosts create
Screen Shot 2014-11-03 at 3.29.50 PM

Notice the areas with  “–openstack-” prefix, this is what was added. If your using neutron network then the network for floating ips is needed. The image can be a Ubuntu or Debian based cloud image, but must support Cloud-Init / Metadata Service. This is how the docker installation is injected.Below is an example of how to kickoff a new Docker OpenStack Daemon: (beware the command is quite long with openstack options, replace X.X.X.X with your keystone endpoint, as well as UUIDs of any openstack resources.) It also includes –openstack-nameserver, this is not required but in my case I was, and will inject a nameserver line into the resolve.conf of the image using Cloud-Init / Metadata Service

In the future I plan on making this so we don’t need as many UUIDs. but rather the driver will take text as input and search for the relevant UUIDs to use. ( limited time to hack on this )

#./bundles/1.3.0-dev/binary/docker-1.3.0-dev hosts create -d openstack

–openstack-image-id=”d4f62660-3f03-45b7-9f63-165814fea55e” \

–openstack-auth-endpoint=”http://X.X.X.X:5000/v2.0” \

–openstack-floating-net=”4a3beafb-2ecf-42ca-8de3-232e0d137931″ \

–openstack-username=”admin” \

–openstack-password=”danger” \

–openstack-tenant-id=”daad3fe7f60e42ea9a4e881c7343daef” \

–openstack-keypair=”keypair1″ \

–openstack-region-name=”regionOne” \

–openstack-net-id=”1664ddb9-8a14-48cd-9bee-a3d4f2fe16a0″ \

–openstack-flavor=”2″ \

-openstack-nameserver=”10.254.66.23″ \

–openstack-secgroup-id=“e3eb2dc6-4e67-4421-bce2-7d97e3fda356” \

openstack-dockerhost-1

The result you will see is: (with maybe some errors if your security groups are already setup)

#[2014-11-03T08:31:51.524125904-08:00] [info] Creating server.
#[2014-11-03T08:32:16.698970304-08:00] [info] Server created successfully.
#%!(EXTRA string=63323227-1c1e-40f6-9c25-78196010936b)[2014-11-03T08:32:17.888292214-08:00] [info] Created Floating Ip
#[2014-11-03T08:32:18.439105902-08:00] [info] “openstack-dockerhost-1” has been created and is now the active host. Docker commands #will now run against that host
If you specified a ubuntu image (I just downloaded the Ubuntu Cloud Img from here)The Docker Daemon will get deployed in a Ubuntu Server in openstack and the Daemon will start on port 2375
Screen Shot 2014-11-03 at 11.36.10 AM
The Instance will look somthing like this in the dashboard of openstack.
Screen Shot 2014-11-03 at 11.36.27 AM
Here is the floating ip association with the Daemon Host.
Screen Shot 2014-11-03 at 11.36.44 AM
And it’s fully functional, see some other Docker Hosts commands below. Here is a video of the deployment (https://www.youtube.com/watch?v=aBG3uL8g124)
(View Hosts)
./bundles/1.3.0-dev/binary/docker-1.3.0-dev hosts
Screen Shot 2014-11-03 at 3.17.30 PM
You can make either the local unix socket or the openstack node the active Daemon and you can use it like any other docker client. This “hosts” command can run locally on your laptop but your containers and daemon run in OpenStack.  One could see this feature replacing something like Boot2Docker.
(docker ps) – Shows containers running in your openstack deployed docker daemon
./bundles/1.3.0-dev/binary/docker-1.3.0-dev ps -a
Screen Shot 2014-11-19 at 10.15.30 AM
(View Remote Info)
./bundles/1.3.0-dev/binary/docker-1.3.0-dev info
Screen Shot 2014-11-03 at 3.18.48 PM
Thanks for the Docker community for putting these events together! Pretty cool! Happy Monday and Happy Dockering.
P.S. I also used a Packer/VirtualBox Setup for DevStack in the begining. Here is the Packer Config and the Preseed.cfg. Just download devstack and run it on there.
{
 "variables": {
 "ssh_name": "yourname",
 "ssh_pass": "password",
 "hostname": "packer-ubuntu-1204"
 },

 "builders": [{
 "type": "virtualbox-iso",
 "guest_os_type": "Ubuntu_64",

 "vboxmanage": [
 ["modifyvm", "{{.Name}}", "--vram", "32"],
 ["modifyvm", "{{.Name}}", "--memory", "2048"],
 ["modifyvm", "{{.Name}}","--natpf1", "web,tcp,,8080,,80"],
 ["modifyvm", "{{.Name}}","--natpf1", "fivethousand,tcp,,5000,,5000"],
 ["modifyvm", "{{.Name}}","--natpf1", "ninesixninesix,tcp,,9696,,9696"],
 ["modifyvm", "{{.Name}}","--natpf1", "eightsevensevenfour,tcp,,8774,,8774"],
 ["modifyvm", "{{.Name}}","--natpf1", "threefivethreefiveseven,tcp,,35357,,35357"]
 ],

 "disk_size" : 10000,

 "iso_url": "http://releases.ubuntu.com/precise/ubuntu-12.04.4-server-amd64.iso",
 "iso_checksum": "e83adb9af4ec0a039e6a5c6e145a34de",
 "iso_checksum_type": "md5",

 "http_directory" : "ubuntu_64",
 "http_port_min" : 9001,
 "http_port_max" : 9001,

 "ssh_username": "{{user `ssh_name`}}",
 "ssh_password": "{{user `ssh_pass`}}",
 "ssh_wait_timeout": "20m",

 "shutdown_command": "echo {{user `ssh_pass`}} | sudo -S shutdown -P now",

 "boot_command" : [
 "<esc><esc><enter><wait>",
 "/install/vmlinuz noapic ",
 "preseed/url=http://{{ .HTTPIP }}:{{ .HTTPPort }}/preseed.cfg ",
 "debian-installer=en_US auto locale=en_US kbd-chooser/method=us ",
 "hostname={{user `hostname`}} ",
 "fb=false debconf/frontend=noninteractive ",
 "keyboard-configuration/modelcode=SKIP keyboard-configuration/layout=USA ",
 "keyboard-configuration/variant=USA console-setup/ask_detect=false ",
 "initrd=/install/initrd.gz -- <enter>"
 ]
 }]
}


