Determining Application Performance Profiles in the Cloud

I want to know how to characterize my workloads in the cloud. With that, I should be able to find systems both over-provisioned and resource starved to aid in right-sizing and capacity planning. CloudForms by Red Hat can do these at the system level, which is where you would most likely take any actions, but I want to see if there’s any additional value in understanding at the aggregate level. cpuWe’ll work backwards for the impatient. I found 7 unique workload types by creating clusters of cpu, mem, disk, and network use through k-means of the short-term data from CloudForms (see the RGB/Gray graph nearby).  The cluster numbers are arbitrary, but ordered by median cpu usage from least to most.

From left to right, rough characterizations of the clusters are:

  1. idle
  2. light use, memory driven
  3. light use, cpu driven
  4. moderate use
  5. moderate-high everything
  6. high cpu, moderate mem, high disk
  7. cpu bound, very high memory
    Continue reading “Determining Application Performance Profiles in the Cloud”

Palin’s Email Network

Gov Palin's Email Network (click for larger version)

Lots of cleanup left to do in the code parsing/cleaning up the emails, but here’s a first pass.  Seems like at least two connected networks, and surprisingly both the yahoo and the Gov’t email addresses are both in the larger one.  I wonder what the smaller one comprises of?

A very big thanks to the folks over at Sunlight Foundation for the data.

Health Care Leans Republican

3.6-times as many former congressional staffers turned health care lobbyists and their immediate connections have network ties closer to former President Bush, than to current President Obama.


The connections in the network map shown below, and used for the analysis above, include people and organizations (e.g. corporate, not-for-profit, public, etc.) the people have been identified with.

Other trivia: Continue reading “Health Care Leans Republican”

Mathematicians Do It Randomly

What it look like if you took all of the Mathematics articles from JSTOR, the digital journal archive, and mapped co-authorship of the papers? It would look something like this.  Interesting to note, that while the distribution does hold to the small world network distribution exponent, there’s some “peakiness” about it that may suggest it’s not really one network, but the merging of several.  Given the role of mathematics on so many other subjects, that would not be a surprise.

JSTOR Mathematics Authors
Largest cluster of co-authorship

Zoomable image with names, after the jump.

Continue reading “Mathematicians Do It Randomly”

Foreign Lobbying of NY Congressmen

Thanks to ProPublica and Sunlight Foundation:

…for the first time digitized one year’s worth of FARA records, making them accessible in a searchable database that allows users to easily follow the money and connect the dots. With the Foreign Lobbying Influence Tracker , anyone can quickly learn what governments are lobbying whom, how often and about what. [source @ ProPublica]

Here are the firms the Congressmen and -women from my home state have been meeting with:

Foreign Lobbying of NY Congress
Foreign Lobbying of NY Congress

and the countries of the governmental department or foreign firm paying the lobbyists:

NY Congress Lobbying by Country
NY Congress Lobbying by Country

Health Care Lobbyists Part Deux

Thanks everyone for showing the strong interest in the Lobbyist map.  I got a couple nice mentions at Mother Jones and, but more importantly, I’ve added in all of the other names in the map.

Circles are people, squares are organizations, and white circles are the lobbyists in question.

If you’d rather the image than the flash bits, here you go, all 2.5MB of it.

A zoomable version of the earlier map is here:

[Thanks to Drew Conway for the Sea Dragon zoomable suggestion]

Best Networked Healthcare Lobbyists? [updated]

The Huffington Post, along with public contributors, has been collecting a list of former Congressional staffers turned healthcare lobbyists. has been keeping track of these former staffers, and thanks to their API, we now have a social graph of their relationships.

Former staffers in white (with names), and the rest of the visual field to show that some are MUCH better networked than others.

If there’s interest, I can add the names of the people they are networked with and start some analysis of the group.

HCIU Congressional Staffers Turned Healthcare Lobbyists
HCIU Congressional Staffers Turned Healthcare Lobbyists

As always, click for a larger image.

Update: network map with all names, and in a zoomable widget here.

Healthcare and the Senate Finance Committee

Late last month, the NY Times had an article about the debate over healthcare legislation taking place in the Senate Finance Committee. Coincidentally, around that time, the folks over at LittleSis, the “free database detailing the connections between powerful people and organizations,” were kind enough to give me early access to their API (thanks Kevin and Matthew!).

So from NY Times:

To LittleSis:


Of the named members in the photo, neither Tom Barthold nor Phil Ellis existed at the time in the LittleSis database, but it’s still showing a pretty networked bunch.

I’d like to see someone do this one better, and include donors.

Election Influence by 527’s: Browsable Map

I wanted to put out what’s been done so far on making yesterday’s post more interactive. There’s an awful lot that could be better about this map. Particularly legibility of labels in the core (it’s just too dense). If you want to see names, I suggest looking at the edges of the map.

Michael Bommarito is looking into better layouts for legibility. And while you are waiting, I suggest getting your fill of everything he’s ever written.

The data was collected from

[21-Apr-2009: You should see a flash image above, but am having an awful time getting this to render on a Mac.  Works great on Linux (Red Hat Enterprise Linux).]