FutureGrid Project Challenge: A CLOUD COMPUTING ARCHITECTURE FOR SUPPLY CHAIN NETWORK SIMULATION
Abstract
Large-scale supply chains usually consist of thousands of stock keep units (SKUs) stocked at different locations within the supply chain. The simulation of large-scale multi-echelon supply chain networks is very time consuming. The purpose of this project is to design a cloud computing architecture to facilitate the computational performance of large scale supply chain network simulations. A Cloud Computing Architecture For Supply Chain Network Simulation (CCAFSCNS) will be designed in this project, and a prototype system will be developed using the computing resources in the FutureGrid. The simulations are essentially compute-intensive Monte-Carlo experiments requiring multiple replications. Replications are distributed across virtual machines within CCAFSCNS. One expected result is to show that the cloud computing solution can significantly shorten the simulation time.
Intellectual Merit
This research will develop a Cloud Computing Architecture For Supply Chain Network Simulation (CCAFSCNS). This architecture will provide a blueprint to develop cloud computing solutions. In addition, this project will provide a way to compare the time to simulate on the local computer and the time spent on cloud computing solution. The impacts of the large scale data will also be considered to simulate large scale supply chain networks.
Broader Impact
A Cloud Computing Architecture For Supply Chain Network Simulation (CCAFSCNS) is developed in this project and can be used as a blueprint to develop cloud computing solutions. A prototype system that implements the CCAFSCNS will be developed to simulate the supply chain networks in the cloud. Companies will be able to use the results to develop better systems and software products that rely on cloud computing for applications.
Use of FutureGrid
The Condor technology is used in this project to manage the computing resources. The VMs of Condor Server, Condor Worker and Condor Client are created based on the Grid Appliance. The Nimbus cloud client is used to start up Condor Worker VMs in the Alamo Network, and these VMs serve as the computing resources for this project. A Java-based web service, which is deployed in the Condor Client, will be available to receive the input file from the user through a web page. After receiving the input file from the user, the web service will generate the Condor job description file based on the input file. The web service sends the input file and the Condor job description file to the Condor Server, and the Condor Server schedules these jobs to be run on the Condor Worker VMs in the Alamo Network. When a job is completed on the Condor Worker, the simulation result will be sent back to the web service. After all the jobs are completed, the web service will package the generated files and notify the user through email.
Scale Of Use
Fifteen VMs will be needed for short durations (around 6 hours) in order to establish feasibility of prototype and be available for testing.
Publications
Results
Large-scale supply chains usually consist of thousands of stock keep units (SKUs) stocked at different locations within the supply chain. The simulation of large-scale multi-echelon supply chain networks is very time consuming. The purpose of this project is to design a cloud computing architecture to facilitate the computational performance of large scale supply chain network simulations. A Cloud Computing Architecture For Supply Chain Network Simulation (CCAFSCNS) was designed in this project, and a prototype system was developed using the computing resources in the FutureGrid. The simulations are essentially compute-intensive Monte-Carlo experiments requiring multiple replications. Replications are distributed across virtual machines within CCAFSCNS. The results show that the cloud computing solution can significantly shorten the simulation time.
Resources used in this project:
- Virtual Machine: Grid Appliance
- Hardware Systems: Alamo Network
- Service Access: Nimbus Cloud Client
Completed Work:
- Customized the Grid Appliance to be Condor Server, Condor Worker and Condor Client.
- Designed a Cloud Computing Architecture For Supply Chain Network Simulation (CCAFSCNS).
- Developed a prototype system that implemented the CCAFSCNS with Excel, Access, Spring Framework, supply chain network simulator, FutureGrid, the Condor System, and the Grid Appliance. The virtual machines (VMs) of the Condor Worker, which is customized based on the Grid Appliance, are started in the Alamo network. These VMs are the computing resources used in the prototype system to run simulation jobs.
- Analyzed the impacts of large scale data on the prototype system
- Did a computational time study on the cloud computing solution based on FutureGird:
- Analyzed the time components used in the cloud computing solution
- Estimated the scheduling time for a simulation request
- Compared the simulation time spent on traditional solution and cloud computing solution and showed that the cloud computing solution can save 70% of the simulation time
Achievements/Publications:
- One Master project report has been submitted to fulfill the requirement for the degree of Master of Science.
- One conference paper has been submitted to the 2012 Winter Simulation Conference.
Broader Impacts:
A Cloud Computing application capable of evaluating the performance of multi-echelon supply networks through simulation is developed in this project. This application includes a web application that can run the simulation from the cloud and a database application that helps users develop the input data and analyze the output data. Companies will be able to use the results to develop better systems and software products that rely on cloud computing for applications involving this use case. In addition, the cloud computing architecture designed in this project can be used to develop other cloud computing solutions. Also, educational materials, such as the tutorials of building the Condor System, are developed to provide how-to knowledge for other researchers and industry collaborators.
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