(Preseed.cfg Starts HERE)
# Some inspiration:
# * https://github.com/chrisroberts/vagrant-boxes/blob/master/definitions/precise-64/preseed.cfg
# * https://github.com/cal/vagrant-ubuntu-precise-64/blob/master/preseed.cfg

# English plx
d-i debian-installer/language string en
d-i debian-installer/locale string en_US.UTF-8
d-i localechooser/preferred-locale string en_US.UTF-8
d-i localechooser/supported-locales en_US.UTF-8

# Including keyboards
d-i console-setup/ask_detect boolean false
d-i keyboard-configuration/layout select USA
d-i keyboard-configuration/variant select USA
d-i keyboard-configuration/modelcode string pc105


# Just roll with it
d-i netcfg/get_hostname string this-host
d-i netcfg/get_domain string this-host

d-i time/zone string UTC
d-i clock-setup/utc-auto boolean true
d-i clock-setup/utc boolean true


# Choices: Dialog, Readline, Gnome, Kde, Editor, Noninteractive
d-i debconf debconf/frontend select Noninteractive

d-i pkgsel/install-language-support boolean false
tasksel tasksel/first multiselect standard, ubuntu-server


# Stuck between a rock and a HDD place
d-i partman-auto/method string lvm
d-i partman-lvm/confirm boolean true
d-i partman-lvm/device_remove_lvm boolean true
d-i partman-auto/choose_recipe select atomic

d-i partman/confirm_write_new_label boolean true
d-i partman/confirm_nooverwrite boolean true
d-i partman/choose_partition select finish
d-i partman/confirm boolean true

# Write the changes to disks and configure LVM?
d-i partman-lvm/confirm boolean true
d-i partman-lvm/confirm_nooverwrite boolean true
d-i partman-auto-lvm/guided_size string max

# No proxy, plx
d-i mirror/http/proxy string

# Default user, change
d-i passwd/user-fullname string yourname
d-i passwd/username string yourname
d-i passwd/user-password password password
d-i passwd/user-password-again password password
d-i user-setup/encrypt-home boolean false
d-i user-setup/allow-password-weak boolean true

# No language support packages.
d-i pkgsel/install-language-support boolean false

# Individual additional packages to install
d-i pkgsel/include string build-essential ssh

#For the update
d-i pkgsel/update-policy select none

# Whether to upgrade packages after debootstrap.
# Allowed values: none, safe-upgrade, full-upgrade
d-i pkgsel/upgrade select safe-upgrade

# Go grub, go!
d-i grub-installer/only_debian boolean true

d-i finish-install/reboot_in_progress note

Nicira (VMWare) NVP/NSX: A Python API and Toolkit

Python

Python Programming Language

vmware NSX by Nicira.

Over the past few years working at EMC we regularly use NVP/NSX in some of our lab environments and throughout the years this means that we have needed to upgrade, manipulate and overhaul our network architecture with NVP/NSX every so often. We have been using a Python library developed internally by myself and Patrick Mullaney with some help from Erik Smith in the early days, and I wanted to share some of its tooling.

(need to drop this in here)

*Disclaimer : By no means does EMC make any representation or take any obligation with regard to the use of this library, NVP or NSX in any way shape or form. This post is the thoughts of the author and the author alone. The examples and API calls have mainly been testing against NVP up to 3.2 before it became NSX. However, most calls should work against the NSX API except for any net-new api endpoints that have not been added. Another note is that this API is not fully featured, it is merely a tool that we use in the lab for what we need, it can be extended, expanded however you like.(update 4/14/15′ See Open-source section toward the bottom for more information ) I’ll be working on opensource this library and try and fill in some of the missing features as I go along. There are two main uses for this python library:

1. Manage NVP/NSX Infrastructure

Managing, automating, and orchestrating the setup of NVP/NSX components was a must for us. We wanted to be able to spin up and down environments on the flow, and or manage upgrading new components when we wanted to upgrade.

The Library allows you to remotely setup Hypervisor Nodes, Gateway Nodes, Service Nodes etc. (Examples Below)

2. Python bindings to NVP/NSX REST API

 Having python bindings for some of the investigative projects we have been working on was out first motivation. A) because we developed and were familiar with Python, B) We have been working with OpenStack and it just made sense.

With the library you can list networking, attach ports, query nodes etc. (Examples are given in the test.py example below.)


Manage NVP/NSX Infrastructure Let me preface this by saying this isn’t a complete M&O / DevOps / Baremetal Provisioning service for NVP or NSX components, it does however manage setting up much of the logical state components with as little hands-on cli commands as possible,  like setting up OVS certificates, creating integrations bridges and contacting the Control Cluster to register the node as a Hypervisor Node, Service Node etc. Openvswitch is the only thing that does need to be installed, and any other nicira debs that come with their software switch/ hypervisor node offering. You just need to install the debs and then let the configuration tool take over.  (I am running this on Ubuntu 14.04 in this demo)

sudo dpkg --purge openvswitch-pki 
sudo dpkg -i openvswitch-datapath-dkms_1.11.0*.deb 
sudo dpkg -i openvswitch-common_1.11.0*.deb openvswitch-switch_1.11.0*.deb 
sudo dpkg -i nicira-ovs-hypervisor-node_1.11.0*.deb

Once OVS is installed on the nodes that you will be adding to your NVP/NSX architecture you can use the library and cli tools to setup, connect, and register the virtual network components. The way this is done is by describing the infrastructure components in JSON form from a single control host, or even a NVP/NSX linux host. We thought about using YAML here, but after everything we chose JSON. Sorry if you a YAML fan. The first thing needed by the tooling is some basic control cluster information. The IP of the controller, the username and password along with the port to use for authentication. (sorry for the ugly blacked out IPs) ss2-1 Next, you can describe different types of nodes for setup and configuration. These nodes can be:

SERVICE, GATEWAY, or COMPUTE

The difference with the SERVICE or GATEWAY node is that it will have a:

"mgmt_rendezvous_server" : true|false 
"mgmt_rendezvous_client" : true|false

respectively instead of a “data network interface. Service or Gateway nodes need this metadata in it’s configuration JSON to be created correctly. I dont have examples of using this (Sorry) and am focusing on using this for Hypervisor Nodes, since this is what we find we are creating/reconfiguring most. Here is an example of such a config. ss5 Once this configuration is complete, you can now reference the “name” of the compute node, and it will provision the COMPUTE node to the NVP/NSX system/cluster. If the hypervisor node is remote the toolkit will use remote sudo SSH, so you will need to enter a user/pass when you get prompted. ss3 Screen Shot 2014-10-10 at 2.17.05 PM As you can see, the command runs through all the process needed to setup the node, and at the end of it, you should have a working hypervisor node ready to go. (Scroll down, you can verify it’s setup by looking at the UI) Here is what it looks like when you do it remotely. loginpass You’ll then see similar output to the below, running through the sequence of setting up the hypervisor node remotely, keys, OVS calls, and contacting the NVP/NSX cluster to register the node using the PKI.

