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Thursday
Oct212010

Machine VM + Cloud API - Rewriting the Cloud from Scratch

Write a little "Hello World" program these days and it runs inside a bewildering Russian Doll of nested environments, each layer adding its own special performance and complexity tax. First, a language executes in its own environment of data structure libraries, memory management, and so on. That, more often than not, will run inside a language VM like the JVM, CLR, or V8. The language VM will in-turn run inside a process that runs inside an OS. An application will run in one or more threads inside a process. And the whole thing will run inside a machine sharing VM layer like Xen. And across all of that are frameworks for monitoring, elasticity, storage, and so on. That's a lot of overhead for a such a little program.

What if we could remove all these taxes and run directly on the new bare metal, which some consider to be a combination of Machine VM + Cloud API? That's exactly what a system called Mirage, described in the paper Turning down the LAMP: Software Specialisation for the Cloud, sets out to do by treating the cloud virtual hardware as a compiler target, and converting high-level language source code directly into kernels that run on it.

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Tuesday
Oct192010

Sponsored Post: Playfish, Electronic Arts, Tagged, Undertone, Box.net, Wiredrive, Joyent, DeviantART, CloudSigma, ManageEngine, Site24x7

Who's Hiring?

Fun and Informative Events

  • Membase Meetups Coming to Major US Cities. The first of these technical meetups is on October 28 at Zynga’s San Francisco offices.

Cool Products and Services

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Monday
Oct182010

NoCAP

In this post i wanted to spend sometime on the CAP theorem and clarify some of the confusion that i often see when people associate CAP with scalability without fully understanding the implications that comes with it and the alternative approaches

You can read the full article here

Friday
Oct152010

Troubles with Sharding - What can we learn from the Foursquare Incident?

For everything given something seems to be taken. Caching is a great scalability solution, but caching also comes with problems. Sharding is a great scalability solution, but as Foursquare recently revealed in a post-mortem about their 17 hours of downtime, sharding also has problems. MongoDB, the database Foursquare uses, also contributed their post-mortem of what went wrong too.

Now that everyone has shared and resharded, what can we learn to help us skip these mistakes and quickly move on to a different set of mistakes?

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Thursday
Oct142010

I, Cloud

Do we need Three Laws of Cloud? Not yet. Neither should we be overly concerned regarding reports of cloud leading to the elimination of IT.

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Tuesday
Oct122010

The CIO’s Problem: Cloud “Mess” or Cloud “Mash”

Loved those mainframe days – you only needed one, but then came along the AS400’s and soon you had ten – but wait, you needed client server and SOA, oh sh%# – now I have ten thousand servers and I need to consolidate server and datacenter operations!

Is Cloud Computing going to follow the same path?

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Friday
Oct082010

4 Scalability Themes from Surgecon

Robert Haas in his SURGE Recap of the Surge conference, reflected a bit, and came up with an interesting checklist of general themes from what he was seeing. I'm directly quoting his post, so please see the post for a full discussion. He uses this framework to think about the larger picture and where PostgreSQL stands in its progression.

  1. Make use of the academic literature. Inventing your own way to do something is fine, but at least consider the possibility that someone smarter than you has thought about this problem before.
  2. Failures are inevitable, so plan for them.  Try to minimize the possibility of cascading failures, and plan in advance how you can operate in degraded mode if disaster (or the Slashdot effect) strikes.
  3. Disk technology matters. Drive firmware bugs are common and nightmarish, and you can expect very limited help from the manufacturer, especially if the drive is billed as consumer-grade rather than enterprise-grade. SSDs can save you a lot of money, both because a given number of dollars buys more IOs-per-second, and because electricity isn't free.
  4. Large data sets require horizontal scalability.  In the era of 1TB drives, "large" doesn't mean quite what it used to,  but even though the amount of data you can manage with one machine is growing all the time, the amount of data people want to manage is growing even faster.
Thursday
Oct072010

Hot Scalability Links For Oct 8, 2010

Tuesday
Oct052010

Sponsored Post: Box.net, Wiredrive, Joyent, DeviantART, CloudSigma, ManageEngine, Site24x7

Who's Hiring?

Cool Products and Services

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Monday
Oct042010

Paper: An Analysis of Linux Scalability to Many Cores  

An Analysis of Linux Scalability to Many Cores, by a number of MIT researchers, is a refreshingly practical paper on what it takes to scale Linux and common applications like Exim, memcached, Apache, PostgreSQL, gmake, Psearchy, and MapReduce to run on 48 core systems. A very timely paper given moderately massive multicore systems are reportedly the near future of computing.

This paper must have taken a lot of work. They both tracked down bottlenecks in a number of applications and the Linux kernel and they also tried to fix them. Modestly speaking the authors said they made "modest" changes to the kernel and applications, but there's nothing modest about what they did. It's excellent work.

After the next bit, which is the abstract, there is a list of the problems they found and how they fixed them.

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