Saturday, July 5, 2008

Yet an another EC2 Experience - Cybernet Slash Support runs Payroll on EC2

Introduction
The objective of this exercise is to determine the effectiveness of utility computing over enterprise method of computation. To ascertain the facts on a real-time scenario, we decided to try out a payroll system as it involves complex and time consuming processes.

We were amazed by the outcome of this exercise as our Payroll system thrived on EC2, and in a nutshell the initial finding is that this is a right step towards reducing cost /time and improving productivity. We are pleased with the outcome and would like to take this opportunity to congratulate Amazon for this great service. Infact, we are pretty confident that based on our testimony of this great service from Amazon, many more enterprises would contemplate and eventually move their payroll system to Amazon web services environment.

Background
Our payroll system takes about 5 to 10 seconds to calculate a pay check per employee and it takes approximately 6 hours for 4500 employees. Our current infrastructure has two dedicated servers to process the payroll and we could achieve each payroll cycle in about 3 hours.

Our challenges were to,

1. Reduce the processing time
2. Optimize the usage of available resources
3. To come up with an approach to withstand the increasing head counts.

Let’s take a look at our approach towards solving the above listed problems.

Our Approach
We looked at various cloud computing offerings and finally decided on AWS. This is mainly because of its cost effectiveness and composite service offerings like EC2, S3 and Simple Message Queue. This unique infrastructure provides flexibility for us to store and operate independently.

Our payroll software is well architected based on distributed computing. We could easily plug out the Payroll engine and make our first AMI. The core components of this AMI are Fedora, Java 1.5 and the Payroll engine. We have also created a My-Sql AMI to host our payroll database.

On the other hand, the transfer of entire database to S3 may pose problems due to its volume. So we decided to extract the necessary information using Kettle (It’s an amazing tool for data extraction needs) and upload to S3.

We launched four different instances of our Payroll AMI and one instance of My-Sql AMI, importing the database from S3. The db server was running with a binary log option switched on. The “payroll engine” configurations were modified in accordance with the My-Sql database and launched the payroll execution. The entire payroll process is completed in about 2 hours, which includes delivering the pay slip PDF to our employees email box.

Upon completion of the payroll process, we took a differential backup on the payroll database and stored it in S3. The differential database is pushed back to the original payroll database using kettle again.

Conclusion
It was a great experience running our payroll on AWS. This is not only because of the fact that it enabled us to reduce the processing time considerably (from 5 hours to approximately 1 hour), but we have also got a scalable solution to withstand our increasing head count day by day.

As we discussed, AWS has rich set of offerings for utility computing like EC2, S3, Simple Message Queue...etc. This would definitely change the enterprise computing to a new dimension soon and our payroll exercise is a classic beginning for that.

The biggest advantage of using EC2 is ‘Power Costs associated with air conditioning in India’ A 200 square feet data center consumes close to $1500 in power costs each month. We plan to embrace Amazon’s cloud computing for several applications that are not constrained by latencies of (250 to 250 ms) on the cloud.

18 comments:

Anonymous said...

Good stuff. Thanks for sharing.

Botchagalupe said...

Great stuff... However, I am trying to better understand the ROI here. Initially you say the payroll takes 6 hours to complete.. then you say..

"two dedicated servers to process the payroll and we could achieve each payroll cycle in about 3 hours."

Later you say it takes two hours then you end with this statement...

"enabled us to reduce the processing time considerably (from 5 hours to approximately 1 hour)"

I am not trying to be a jerk but I just trying to understand the numbers. Here are a few questions I have:

1) What was the original configuration of the dedicated servers (e.g., one MySQL and one Payroll server)?

2) When you went to 4 Payroll servers did how did you achieve the parallel processing effect (splitting the data up, SQS, ...)?

3) Was their any latency uploading and downloading the S3 data?

TIA

CSS Labs said...

If you read the blog carefully, 2 hours is for completing the entier process including delivery of our pay check PDF file.

I mentioned abour one hour time period for completing the payroll.

The point here is not about the numbers, its about building scalable solution using cloud computing.

To answer to your questions,

Our local setup has clustered database servers used for various purposes not only for keeping payroll related information.

We have two dedicated servers for processing our payroll.

As i mentioned in my post, our payroll engine has been built for distributed computing. I believe it uses some messaging infrastrucutre to share their state. We have used only EC2 and S3for running our payroll.

We found some latency issues uplaoding directly from india to the AWS S3 servers. But we managed with the help of our US servers.

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