Sending… rm -f /etc/openvswitch/vswitchd.cacert
Sending… mkdir -p /etc/openvswitch
Sending… ovs-pki init –force
Sending… ovs-pki req+sign ovsclient controller –force
Sending… ovs-vsctl — –bootstrap set-ssl /etc/openvswitch/ovsclient-privkey.pem /etc/openvswitch/ovsclient-cert.pem /etc/openvswitch/vswitchd.cacert
Sending… cat /etc/openvswitch/ovsclient-cert.pem
printing status
0
[sudo] password for labadmin:
Certificate:
    Data:
        Version: 1 (0x0)
        Serial Number: 12 (0xc)
    Signature Algorithm: md5WithRSAEncryption
        Issuer: C=US, ST=CA, O=Open vSwitch, OU=controllerca, CN=OVS controllerca CA Certificate (2014 Oct 10 10:56:26)
        Validity
            Not Before: Oct 10 18:15:57 2014 GMT
            Not After : Oct  7 18:15:57 2024 GMT
        Subject: C=US, ST=CA, O=Open vSwitch, OU=Open vSwitch certifier, CN=ovsclient id:4caa4f75-f7b5-4c23-8275-9e2aa5b43221
        Subject Public Key Info:
            Public Key Algorithm: rsaEncryption
                Public-Key: (2048 bit)
                Modulus:
                    00:c2:b9:7d:9f:1d:00:78:4b:c0:0d:8a:52:d5:61:
12:02:d3:01:7d:ea:f6:22:d4:7d:af:6f:c1:91:40:
                    7f:e1:a1:a1:3d:2d:3f:38:f6:37:f6:83:85:3c:62:
                    b4:cc:60:40:d9:d3:61:8d:26:96:94:47:57:2d:fa:
                    53:ce:48:84:4c:a2:01:84:d8:11:61:de:50:f9:b5:
                    ff:9c:4b:6e:9c:df:84:48:f1:44:ec:e0:fd:e4:a1:
                    b6:0b:5c:23:59:5c:1d:cf:46:44:19:14:1c:92:a1:
                    28:52:19:ab:b8:5e:23:17:7a:b8:51:af:bc:48:1c:
                    d2:d8:58:67:61:a3:e6:51:f5:a0:57:a9:16:36:e8:
16:35:f6:20:3c:51:f8:4c:82:51:74:8b:48:90:e4:
                    dc:7a:44:f2:2b:d7:68:81:f6:9e:df:15:14:80:27:
                    77:e1:24:36:ac:fd:79:c2:03:64:1a:c0:4a:5b:b7:
                    dd:d3:fb:ca:20:13:f7:09:9e:03:f8:b0:fe:14:e7:
                    c9:7e:aa:1d:79:c3:c1:c2:a6:c3:68:cf:ff:ec:a4:
                    9a:5f:d4:f4:df:c2:e6:1d:a2:63:68:f2:d1:1d:00:
                    19:18:18:93:72:37:9e:b9:a4:2b:23:fd:83:ab:40:
                    52:0d:2e:9c:08:82:50:c0:1b:ec:e9:40:fc:1d:74:
                    1d:f3
                Exponent: 65537 (0x10001)
    Signature Algorithm: md5WithRSAEncryption
         85:fe:4b:97:77:ce:82:32:67:fc:74:12:1e:d4:a5:80:a3:71:
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         94:26:94:e3
—–BEGIN CERTIFICATE—–
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Sending… ovs-vsctl set-manager ssl:10.*.*.*
Sending… ovs-vsctl br-set-external-id br-int bridge-id br-int
Sending… ovs-vsctl — –may-exist add-br br-int
Sending… ovs-vsctl — –may-exist add-br br-eth1
Sending… ovs-vsctl — –may-exist  add-port br-eth1 eth1

After setup you can verify that the node is setup by logging into it if your remote, and running the ovs-vsctl show command. This will show you all configuration that has been done to setup the hypervisor node. Managers and Controllers are setup and connected, and bridge interfaces created and ready to connect tunnels. Screen Shot 2014-10-10 at 2.17.16 PM We can also verify that the Hypervisor node is setup correctly by looking at the NSX/NVP Manager Dashboard. This shows that the Ubuntu Node is now connected, using the correct Transport Zone, and is up and ready to go. Screen Shot 2014-10-10 at 2.17.29 PM Thats the end of what I wanted to show as far as remote configuration of NVP/NSX components goes. We use this a lot when setting up our OpenStack environments and when we add or remove new Compute nodes that need to talk on the Neutron/Virtual Network. Again there are some tweaks and cleanups I need to address, but hopefully I can have this available on a public repo soon.

Python bindings to NVP/NSX REST API

Now I want to get into using the toolkit’s NVP/NSX Python API. This is a API written in python that addresses the REST API’s exposed from NVP/NSX. The main class of the API takes Service Libraries as arguments, and them calls the init method on them to instantiate their provided functions. For instance “ControlSevices” focuses on control cluster API calls rather than the logical virtual network components.

from nvp2 import NVPClient
from transport import Transport
from network_services import NetworkServices
from control_services import ControlServices
import logging

class NVPApi(Transport, NetworkServices, ControlServices)
    client = NVPClient()
    log = logging
    def __init__(self, debug=False, verbose=False):
        self.client.login()
        if verbose:
          self.log.basicConfig(level=self.log.INFO)
        if debug:
          self.log.basicConfig(level=self.log.DEBUG)
        Transport.__init__(self, self.client, self.log)
        NetworkServices.__init__(self, self.client, self.log)
        ControlServices.__init__(self, self.client, self.log)

Some example of how to instantiate the library class and use the provided service functions for NVP/NSX are below. This is not a full featured list, and more than less they are for NVP 3.2 and below. We are in the process of making sure it works across the board, so far NVP/NSX APIs have been pretty good at being backward compatible.

