Recent Readings

The cloud is great. Stop the hype. – This is an excellent article on what cloud computing is and isn’t and when the use of the cloud is the correct technical or architectural choice. I had a long post planned on the overloaded term “cloud computing” but OmniTI covers all the important points in this article. Like any new approach to infrastructure deployment that promises quick provisioning of services, people often forget that all of that infrastructure needs to be managed. There are a lot of good tools coming out to help with that management but none make it zero cost.

Dissecting Today’s Internet Traffic Spikes – With the above article on cloud computing and this article on the sudden nature of internet traffic spikes, I’m becoming an OmniTI fanboy.  Part of my job is to worry about designing and provisioning correctly for sudden changes in traffic patterns, and Theo is correct that you have to design for spikes, not react to spikes.

Kanban For Sysadmins – I’ve started doing Kanban at work for one of our Operations teams and have been really pleased with the results so far — so much so that we’re rolling it out for another team this week and hopefully the rest of the department over the next few weeks. We track our work in Request Tracker, but it is hard to know 1. what is being worked on right now, and 2. how much throughput a team has. Kanban lets us know both, and it also lets us avoid the entire topic of prioritization of future work. We only prioritize when we are ready to start doing new work. I’ll post a follow-up to this once we’re further along in our Kanban experiment.

Hello From A libc-free World! – Have you ever wondered what, exactly, your “Hello, world!” program does? Jessica at Ksplice dives into what happens when you build a super-simple C program (it’s more complicated than you think!).

Data-Intensive Text Processing With MapReduce – A freely available draft in PDF of an upcoming book on using MapReduce to process large text datasets. One of the cool things we’ve done at ITA is add tracking data to each and every request that passes throughout our reservation system, and we output this tracking information in each log entry in every component we’ve written. The structure of this tracking data is such that if you aggregate the logs from all of the components you can easily construct a graph of the request’s path through the reservation system (including the asynchronous calls). The problem now is searching all of that log data, and I’ve been curious about MapReduce as it applies to this sort of data mining.

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