Animation rendering on multimedia fog computing platforms

Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom

Volumn 0, Issue , 2016, Pages 336-343

  Hong, Hua Jun ^a   Chuang, Jo Chi ^a   Hsu, Cheng Hsin ^a  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

Author keywords

animation rendering; Edge computing; fog computing; prediction; volunteer computing

Indexed keywords

ANIMATION; DISTRIBUTED DATABASE SYSTEMS; EDGE COMPUTING; FOG; FORECASTING; LEARNING ALGORITHMS; MACHINE LEARNING; MULTIMEDIA SERVICES; MULTIMEDIA SYSTEMS; SIMULATION PLATFORM; WEB SERVICES;

COMPUTING PLATFORM; DISTRIBUTED MULTIMEDIA; MULTI-FOLD CROSS VALIDATION; PREDICTION ACCURACY; PREDICTION ALGORITHMS; STATISTICAL MODELING; TRACE DRIVEN SIMULATION; VOLUNTEER COMPUTING;

FOG COMPUTING;

EID: 85012980528     PISSN: 23302194     EISSN: 23302186     Source Type: Conference Proceeding    
DOI: 10.1109/CloudCom.2016.0060     Document Type: Conference Paper

References (30)

1. Bitbrains. http://www.bitbrains.nl/solvinity-en
2. Disney rendered its new animated film on a 55,000-core supercomputer. http://www.engadget.com/2014/10/18/disney-bighero-6/
3. Renderstorm. http://www.render-storm.com/index.html
4. Scikit-learn. http://scikit-learn.org
5. Shaderlight. http://limitlesscomputing.com/Shaderlight
6. XGBoost. https://github.com/dmlc/xgboost
7. Zync. https://www.zyncrender.com/
8. New technology revs up Pixars Cars 2. http://www.cnet.com/news/new-technology-revs-up-pixarscars-2/, June 2011
9. D. Anderson. Boinc: A system for public-resource computing, and storage. In Proc. of IEEE/ACM Grid Computing (GC), pages 4-10, Pittsburgh, PA, November 2004
10. D. Anderson, J. Cobb, E. Korpela, M. Lebofsky, and D. Werthimer. SETI@home: An experiment in public-resource computing. ACM Transactions on Communications, 45(11): 56-61, November 2002
11. O. Babaoglu, M. Marzolla, and M. Tamburini. Design, and implementation of a P2P cloud system. In Proc. of ACM Symposium on Applied Computing (SAC), pages 412-417, Trento, Italy, March 2012
12. F. Bonomi, R. Milito, J. Zhu, and S. Addepalli. Fog computing, and its role in the internet of things. In Proc. of ACM Workshop on Mobile Cloud Computing (MCC), pages 13-16, Helsinki, Finland, August 2012
13. P. Brucker, and S. Knust. Complex Scheduling. Springer, 2012
14. T. Estrada, M. Taufer, and K. Reed. Modeling job lifespan delays in volunteer computing projects. In Proc. of IEEE/ACM International Symposium on Cluster Computing, and the Grid (CCGRID), pages 31-338, Shanghai, China, May 2009
15. J. Friedman, R. Tibshirani, and T. Hastie. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2001
16. K. Graffi, D. Stingl, C. Gross, H. Nguyen, A. Kovacevic, and R. Steinmetz. Towards a P2P cloud: Reliable resource reservations in unreliable P2P systems. In Proc. of IEEE International Conference on Parallel, and Distributed Systems (ICPADS), pages 27-34, Shanghai, China, April 2010
17. T. Lin, T. Lin, C. Hsu, and C. King. Context-aware decision engine for mobile cloud offloading. In Proc. of IEEE Wireless Communications, and Networking Conference Workshops (WCNCW), pages 111-116, Shanghai, China, April 2013
18. Y. Liu, Y. Guo, and C. Liang. A survey on peer-to-peer video streaming systems. Peer-to-peer Networking, and Applications, 1(1): 18-29, March 2008
19. J. Pouwelse, P. Garbacki, D. Epema, and H. Sips. The bittorrent P2P file-sharing system: Measurement, and analysis. In Proc. of International Workshop on Peer-to-Peer Systems (IPTPS), pages 205-216, Ithaca, NY, February 2005
20. F. Sarmenta. Volunteer computing. Technical report, Massachusetts Institute of Technology, 2001
21. M. Satyanarayanan, P. Bahl, R. Cceres, and N. Davies. The case for VM-based Cloudlets in mobile computing. IEEE Transactions on Pervasive Computing, 8(4): 14-24, December 2009
22. J. Schmidhuber. Deep learning in neural networks: An overview. Neural Networks, 61: 85-117, January 2015
23. B. Scholkopf, and A. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2001
24. S. Shen, V. Beek, and A. Iosup. Statistical characterization of business-critical workloads hosted in cloud datacenters. In Proc. of IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid), pages 465-474, Shenzhen, China, May 2015
25. O. Sonmez, N. Yigitbasi, A. Iosup, and D. Epema. Tracebased evaluation of job runtime, and queue wait time predictions in grids. In Proc. of ACM International Symposium on High Performance Distributed Computing (HPDC), pages 111-120, Munich, Germany, June 2009
26. M. Tao, S. Dong, and L. Zhang. A multi-strategy collaborative prediction model for the runtime of online tasks in computing Cluster/Grid. Cluster Computing, 14(2): 199-210, June 2011
27. L. Vaquero, and L. Merino. Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM Computer Communication Review, 44(5): 27-32, October 2014
28. T. Verbelen, S. Pieter, T. Filip, and D. Bart. Cloudlets: Bringing the cloud to the mobile user. In Proc. of ACM Workshop on Mobile Cloud Computing, and Services (MCS), pages 29-36, Low Wood Bay, UK, June 2012
29. D. Willis, A. Dasgupta, and S. Banerjee. Paradrop: a multitenant platform for dynamically installed third party services on home gateways. In Proc. of ACM SIGCOMM Workshop on Distributed Cloud Computing (DCC), pages 43-44, Chicago, IL, Augest 2014
30. K. Xu, M. Song, X. Zhang, and J. Song. A cloud computing platform based on P2P. In Proc. of IEEE International Symposium on IT in Medicine, and Education (ITIME), pages 427-432, Jinan, China, Augest 2009