from api.nvp_api import NVPApi
#Instantiate an  API Object
#api = NVPApi()
api = NVPApi(debug=True)
#api = NVPApi(debug=True, verbose=True)
#print "Available Functions of the API"
# print dir(api)
# See an existing "Hypervisor Node"
print api.tnode_exists("My_Hypervisor_Node")
# Check for existing Transport Zones
print api.get_transport_zones()
# Check for existing Transport Zone
print api.check_transport_exists("My_Transport_Zone")
# Check Control Cluster Nodes
nodes = api.get_control_cluster()
for node in nodes:
print "\n"
  print node['display_name']
  for role in node['roles']:
     print "%s" % role['role']
     print "%s\n" % role['listen_addr']
print "\n"
# Check stats for interfaces on transport node
stats = api.get_interface_statistics("80d9cb27-432c-43dc-9a6b-15d4c45005ee",
   "breth2+")
print "Interface %s" % ("breth2")
for stat, val in stats.iteritems():
  print "%s -- %s" % (stat, val)
print "\n"

I hope you enjoyed reading through some of this, it’s really gone from a bunch of scripts no one can understand to something halfway useful. There are definitely better ways to do this, and by no means is this a great solution, it just one we had lying around that I still use from time to time. Managing the JSON state/descriptions about the nodes was the hardest part when multiple people start using this. We wound up managing the files with Puppet and also using Puppet for installing base openvswitch software for new NVP/NSX components on Ubuntu servers. Happy Friday, Happy Coding, Happy Halloween!

(Update): Open-source

I wanted to update the post with information about the library and how to access it. The great folks at EMC Code ( http://emccode.github.io/ ) have added this project to the #DevHigh5 Program and it is available on their site. Look at the right side and lick the DevHigh5 tag, and look for Nicira NVP/NSX Python under the projects.

Screen Shot 2015-04-14 at 9.21.44 PM

Information can be seen by hovering over the project.

Screen Shot 2015-04-14 at 9.22.03 PM

The code can be viewed on Github and on pypi and can be installed via pip install nvpnsxapi

Screen Shot 2015-04-14 at 9.44.59 PM

https://pypi.python.org/pypi/nvpnsxapi  Screen Shot 2015-04-14 at 9.54.46 PM

Enjoy 🙂

COSBench, Intel’s Cloud Object Storage Bench-marking tool and how to visual it’s data with matplotlib

 

 

 

 

 

 

 

 


 

I am probably missing a few images here, but you get the point, Object Storage is here to stay. It’s becoming more popular as workloads move to cloud based application architecture where HTTP dominates at scale more than ever. So comes the need to be able to run performance tests on our (pick your favorite) open-source object-storage implementation… or not, if your not into it.

The tool I want to talk about it Intel’s COSBench, or “Cloud Object Storage Bench” if you will, a Java based performance benchmarking tool for Object Storage Systems. I’ll also touch on a neat way to visualize the data output by COSBench itself. COSBench defines itself in part:

COSBench is a benchmarking tool to measure the performance of Cloud Object Storage services. Object storage is an emerging technology that is different from traditional file systems (e.g., NFS) or block device systems (e.g., iSCSI). Amazon S3 and Openstack* swift are well-known object storage solutions. (https://github.com/intel-cloud/cosbench)

It allows you to run tests at scale using “Driver Nodes” and “Controller Nodes”. A Driver Node is the node that does the heavy lifting and generates the load that the test will be producing. A Controller Node collects metrics, orchestrates the jobs and keep track of which tests are running on which Drivers etc. Essentially the M&O/Dashboard. Read more about the specifics in the User Guide on Github (UserGuide). I wont go into detail about how to install in the post, I will just say the guide is pretty straight forward, I ran a multi-driver installation on top of OpenStack IceHouse to test Ceph (S3 and Swift Interfaces on the Rados Gateway) and Amazon S3 Directly.

What I will go into a bit is how to define a job, below is an example of how to setup a test for a Rados Gateway Swift endpoint using Ceph. As you can see below, I used a token from OpenStack using the keystoneclient, and an endpoint ending  in /swift/v1 in the “Storage” directive of the COSBench XML file. This small test will run a 100% READ test on 240 Objects in 12 different swift containers. This is what the “container=(#,#) and Objects=(#,#) denote. These objects will be in size Ranges of 25MB, meaning 25MB, 75MB, 175MB…etc.

screenshot1

After submitting the test you will see output in the Controller dashboard that looks like the below image. To get to this data yourself, click on the the “view details” next to the finished job, then click on “view details” next to the Stage ID of w<#>-s<#>-main with the name “main“. You can then click on the “view timeline status” underneath the General Report to get the below timeline data.

Screen Shot

This will give you a breakdown (I believe of every 5 seconds) of the performance metrics collected. The way the metrics are collected and how they are computed is explained in the User Guide (referenced above). If you click the “export CSV file” you can download the CSV version of the output for analysis. Which should look like the below excel sheet:

Screen Shot

Now to the fun part, with this data we can do some fun and interesting things with a python graphing library called matplotlib. Using this we can extract the data we want, like bandwidth, latency or throughput and draw graphs to better visualize our data. I have a few scripts that can be used to do this, made specifically to take input from a COSBench CSV file. Just start the script and pass it the CSV file. (more info on the Github page) https://github.com/wallnerryan/matplotlib-utils-cosbench

Run something like:

#cd matplotlib-utils-cosbench/
#python graph_data_bandwidth_bf.py <csv.file>

The output from the script will be a PNG graph image, the above command gives a graph of Bandwidth over Time in MB/s with a best fit line drawn through the graph. Go ahead, try it if you would like. The output will look something like the below image:

*(depending on your performance numbers, and relative hardware and environment, the graph may look very different)

constant-after-demo.

As a tip, in this case, my Y-Axis is capped at 250, because I know my data points did not go above 250MB/s, if they do, look at the lines around 68 in the source code, there is a message about how to change this. In this example it will look like the below image. (I had changed this one to be 250 based on the below code snippet)

Screen Shot

A note on the script, if you have a lot of data points the graphs can get junked up with data points being too close, there is a option to specify that you would only like the data points every X number of data points. (e.g it will read every 5th data point) Just pass in a separate argument in the form on an integer to the script at the end.

#python graph_data_bandwidth_bf.py <csv.file> <number>

Well, I hope this was interesting for some, and if you have any questions of comments please feel free to comment here or on my github or send me and email. Until next time, cheers.

