A Multi Criteria Recommendation Engine Model for Cloud Renderfarm Services

Abstract

Cloud services that provide a complete platform for rendering the animation files using the resources in the cloud are known as cloud renderfarm services. This work proposes a multi criteria recommendation engine model for recommending these Cloud renderfarm services to animators. The services are recommended based on the functional requirements of the animation file to be rendered like the rendering software, plug-in required etc and the non functional Quality of Service (QoS) requirements like render node cost, time taken to upload animation files etc. The proposed recommendation engine model uses a domain specific ontology of renderfarm services to identify the right services that could satisfy the functional requirements of the user and ranks the identified services using the popular Multi Criteria Decision Analysis method like Simple Additive Weighting (SAW). The ranked list of services is provided as recommended services to the animators in the ranking order. The Recommendation model was tested to rank and recommend the cloud renderfarm services in multi criteria requirements by assigning different QoS criteria weight for each scenario. The ranking based recommendations were generated for six different scenarios and the results were analyzed. The results show that the services recommended for each scenario were different and were highly dependent on the weights assigned to each criterion.

Ranking cloud render farm services for a multi criteria recommender system

“…The work of Tran et al [24] on the QoS ontology and its QoS-based ranking algorithm for Web services was an important contribution that led to further research on ranking the cloud services for selection by many researchers like Garg et al [25] on SMI attributes based cloud services ranking, Wang et al [26] work on cloud model for service selection etc. Inspired by these work, we have worked on the problem of classifying, ranking and recommending cloud renderfarm services [27][28][29][30]. During these works we felt the need for an ontology specific to the cloud renderfarm services and that inspired this work on ontology and similarity reasoning for identifying the right cloud services.…”