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'Going Spatial' is my personal blog, the views on this site are entirely my own and should in no way be attributed to anyone else or as the opinion of any organisation.

My tweets on GIS, Humanitarian, Tech, Games and Randomness

Thursday 24 June 2010

Amazon Cloud is Ubuntu cloud - now shall we set up our own private cloud for GIS?

It was with some interest that I discovered that Amazon Web Services (AWS) was built on top of Ubuntu. I thought it was using XenServer or something but no, it is Ubuntu.

The details of their enterprise cloud offering is here.

What is of great interest is the ability to create your own cloud from the same installation. Am very tempted as our work on AWS, while fruitful has met with some odd issues. Also, the AWS isn't cheap - there has been a lot of testing over the past month and every hour that we have the AMIs running, it is costing us. Yes, the large AMIs may only cost $0.48 per hour but it soon adds up over time especially when one is only testing.

As part of our testing, we noticed that the AMIs were a bit sluggish in terms of CPU performance when compared to a standalone server. We decided to investigate this a bit further and discovered that Amazon rates their AMIs in Amazon EC2 Compute Units in order to provide everyone with a consistent measure of CPU capacity. Since Amazon purchase commodity servers all with a different rating, this approach makes perfect sense. However, how much horsepower is a since Amazon EC2 Compute Unit?

According to their website, it One EC2 Compute Unit provides the equivalent CPU capacity of a 1.0-1.2 GHz 2007 Opteron or 2007 Xeon processor.

Not one of the fastest is it? Over time, Amazon will add more processors but this unit will remain. Please note that these are also single-core.

Here's a selection of Instances that Amazon now make available to your CPU hungry GIS application. We've been using the following:

Small Instance

1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
32-bit platform
I/O Performance: Moderate
API name: m1.small
Cost: $0.12 per hour

Large Instance
7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
64-bit platform
I/O Performance: High
API name: m1.large
Cost: $0.48 per hour

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