Linux Containers: Parallels, LXC, OpenVZ, Docker and More

The State of Containers

Why should we care?

Image

Background

What’s a container?

            A container (Linux Container) at its core is an allocation, portioning, and assignment of host (compute) resources such as CPU Shares, Network I/O, Bandwidth, Block I/O, and Memory (RAM) so that kernel level constructs may jail-off, isolate or “contain” these protected resources so that specific running services (processes) and namespaces may solely utilize them without interfering with the rest of the system. These processes could be lightweight Linux hosts based on a Linux image, multiple web severs and applications, a single subsystem like a database backend, to a single process such as ‘echo “Hello”’ with little to no overhead.

            Commonly known as “operating system-level virtualization” or “OS Virtual Environments” containers differ from hypervisor level virtualization. The main difference is that the container model eliminates the hypervisor layer, redundant OS kernels, binaries, and libraries needed to typically run workloads in a VM.

Hypervisor-based

Image

Some of the main business drivers and strategic reasons to use containers are:

  • Ability to easily run and accommodate legacy applications
  • Performance benefits of running on bare-metal, no overhead of hypervisor
  • Higher density and utilization for resources in the datacenter
  • Adoption for new technologies is accelerated, put in isolated secure containers
  • Reduce “shipping” pains; code is easily streamlined to customers, fast. 

Container-based

Image

            Containers have been around for over 15 years, so why is there an influx of attention for containers? As compute hardware architectures become more elastic, potent, and dense, it becomes possible to run many applications at scale while lowering TCO, eliminating the redundant Kernel and Guest OS code typically used in a hypervisor-based deployment. This is attractive enough but also has benefits such as eliminating performance penalties, increase visibility and decrease difficulty of debug and management.

 Image

image credit: Jerome Petazzoni from dotCloud)

http://www.socallinuxexpo.org/sites/default/files/presentations/Jerome-Scale11x%20LXC%20Talk.pdf

            Because containers share the host kernel, binaries and libraries, can be packed even denser than typical hypervisor environments can pack VM’s.

 Image

image credit: Jerome Petazzoni from dotCloud)

http://www.socallinuxexpo.org/sites/default/files/presentations/Jerome-Scale11x%20LXC%20Talk.pdf

Solutions and Products

            Companies such as RedHat, Sun, Canonical, IBM, HP, Docker and others have adapted or procured slightly different solutions to Linux Containers. Below is a brief overview of the different solutions that deal with containers and or operating system-level virtualization.

Container Solutions

  • LxC ( Linux Containers )

o   0.1.0 releases in 2008

o   Works with general vanilla Linux kernels off the shelf.

o   GNU GPLv2 License

o   Used as a “container engine” in Docker

o   Google App Engine utilizes an LXC-like technology

o   Parellels Virtouzzo utilizes LXC

o   Rackspace Cloud Databases utilize LXC

o   Heroku (Application Deployment Platform) utilize LXC

  • Docker

o   Developed by (formally dotCloud) Docker Inc.

o   Apache 2.0 License

o   Docker is really an orchestration solution built on top of the linux kernel, namespaces, cgroups, chroot, and file system constructs. Docker originally chose LXC as the “engine” but recently developed their own solution called “libcontainer

o   Solutions:

  • “Decker” – Modified version of the engine works with Cloud Foundry to deploy application workloads
  • Openshift
  • AWS Elastic Beanstalk Containers
  • Openstack Solum
  • Openstack Nova
  • OpenVZ

o   Supported by Parallels Inc. (back in 1999 as SWsoft became Parallels in 2004)

o   Share many of the same developers as LXC, but was developed earlier on, LXC is a derivation of OpenVZ for the mainline kernel.

o   GNU GPL v2 License

o   Runs on a patched Linux kernel (specific kernel) or 3.x with reduced feature set

o   Live Migration Abilities (check pointing) (CRIU “criu.org)

Rackspace Cloud Databases also utilize OpenVZ

  • Warden

o   Developed by Cloud Foundry as an orchestration layer to create application containers. Initially said working with LxC was “too troublesome”.

o   (Comparison) Warden and Docker both orchestrate containers controlling the subsystems like linux cgroups, namespaces and security.

  • Solaris Containers

o   A “non-linux” containerization mechanism. Differ from “true” linux systems of the mainline kernel

o   Utilizes “Zones” as a construct for partitioning system resources. Zones are an enhanced chroot mechanism that adds additional features like ones included in ZFS that allow snapshotting and cloning.

o   Zones are commonly compared to FreeBSD Jails

  • (Free) BSD Jails

o   Also “non-linux” containerization mechanism. Differ from “true” linux systems of the mainline kernel

o   Also an “enhanced chroot”-like mechanism where not only does it use chroot to segregate the file system but it also does the same for users, processes and networks.

  • Linux V-Server

o   GNU GPL v2

o   Patched kernel to enable os-level virtualization

o   Partitions of CPU, Memory, Network, Filesystem are called “Security Contexts” which uses a chroot-like mechanism

o   Utilizes CoW(Copy on Write) file systems to save storage space.

  • Workload Partitions

o   AIX Implementation that provides resource isolation like container technologies to in Linux

  • Parallels (SWsoft) Virtuozzo Containers

o   Originially developed by SWsoft, parallels utilizes linux namespaces and cgroups technologies in the kernel to provide isolation.

o   Virtuozzo Containers become OpenVZ, when then became LXC for mainline linux kernel.

  • HP-UX Containers

o   HP’s Unix variant of containers. Like AIX WPARS this is a container technology tailored toward Unix platforms.

  • WPARS

o   Developed by IBM, this container technology is aimed at the AIX (Unix) based server OS platform.

o   Provides os-level environment isolation like other container models do.

o   Live application mobility (migration)

  • iCore Virtual Accounts

o   A Free Windows XP container solution. Provides os-level isolated computing environments for XP

  • Sandboxie

o   Developed by Invincea for Windows XP

o   “Sandboxes”, like a container, are created for isolated environments.

Related tools and mechanisms

  • Sysjail

o   A userspace virtualization tool developed for Open BSD systems. Much like the FreeBSS “jail”

  • Chroot

o   Kernel level function that allows a program to run in a host system in its own root filesytem.

  • Cgroups

o   Developed in 2006, used initially by Google Search

o   Unified in linux kernel by 2013.

