Last month, I attended O’Reilly’s Velocity Web Performance and Operations Conference and, without fail, the information I came away with reinforced existing VMIX initiatives and strategies, and inspired new ones.

Attending the Velocity Conference, I had three goals:

• To listen to relevant industry-leading speakers, regarding how to improve performance and operational efficiency, taking this information and disseminating it to my colleagues who couldn’t attend.
• To meet and discuss with peers from companies facing the same challenges and hurdles when it comes to performance and operations. Understanding their approaches and pitfalls while offering my own experiences.
• And, finally, being away from interruptions at work, to simply be inspired by the incredibly smart people around me to draft new strategies for increasing scale, efficiency and performance to bring back and implement.

It’s All About the Data

At Velocity this year, as well as the previous two, I became aware of underlying themes that went beyond specific technologies or tools.

The most important, which in some cases may seem obvious, is to make decisions based on data.

A lot of times, decisions are made based on theory, instinct, wishful thinking and other motivating factors.

Validating these factors with data collected from your applications, infrastructure and clients will allow you to make the most informed decision. The more data points you collect, the higher the odds are that your decisions will result in the kind of outcomes you think they should.

One important method of data collection that is overlooked at times is the human factor. No matter how much our hardware computing evolves, humans still decide which methods or strategies to implement in order to provide a complete solution. And the human brain is still the highest performing computer on the market. Going to a conference and interacting with peers and colleagues on operations and web performance allows us to complete the system.

Learning and Implementing

As a result of attending the Velocity Conferences over the past three years, we have been able to implement the following technologies and optimizations, which have greatly impacted our efficiency at VMIX.

Velocity 2008

I met Brian Moon and Alan Kasindorf (dormando) and, after discussing file-system optimization, proceeded to implement some file-system tuning which, in turn, helped increase our Apache Web Servers running mod_disk_cache efficiency by 3 times.

Velocity 2009

After several BOF (Birds of a Feather) sessions, and speaking with various attendees, I was convinced to give Puppet (Configuration Management Framework) a try, along with Cobbler for rapid Linux installs. We’d previously used CFengine and Standalone Kickstart and felt they were not necessarily the right tools for our operation. After proof of concept (POC), we deployed Puppet and Cobbler and are now able to build and control systems at a quicker rate, with more management and less complication.

Velocity 2010

I attended a talk by Leif Hedstrom on Apache Traffic Server and realized it was open sourced from Yahoo to the Apache Organization. I had known about the Traffic Server back in the Inktomi days (1990’s), as well as from a company I worked for that licensed the code. However, I hadn’t realized it was openly available. After hearing that Yahoo uses the Traffic Server for their own CDN network, I decided to give it a shot and plan on using it to possibly replacing our Squid servers at VMIX.

Currently, we are running a bit of a bake-off between Squid and Apache Traffic Server, and preliminary results for Traffic Server are so favorable that, by next month, Squid might be calamari.

In quick closing, hopefully O’Reilly and the Velocity Program Committee keeps up the good work and my list of things learned and implemented grows!

Phil Chen is Director of Systems Engineering at VMIX.

The following shows Version 4 IP addressing consisting of 32-bit binary number of four octets (dotted decimal notation)

Classful Addresses

Class A (24 Bit) 1-126.x.x.x
Class B (20 Bit) 128-191.x.x.x
Class C (16 Bit) 192-223.x.x.x
Class D Multicast 224-239.x.x.x
Internal Loopback 127

Private IP Address Ranges

Class A (24 Bit) 10.0.0.0-10.255.255.255 CIDR Mask /8 Decimal Mask 255.0.0.0
Class B (20 Bit) 172.16.0.0-172.31.255.255 CIDR Mask /12 Decimal Mask 255.240.0.0
Class C (16 Bit) 192.168.0.0-192.168.255.255 CIDR Mask /16 Decimal Mask 255.255.0.0

/24 IP Bit Mask

Dotted Decimal Mask: 255.255.255.0
Effective Hosts: 254
Effective Subnets: 1
Usable IP's: 254

/25 IP Bit Mask

Dotted Decimal Mask: 255.255.255.128
Effective Hosts: 252
Effective Subnets: 2
Usable IP's: 126

/26 IP Bit Mask

Dotted Decimal Mask: 255.255.255.192
Effective Hosts: 248
Effective Subnets: 4
Usable IP's: 62

/27 IP Bit Mask

Dotted Decimal Mask: 255.255.255.224
Effective Hosts: 240
Effective Subnets: 8
Usable IP's: 30

/28 IP Bit Mask

Dotted Decimal Mask: 255.255.255.240
Effective Hosts: 224
Effective Subnets: 16
Usable IP's: 14

/29 IP Bit Mask

Dotted Decimal Mask: 255.255.255.248
Effective Hosts: 192
Effective Subnets: 32
Usable IP's: 6

/30 IP Bit Mask

Dotted Decimal Mask: 255.255.255.252
Effective Hosts: 128
Effective Subnets: 64
Usable IP's: 2