The document presents a project on an Infectious Disease Surveillance Simulation (IDSS) conducted by Jorge Edison Lascano as part of the CS7930 class at Utah State University. It details the implementation and automated deployment of the system on AWS, including the use of vector timestamps to study asynchronous communication in a distributed environment. The results highlighted an effective handling of processes and the ability to partially order events within the system.

1. 3/25/2015 This is a project for the CS7930 Class
-Distributed Systems Development with Research
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An Infectious Disease
Surveillance Simulation
(IDSS) in the Cloud
Prepared by: Jorge Edison Lascano
edison_lascano@yahoo.com
PhD student at Utah State University
Associate Professor at Universidad de las Fuerzas Armadas ESPE-Ecuador
Presented in the Industry Day at Utah State University
Computer Science Department

2. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
Introduction
• Objective
– Understand the behavior of Vector Timestamps in a
Distributed System Deployed in the Cloud.
• Results
– Infectious Disease Surveillance System
• Implemented and tested
• Setup for automated deployment to the cloud
(AWS EC2 instances with S3 storage)
• Demonstrated effective use of Vector
Timestamps
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3. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
Agenda
• Project Overview
• Tasks Details
• Technology
• Outstanding Risks and Issues
• Demo (github available)
• Results
• Conclusion
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4. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
Project Overview
• Project description
– This project is divided in three phases:
• Implement a IDSS using communication
protocols like: UDP/TCP/HTTP
• Implement automated deployment that
– Starts up Virtual Machines in a cloud
– Deploys software to those VM’s
– Launches the IDSS
• Study the resulting Vector Timestamps
– This project allowed us to see the behavior of
asynchronous calls in a distributed environment
and to deploy and run automatically its components
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5. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
IDSS Diagram
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6. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
Vector Timestamp
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Source http://compquiz.blogspot.com/2010/12/logical-and-vector-timestamps-in.html

7. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
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Tasks Details
Ord. Task Description
1 Implement HDS A Health District System collects infectious disease
information from EMRs and keeps track of diseases,
also sends information to every DOA; DOA1: Influenza,
DOA2: Chicken pox; and, DOA3: Measles.
Saves its Vector Timestamp to the log file
2 Implement EMR The Electronical Medical Record Simulator sends
information about diseases periodically to its HDS
Saves its Vector Timestamp to the log file.
3 Implement DOA Every DOA keeps track of a disease rate, if a threshold
is reached, it sends a notification to every HDS.
Saves its Vector Timestamp to the log file.
4 Local Tests EMR – HDS – DOA are generating traffic and Vector
Timestamps locally

8. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
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Tasks Details
Ord. Task Description
5 Launching /
starting
Instances
A number of EC2 instances are launched, these
instances will host the EMRs, HDSs and DOAs
processes.
6 Name
Resolution
A shell script that registers the corresponding 9 EMRs, 3
HDs and 3 DOAs is implemented and updates the hosts
file in every EC2 instance. The number of launched
instances vary from 1 to 15. A simple Round Robin
algorithm is implement in case of less than 15 instances
were launched
7 Deployment A script that allows the update of changes and automatic
distribution of the system in the EC2 instances is created
8 Execution After the system was deployed, a script that runs every
process based on the name resolution will execute every
one of the 15 processes

9. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
Technology
• Framework: node.js
• IDE: Webstorm
• Cloud Provision: AWS: S3+EC2+EBS
• Languages
– Javascript (node.js)
– shell script (bash, awk, sed)
• Communication Protocols
– UDP, HTTP
– ssh, aws cli
• Partial Ordering Algorithm: Vector Timestamps
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10. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
Outstanding Risks and Issues
• Project Risks
– Phase 1: Implementation of the system
• Risk:
– New language, new IDE
• Solution:
– Tutorials
– Phase 2: Distribution in the Cloud
• Risk :
– Running out of money (I still can not find my money)
• Solution:
– Monitor, detach and shutdown every resource
• Project Issues
– Phase 1:
• Debugging
– Phase 2:
• Testing
• S3 sync will not update if the file size is the same
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11. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
Demo
• https://github.com/elascano/HW2VectorTimestamp_on_AWS
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EC2 instances
Vector Timestamps

12. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
Results
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• A set of vector timestamps that can be used to
partially order the execution if the events.
• These vector timestamps allow to see which
are the processes that supports most of the
load in the whole system.
• A non-blocking / asynchronous system that
allows processing information from any client
at any point of time, the asynchronous feature
was easily implemented using node.js
• An automated process for deployment of
distributed systems.

13. 3/25/2015 [Infectious Disease Surveillance
Simulation in the Cloud]
Conclusion
• This project helped understand the order/
disorder of how the processes and their events
are executed based on the communication
model in a real distributed environment.
• If there is not direct or indirect connection
(communications) between process x and y,
then the y-th element of x's timestamp doesn't
get updated.
edison_lascano@yahoo.com
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