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Ranking cloud render farm services for a multi criteria recommender system

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Abstract

Recommender systems that recommend ideal services or items to the online users are a very useful tool for both the users and the businesses. Usually for recommending services, multiple attributes of the services are evaluated and these types of recommender systems that evaluate multiple attributes are called multi criteria recommender systems. In these types of multi criteria recommender systems the ranking of services plays a major role. This work is focused on ranking the cloud render farm services which are of the PaaS (Platform-as-a-Service) type of cloud services that provide the entire platform for the animators to render the files using the cloud resources. This work identifies the Quality of Service (QoS) attributes that are important for the animators for selecting a cloud render farm service. The QoS values of four different real time cloud render farm services were collected by conducting real time experiments using the files of the “Big Buck Bunny”, an open-source animated film project and were ranked using three Multi-Criteria Decision Making (MCDM) methods, namely the AHP (Analytical Hierarchical Process), TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) and SAW (Simple Additive Weighting). The analysis of the ranks obtained using the three different MCDM methods provide many useful insights and conclusions.

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References

  1. Liu F, Tong J, Mao J, Bohn R, Messina J, Badger L and Leaf D 2011 NIST Cloud Computing Reference Architecture. NIST Special Publication. 500: 292

    Google Scholar 

  2. Armbrust M, Fox A, Griffith R, Joseph A D, Katz R, Konwinski A and Lee G 2010 A view of cloud computing. Commun. ACM 53: 50–58

    Article  Google Scholar 

  3. Buyya R, Yeo C, Venugopal S, Broberg J and Brandic I 2009 Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25: 99–616

    Article  Google Scholar 

  4. Cusumano M 2010 Cloud computing and SaaS as new computing platforms. Commun. ACM 53: 27–29

    Article  Google Scholar 

  5. Kennedy J and Healy P EP2538328 A1 [Patent] A method of provisioning a Cloud-based render farm

  6. Wilkiewicz J J and Hermes D J US20140094302 A1 [Patent] Cloud-based multi-player game play video rendering and encoding

  7. Madhavan K P C, Arns L L and Bertoline G R 2005 A distributed rendering environment for teaching animation and scientific visualization. Comput. Gr. Appl. 25: 32–38

    Article  Google Scholar 

  8. Wang S H, Li X Z and Zhang L 2013 The rendering system planning of the 3d fashion design and store display based on cloud computing. Appl. Mech. Mater. 263: 3

    Google Scholar 

  9. Crockett T W 1997 An introduction to parallel rendering. Parallel Comput. 23: 819–843

    Article  Google Scholar 

  10. Chalmers A, Davis T, Kato K and Reinhard E 2001 Practical parallel processing for today’s rendering challenges. SIGGRAPH Course Notes 40: 32–38

    Google Scholar 

  11. Toshi A C T A D, Reinhard K E and Antonio S 2002 Practical Parallel Rendering 53: 27–29

    Google Scholar 

  12. Yao J, Pan Z and Zhang H 2009 A Distributed Render Farm System for Animation Production”, Entertainment Computing–ICEC. Berlin: Springer, pp. 264–269

    Google Scholar 

  13. Hong Z, Wang Y and Shi M 2011 SPN model-based performance of job scheduling plans for distributed rendering environment. In: International Conference on Multimedia Technology (IEEE), pp. 27–29

  14. Gooding S L, Arns L, Smith P and Tillotson J 2006 Implementation of a distributed rendering environment for the TeraGrid. In: Challenges of Large Applications in Distributed Environments (IEEE), pp. 13–22

  15. Gonzalez-Morcillo C, Weiss G, Vallejo D, Jimenez-Linares and Castro-Schez J J 2010 A multiagent architecture for 3D rendering optimization. Appl. Artif. Intell. 24: 313–349

    Article  Google Scholar 

  16. Tal D 2013 Rendering in SketchUp: from modeling to presentation for architecture, landscape architecture, and interior design. Hoboken: Wiley, pp. 41–43

    Google Scholar 

  17. Siti Rashidah and Misrohim 2010 Implementation of Network Rendering Farm. Project Report, UTeM, pp. 52, 53

  18. Chong A, Sourin A and Levinski K 2006 Grid-based computer animation rendering. In: Proceedings of the 4th international conference on Computer graphics and interactive techniques, GRAPHITE ’06. ACM

  19. Glez-Morcillo C, Vallejo D, Albusac J, Jiménez L and Castro-Schez J J 2011 A new approach to grid computing for distributed rendering. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), IEEE.

