MAMSaaS: Mashup Architecture with Modeling and Simulation as a ServicePublic Deposited
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In recent years, developing Modeling and Simulation (M&S) applications has become more and more complex. New technologies, like Web Services (WS) and Cloud computing, have been recently used in Modeling and Simulation (M&S). However, developing M&S applications using these technologies is still a complicated process. The reasons for this include: 1) it is hard to develop web services for varied M&S resources; 2) it is complicated to deploy M&S resources in the Cloud; 3) it is hard to integrate with varied M&S services; 4) it is complex to identify and select resources and services based on their meaning. In this research, we aim to simplify the development and integration of M&S applications using web technologies by solving the issues mentioned above. To do so, we propose the Mashup Architecture with Modeling and Simulation as a Service (termed MAMSaaS). MAMSaaS is a layered and lightweight M&S application development approach. It has five layers, which are Cloud, Box, Wiring, Mashup, and Tag Ontology Layers. It has a simplified life cycle to develop, deploy, identify, select, integrate and execute varied M&S resources as services in the Cloud. In the Cloud Layer, we developed CloudRISE middleware to expose RESTful Modeling and Simulation as a Service (MSaaS) for varied M&S resources; in addition, we propose new methods using Cloud computing and Experimental Framework concept to simplify the deployment of experiment environment. In the Box, Wiring and Mashup Layers, we present a new method based on mashup technologies to simplify the integration, execution and visualization of M&S applications. In the Tag Ontology Layer, we propose a new semantic selection approach using tag-mining and ontology-learning technologies, to identify and select M&S resources based on their meanings.
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- Copyright © 2016 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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