%0 Conference Proceedings
%T A Statistics Based Prediction Method for Rendering Application
%+ Xi'an Jiaotong University (Xjtu)
%A Li, Qian
%A Wu, Weiguo
%A Xu, Long
%A Huang, Jianhang
%A Feng, Mingxia
%Z Part 3: Scheduling and Load-Balancing
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 13th IFIP International Conference on Network and Parallel Computing (NPC)
%C Xi'an, China
%Y Guang R. Gao
%Y Depei Qian
%Y Xinbo Gao
%Y Barbara Chapman
%Y Wenguang Chen
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-9966
%P 73-84
%8 2016-10-28
%D 2016
%R 10.1007/978-3-319-47099-3_6
%K Rendering
%K Prediction
%K Statistics
%K Classification
%K Render farm
%Z Computer Science [cs]Conference papers
%X As an interesting commercial application, rendering plays an important role in the field of animation and movie production. Generally, render farm is used to rendering mass images concurrently according to the independence among frames. How to scheduling and manage various rendering jobs efficiently is a significant issue for render farm. Therefore, the prediction of rendering time for frames is relevant for scheduling, which offers the reference and basis for scheduling method. In this paper a statistics based prediction method is addressed. Initially, appropriate parameters which affect the rendering time are extracted and analyzed according to parsing blend formatted files which offers a general description for synthetic scene. Then, the sample data are gathered by open source software Blender and J48 classification algorithm is used for predicting rendering time. The experimental results show that the proposed method improve the prediction accuracy about 60 % and 75.74 % for training set and test set, which provides reasonable basis for scheduling jobs efficiently and saving rendering cost.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-01647989/document
%2 https://inria.hal.science/hal-01647989/file/432484_1_En_6_Chapter.pdf
%L hal-01647989
%U https://inria.hal.science/hal-01647989
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-9966