  20. Patoli M Z, Gkion M, Al-Barakati A, Zhang W, Newbury P and White M 2009 An open source grid based render farm for blender 3d, Power Systems Conference and Exposition. PSCE’09. IEEE/PES

  21. Garg S K, Versteeg S and Buyya R 2011 SMICloud: a framework for comparing and ranking cloud services. In: Fourth IEEE International Conference on Utility and Cloud Computing (UCC), pp. 210–218

  22. Wang S, Zheng Z, Sun Q, Zou H and Yang F 2011 Cloud model for service selection. In: Computer Communications Workshops (INFOCOM WKSHPS), IEEE, pp. 666–671

  23. Hussain F K and Hussain O K 2011 Towards multi-criteria cloud service selection. In: Proceedings of the 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 44–48

  24. Saaty T L 1990 How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res. 48: 9–26

    Article  Google Scholar 

  25. Tran V Xuan, Tsuji H and Masuda R 2009 A new QoS ontology and its QoS-based ranking algorithm for Web services. Simul. Model. Pract. Theory 17: 1378–1398

    Article  Google Scholar 

  26. Afshari A, Mojahed M and Yusuff R M 2010 Simple additive weighting approach to personnel selection problem. Int. J. Innov. Manag. Technol. 1: 511

    Google Scholar 

  27. García-Cascales M S and Lamata M T 2012 On rank reversal and TOPSIS method. Math. Comput. Model. 56: 123–132

    Article  MathSciNet  Google Scholar 

  28. Zheng Z, Zhang Y and Lyu M R 2010 CloudRank: a QoS driven component ranking framework for cloud computing. In: Proceedings of the IEEE Symposium on Reliable Distributed Systems

  29. Garg S K, Versteeg S and Buyya R 2012 A framework for ranking of cloud computing services. Future Gener. Comput. Syst. 29: 1012–1023

    Article  Google Scholar 

  30. Tran V X, Tsuji H and Masuda R 2009 A new QoS ontology and its QoS based ranking algorithm for Web services. Simul. Model. Pract. Theory 17: 8

    Article  Google Scholar 

  31. Perinici B and Siadat H 2011 Selection of service adaptation strategies based on fuzzy logic. In: Proceedings of the IEEE World Congress on Services, p. 4

  32. Pernici B and Siadat H 2011 Evaluating web service QoS: a neural fuzzy approach. In: Proceedings of the IEEE International Conference on Service-Oriented Computing and Applications (SOCA’11), USA, p. 2

  33. Cheung R, Cao J, Yao G and Chan A 2006 A fuzzy-based service adaptation middleware for context-aware computing. In: Proceedings of the International Conference on Embedded and Ubiquitous Computing, pp. 580–590

  34. Sengupta A, Mazumdar C and Barik M S 2005 e-Commerce security—a life cycle approach. Sadhana 30(2–3): 119–140

    Article  Google Scholar 

  35. Patil V and Shyamasundar R K 2005 Trust management for e-transactions. Sadhana 30(2–3): 141–158

    Article  MathSciNet  Google Scholar 

  36. Madanmohan T R 2005 Successful e-marketplaces: an institutional perspective. Sadhana 30(2–3): 431–444

    Article  Google Scholar 

  37. Raghavan N S 2005 Data mining in e-commerce: a survey. Sadhana 30(2–3): 275-289

    Article  Google Scholar 

  38. Rajaraman V 2005 Building blocks of e-commerce. Sadhana 30(2–3): 89–117

    Article  Google Scholar 

  39. Annette R and Banu A 2017 Multi Criteria Recommendation Engine for Cloud Render Farm Service. Chennai: B.S.A Crescent Institute of Science & Technology

    Google Scholar 

  40. Annette R and Banu W A 2014 A service broker model for cloud based render farm selection. Int. J. Comput. Appl. 96(24): 11–14

    Google Scholar 

  41. Annette J R, Banu W A and Chandran P S 2015 Rendering-as-a-service: taxonomy and comparison. Procedia Comput. Sci. 50: 276–281

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science Engineering Department, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602 105, India

    J Ruby Annette

  2. Computer Science Engineering Department, B.S.A Crescent Institute of Science and Technology, Chennai, India

    Aisha Banu

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  1. J Ruby Annette
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  2. Aisha Banu
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Correspondence to J Ruby Annette.

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Annette, J.R., Banu, A. Ranking cloud render farm services for a multi criteria recommender system. Sādhanā 44, 7 (2019). https://doi.org/10.1007/s12046-018-0981-0

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  • DOI: https://doi.org/10.1007/s12046-018-0981-0

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