  • Namespaces

o   Construct that allows partitioning and isolation of different resources so that they are only available to the processes in the container. Namespaces are Network (NET), UTS(hostname), PROC(process id), MNT (mount), IPC and User (Security Seperation)

  • Libcontainer

o   Written in Go programming language and developed by dotCloud/Docker it is a native Go implementation of “lxc-like” control over cgroups and namespaces.

  • Libct

o   Container library developed by engineers at Parallels

  • LPARs (Logical Partitions)

o   (Not linux related, not part of the related container “hype”) but an LPAR is essentially a partitioned set of network, compute, security, storage that can run processes and virtual machine.The difference is LPAR’s need an OS image.

Note: Kernel Namespaces and Cgroups became the defacto standard for creating linux containers and is used by most of the companies who have containerized technology, LXC, Docker, ZeroVM, Parallels, etc. 2013 was the first year that a linux kernel supporting OpenVZ worked with no patches, this was an example of kernel unification and the communities have since seen a boom in container technologies.

Different Containerization Models

            The models in which containers and containerization are formed have somewhat of a common denominator. They all need a shared kernel, and in some way have all made adaptations to the linux kernel to provide constructs like “security contexts”, “jails”, “containers”,“sanboxes”, “zones”, “virtual environments” etc. At a low-level the model remains consistent, partitioning host resources into smaller isolated environments, but when we look at how they are delivered, that’s where we see the different usecase models emerge.

Low-level Model

“Jails or Zones” with Patches Linux Kernel

  • Proprietary based solutions usually based off a patched linux kernel. OpenVZ, Parallels and Unix bases solutions started this way. Once cgroups and namespace were adopted into the kernel, this became the common way to bring a containerized solution to market.

“Cgroups and Namespaces”

  • Defacto standard for create Linux-based Isolated OS-level containers.

High-level Container Orchestration and Delivery Models

            The common ways containers are consumed are through some orchestration mechanism, usually through a portal or tool. This tool then communicates to a service level and requirements (YAML, Package List , or Built Images) get forwarded to a backend container engine. Whether that is LXC, Docker, OpenVZ, or others is up to the provider.

 Image

IaaS

            In this model containers are consumed as VM’s are, they can be requested with such attributes like CPU, Network, and Storage options. From the consumer’s point of view, it looks exactly like a VM.

            A few examples of this model are Openstack and Docker itself. The “docker-way” uses a userspace daemon that takes CLI or RESTful requests from a client. The daemon, which sits on the compute resources, utilizes a “container engine” like libcontainer or LxC to build the isolated environment based on a certain type of Linux image (from Docker Registry or Glance) provided. A note here that Docker Registry [4] is a platform for uploading and storing pre-built docker-images specific to an application, say a Fedora Image with Apache installed and configured.

            Openstack takes advantage of this Docker model and provides ways for Nova to integrate with Docker via a single driver to provide IaaS to consumers. It also integrates with Openstack Heat.

PaaS

            This is one of the major “best-fit” usecases for containers. Containers offer the agility, consistency, and efficiency PaaS platforms need, containers can be spun up/down, changed in seconds. This lends itself useful to platforms like OpenShift , Heroku, Cloudfoundry, and Openstack Solum. Applications can be imported and recognized at the same time that containers provide easily customizable computing environments on the fly for different types of workloads. The consumer does not interact with the container in the model, but rather the provider takes advantage of the container technology itself.

SaaS

            Containers lend themselves very well to sharing software; containers can easily be used to provide a software service on demand. An example of this case is http://www.memcachedasaservice.com/ [1], which uses containers to provide memcache-based service inside a container to the consumer. The benefit here is that you can provide these services in a largely distributed and scalable while also allowing the provider to densely utilizes its resources.

Pros & Cons of different containerization models

Model Pros Cons
IaaS Fast, Dense, Bare Metal performance. Limited Options, No windows VM’s. Lacks some security features compared to VM’s. (This can be argued though)
PaaS Efficient, Flexible, Dense, Easy to Manage Limited to Linux
SaaS Flexible, Easy to Manage Limited to Linux

Note* Although this says “limited to linux containers” there has been some talk about getting container orchestration solutions to be able to talk commonly between lightweight virtualization solutions so that describing a container could be common and thus this could deploy containers to LNX / Windows solutions. Libct is one effort here to unify container solutions.

Type of apps and workloads, what model works best

(Top use cases for containers, PaaS seems to stick out at best-fit)

HPC Worklods

            Containers do not have the overhead of a hypervisor layer and because of this they gain the performance of the host it is running on. Thousands of containers can be spun up in and instant to run distributed operations with power and scale.

Public and Private Clouds

            Containers lend themselves well to cloud-based solutions because of the density, flexibility, and speed of containers. Openstack, Google Compute and Tutum are all using containers in this space.

PaaS & Manages Services

            Probably one of the best use cases for containers in the market today. Providing PaaS involves a lot of orchestration and flexibility of the underlying service, containers are a clear winner in this space. CloudFoundry, Openshift, AWS Elastic BeanStalk, and Openstack Solum are PaaS solutions based on containers.

SaaS, Application Deployment

            A close second to a best-fit model to PaaS, containers also lend themselves well to SaaS architectures as containers can provide isolated, customizable environments for different software services independent of the host it is running on. Memcache as a Service, and Rackspace Cloud Databases are good examples of this.

Development and Test/QA

            One of this initial usecases for containers was to allow developers the freedom of running unit tests, trying new code, and running experiments in an isolated manner. Containers today are still widely used for this purpose and some system have there CI system built together with container technology to run isolated test jobs on new code.

An aside on Containers in the real world

Openstack + Containers

Nova

            Since the Havana release Openstack Nova has supported (in some way) using docker containers as an alternative or side-by-side to VM’s. Originally the openstack driver delivered was directly to a host, but now in the IceHouse release, Openstack Heat does the driving while the container engine is setup and run inside of a cloud instance. the nova driver is now part of stackforge and will possibly try to rejoin the nova code base in Juno.

http://blog.docker.com/tag/openstack-2/

Solum

            Openstack Solum is a PaaS incubation project in openstack that is currently part of stackforge that uses docker in a similar way that OpenShift and CloudFoundry do to orchestrate applications. Containers are used in the background to this project to build specialized workloads for the consumer.

https://wiki.openstack.org/wiki/Solum

Trove

            The DBaaS (Database as a Service) Openstack project is also using containers to deliver multi-tenant databases on-demand within the Openstack architecture.

