3D Interior Design System Model Based on Computer Virtual Reality Technology
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Abstract
Globally, data volume is increases exponentially with increase in the proliferation with Cloud Computing. MapReduce is emerged as the prominent solution for the unprecedented growth in the efficient manner as it process both structured and unstructured data. The dynamic landscape of Virtual Reality has seen a significant shift towards technology-driven approaches, with data analytics and personalized learning becoming increasingly important. This paper introduces an innovative framework that leverages the power of Hadoop and MapReduce to elevate 3D virtual reality experiences within diverse VR Cloud settings. This paper presents the development of an efficient Cache-Based MapReduce framework (CMF) where Cache algorithms are effectively used to process queries on large-scale cloud-based data. The Hadoop System processes data in single-node Hadoop Clusters (Pseudo-distributed) as well as heterogeneous Hadoop Clusters (fully distributed nodes) within Amazon Web Services (AWS). The Hadoop System process the data in the single node Hadoop Cluster (Pseudo-distributed) heterogeneous Hadoop Cluster (fully distributed node) in the Amazon Web Services (AWS). The experimental analysis is evaluated for the SmallGutenberg and LargeGutenberg database. The developed model achieves the average reduction in job of 48.01% with reduction in execution time of 51.99%. The CMF of 7-node, 9-node, 15-node and 20-node reduction in execution time is measured as 49.91%, 51.38%, 54.71% and 45.29% respectively.
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