Cloud Render Farm Services Discovery Using NLP And Ontology Based Knowledge Graph
Ruby Annette, Aisha Banu, Sharon Priya, Subash Chandran

TL;DR
This paper introduces RenderSelect, an ontology-based engine that improves discovery of cost-effective cloud render farm services by semantically matching functional requirements, outperforming traditional search methods.
Contribution
It presents a novel ontology and reasoning algorithms for semantic service discovery tailored to cloud render farms, enhancing accuracy and efficiency.
Findings
Ontology-based discovery outperforms non-ontology methods
Semantic reasoning improves service matching accuracy
Engine effectively identifies suitable render farm services
Abstract
Cloud render farm services are the animation domain specific cloud services Platform-as-a-Service (PaaS) type of cloud services that provides a complete platform to render the animation files. However, identifying the render farm services that is cost effective and also matches the functional requirements that changes for almost every project like the animation software, plug-ins required etc., is a challenge. This research work proposes an ontology-based service discovery engine named RenderSelect for the cloud render farm services. The cloud render farm ontology semantically defines the relationship among the cloud render farm services. The knowledge-based reasoning algorithms namely, the Concept similarity reasoning, Equivalent reasoning and the Numerical similarity reasoning have been applied to determine the similarity among the cloud services. The service discovery engine was…
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Taxonomy
TopicsCloud Computing and Resource Management · Service-Oriented Architecture and Web Services
Methodstravel james · Ontology