CloudFoundry + Containers

            Cloud foundry Platform as a Service utilizes both LXC, and Docker technology under the covers. CloudFoundry had originally chosen LXC and built a tool called “warden” on top of it to manage the containers because they didn’t like using LXC outright. Docker containers also have something called a Dockerfile, which in short is a list of actions to be taken on the containerized environment once it’s built, like package management and installation to the startup and management of services. Much like a DevOps tool, this can be very powerful. This was a driving factor for the adopted version of Docker call “Decker” which implements their Droplet Execution Agent’s API. CF now lets you deploy docker and lxc based containers (droplets) using CF’s tooling.

Openshift+ Containers

            Openshift (by Redhat) much like the CloudFoundry Droplet provides something called Gears in its PaaS offering. Gears are native containers built from cgroups and namespaces that run the workloads. Openshift recently [2] adopted the Docker technology to deploy gears. This allowed them to take advantage of Docker inside their Cartridge and Gear system. By using Docker Images with metadata as a Cartidge and using Docker Containers as Gears(containers) based on the Cartridge. Redhat chose the container model because they could “achieve a higher density of applications per host OS and enable those applications to be deployed much more quickly than with a traditional VM-based approach”.

AWS+ Containers

            Amazon Elastic Beanstalk allows developers to load their applications into AWS while providing them flexibility and management within the PaaS. Elastic Beanstalk recently [3] adopted Docker so that developers can package or “build” Docker images (templates for the application) and deploy them into AWS with support from Elastic Beanstalk.

Google + Containers

Not (google+) but rather google using linux containers. I dont have much detail on the implementation here, but i’ve heard Google uses linux containers both, originally for Google Search, and now in its cloud compute engine. If anyone has more detail here, please comment 🙂

Legacy Code Support

            Containers are also used to run legacy application within the datacenter. Even when hardware refresh occurs, containers can implement older libraries and images to provide legacy applications to run on modern hardware.

New Technology Adoption

            Containers also offer a solution to early adoption to software. Containers offer secure isolated environments that let developers run, test, and evaluate new applications and software.

State of Security and Containers

            For truly secure container the root user in container can be mapped to “nobody” user/group and when this user gets out of container it doesn’t not affect “root” user on the host because “nobody” have very few privileges. Therefore:

  • Root on the container is not Root on the host.

            Not all container technologies utilize this security model but do implement SELinux, GRSEC, and AppArmour, which help. Safely running the workloads as non-root users will be the best way to help distort the lines between the security of VM and Containers.

            Other attack surfaces for containers can be (in order from less likely to more likely) the linux kernel constructs like cgroups and namespaces themselves, to the client or daemon responsible for responding to requests for host resources, which could be API, Websocket, or Unix Socket. Designed correctly, and used correctly, containers can be a secure solution.

There is a lot more on the security topic, you can start (here) for a good introduction.

Thanks for Reading

Please feel free to correct the history or any fact that I may have looked over too quick or didnt get right, I’d be happy to change it and get it correct. Containers are gaining ground in todays cloud infrastructures and there is a lot of interesting things going on, so keep your eyes and ears open because I’m sure you will hear more about them in the coming years.

Something I didnt touch on in the post was how storage works with containers. There are many different options that have pros and cons, Whether container solutions are using aufs, btrfs, xfs, device mapper, copy on write mechanisms, etc the main point is that they work at the file layer, not the block layer. While you could export iscsi/FC volumes to the hosts and use something lik e –volumes-from in Docker for persistency this is outside the direct scope of how containers maintain a low profile on the host. If you want more info or another post on this I can certainly do so.

Also, In coming posts, I think I will try and get some technical tutorials and demos around the container subject, keep posted, I will most likely be using Docker or LxC directly to do the demos!

Resources

docker architecture

References

[1] http://www.memcachedasaservice.com/,
    http://www.slideshare.net/julienbarbier42/building-a-saas-using-docker
[2] https://www.openshift.com/blogs/the-future-of-openshift-and-docker-containers
[3] http://aws.amazon.com/about-aws/whats-new/2014/04/23/aws-elastic-beanstalk-adds-docker-support/
[4] https://registry.hub.docker.com/

Optimizing the Cloud: Nova/KVM

Sources:[“ http://www.slideshare.net/openstackindia/openstack-nova-and-kvm-optimisation”,” http://www.linux-kvm.org/page/KSM”,” http://pic.dhe.ibm.com/infocenter/lnxinfo/v3r0m0/index.jsp?topic=%2Fliaat%2Fliaattunkickoff.htm”]

Compute nodes represent a potential bottleneck in an OpenStack Cloud Environment, because the compute nodes run the VM’s and Applications, the workloads fall on the I/O within the Hypervisor. Everything from Local File system I/O, RAM Resources and CPU can all affect the efficiency of your cloud.

One thing to consider when provisioning physical machines is to look at what Guest/VM flavours you are going to allow to be deployed on that machine. Flavours that eat up 2VCPUs and 32G of RAM may not be well suited with a machine with only 64G of RAM and 8 CPU Cores

The notion of tenancy is also important to keep track of, tenant size and activity factors into how your environments resources are used. Tenants consume images, snapshots, volumes and disk space. Consider how many tenants will consume your cloud and adjust your resources accordingly. Make sure if you plan to take advantage of overprovisioning think about thin provisioning and potential performance hits.

Using KVM:

KVM is a well-supported hypervisor for Nova and has its own ways to increase performance. KVM isn’t the only Hypervisor to choose, Hyper-V, Xen, VMware can also be supported in Openstack, but KVM is a powerful competitor. Tuning your hypervisor is just as important as tuning your cloud resources and environment, so here are some things to consider:

  • I/O Scheduler: cfq vs deadline
  • Huge Pages
  • KSM (Kernel Same-page Merging)
    • A de-duplication feature, saves on memory usage. Helps scale vms/per hypervisor node (compute node)
  • Hyper-threading
  • Guest FS location (on hypervisor block devices)
  • Disable Zone Reclaim
  • Swappiness

Personally running Grizzly, we can see that KSM is working,

cat /sys/kernel/mm/ksm/pages_sharing

1099262

Running Ubuntu linux 3.2.00-23 kernel ns qemu-kvm-1.0. This should mean that running multiple VM’s should have better performance.

Puppet, Chef, Orchestration and DevOps:

The world of IT Systems Administration, DevOps and Orchestration of bare-metal resources to virtual applications is starting to have the need to become fully automated with custom hooks for different scale-out HPC workloads, cloud environments, to single deployments for SMBs. Automation via specialized scripts for network and compute need to become a thing of the past.

In steps:

I personally have had the opportunity to work with Foreman, Heat, Puppet, and (somewhat) Chef. The others also contain great ways to automate from bare-metal, all the way to fully virtual “stacks” of network, compute and storage.

Puppet and Chef share a space in the IT automation industry and both succeed in their vision. Whether you decide to use Puppet or Chef depends on your alignment and what you’re trying to accomplish. I’ve heard that Chef’s master node scales better but have personally never tested this theory. Both try to accomplish sub-version of package management and configuration control over a subset of nodes in an IT environment. Razor bare metal provisioning was developed as a venture between EMC and Puppetlabs and offers a path to full automation between the two, Not to say that any other DevOps tool or bare-metal provisioning workflows can’t be substituted, I may just be a bit bias.

Into the IT wormhole 

(brief notes)

Puppet

  • Client-server based. Puppetmaster and Puppet Clients. Declarative Language for “write once deploy many”.
  • Has open-source Openstack Packages for on-demand Openstack Delivery/Configuration Version control.
  • Integrates with Openstack Heat/TripleO for managing packages and configurations.
  • Deployment of monitoring tools, security tools all possible within private cloud.
  • Integrates well with Razor

Chef

  • Client-server based orchestration management “infrastructure as code” for deploying applications, version control, config files.
  • Written in Ruby. “Cookbooks” CB’s can be written to deploy Openstack “Chef for openstack” components, and potential to deploying security, monitoring etc.
  • Github.com/opscode/openstack-chef-repo (Grizzly, Nicira Plugin, KVM, LXC)
  • Ceilometer, Quantum Cookbooks (By Dreamhost)
  • NVP, OVS Cookbooks (By Nicira)
  • Chef agent for Arista switches, “kind of SDN”
  • Roles and recipes, Role could be “Allinone Devstack or Controller Node or Base Node

Juju

  • Like heat, JuJu deploys and manages services and application within a cloud provider. JuJu can deploy openstack components (e.g Glance) or deploy applications (e.g wordpress) on top of existing clouds.
  • Juju.ubuntu.org

To integrate with openstack you must specify these options:

openstack:
type: openstack_s3
control-bucket:  admin-secret:
auth-url: https://yourkeystoneurl:443/v2.0/
default-series: precise
juju-origin: ppa
ssl-hostname-verification: True
default-image-id: bb636e4f-79d7-4d6b-b13b-c7d53419fd5a
default-instance-type: m1.small

Heat

  • Heat is an orchestration tool for managing “stacks” or applications deployed on the cloud. Heat can orchestrate ports, routers, instances, Floating IPs, Private Networks etc.
  • Packaging can also be installed via Heat templates to do things like “deploy a stack and make it a 4 node WordPress cluster.”
  • Provides OpenStack-like CLI and Database show, list, create methods for interactions.

TripleO

  • Dynamic “Cloud on Cloud” version control of your cloud.
  • Need a “seed” cloud stack to provision 2 HA Nova Bare Metal (Ironic) servers, these bare metal stacks will provision a “overcloud” via Heat, the bare metal servers will know about available nodes via node enrollment via MAC Address.
  • Integrates well with Puppet/Chef for Package Management/Configuration if you did not want to use Heat.
  • Comes with a set of tools, os-apply-config, os-refresh-config, diskimage-builder.
    • diskimage-builder is used to build custom images with a notion of “elements”, these elements can be anything from a service, a application, a database, etc (e.g Glance, MySQL) and you can add them to the image you build. Quit a useful tool by itself actually.
    • You can build a base ubuntu qcow image that works with Openstack and Glance (Grizzly) by using the command:

 disk-image-create vm base -o base -a i386

Razor

  • Specialized microkernel used to PXE boot with that checks in with Razor to provide inventory of the system, user-created policies will apply a configuration to the node
  • Able to and off to DevOps (Chef, Puppet)

Pxe_dust

  • Complete solution for pxe booting, not really a package mgmt. or config solution.
  • Chef has pxe_dust recipe, AFAIK is interoperable with Chef.

Crowbar

  • Hardware provisioning and application mgmt. (by Dell/SUSE)
  • Crowbar.github.com
  • Features
    • server discovery (crowbar_machines –U crowbar –P crowbar list)
    • firmware upgrades
    • operating system installation via PXE Boot.
    • application deployment via Chef. (e.g. openstack)

Cobbler

Cobbler is a Linux installation server that allows for rapid setup of network installation environments. It glues together and automates many associated Linux tasks so you do not have to hop between many various commands and applications when deploying new systems, and, in some cases, changing existing ones. Cobbler can help with provisioning, managing DNS and DHCP, package updates, power management, configuration management orchestration, and much more. With a simple series of commands, network installs can be configured for PXE, reinstallation, media-based net-installs, and virtualized installs (supporting Xen, qemu, KVM, and some variants of VMware). Cobbler uses a helper program called ‘koan’ (which interacts with Cobbler) for reinstallation and virtualization support.

Foreman

Through deep integration with configuration management, DHCP, DNS, TFTP, and PXE-based unattended installations, Foreman manages every stage of the lifecycle of your physical or virtual servers. The Foreman provides comprehensive, auditable interaction facilities including a web frontend and robust, RESTful API

  • Theforman.org
  • Foreman has tight integration with Puppetlabs as well, Foreman integrates puppet manifests directory into it Web UI which makes for a nice management dashboard for provisioning applications.(see below)

xcat

xCAT’s purpose is to enable you to manage large numbers of servers used for any type of technical computing (HPC clusters, clouds, render farms, web farms, online gaming infrastructure, financial services, datacenters, etc.). xCAT is known for its exceptional scaling, for its wide variety of supported hardware, operating systems, and virtualization platforms, and for its complete day 0 setup capabilities.

  • Allows for a stateless boot (boot off/download RAMDisk image of xcat management node) with available scratch disk for persistent data on reboot. Satalite files (NFS mounted filesystem ) allows for other reboot/persistence. Though, in both cases, no stateful information should be allowed on either.
  • Developed by IBM, Power and Z Support.