Abstract
Cloud computing platforms and rendering farms are driven by Internet computer graphic applications. The priority-based scheduling method is an efficient way to assign computing resources to a cloud computing platform by calculating the ratio of resources based on payments in advance. Typically, the job arrival rates of users on a cloud computing platform are not evenly distributed. Thus, low-paying users can grab more resources by increasing their job arrival rates. The purpose of this paper is to propose a new method that adjusts the resource allocation ratio based on the level of payment within a specified time interval. A system administrator can determine a user’s resource quota’s validity by controlling the length of the time interval. According to our experimental results, our new method effectively allocates resources based on payment level and reduces the impact of different job arrival rates.
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References
Chen, L.P., Leu, F.Y., Kuo, C.C., Lin, T.C., Wang, M.J.: Efficient weighted and balanced resource allocation for high-performance render farms. In: Barolli, L., (ed.) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2022. Lecture Notes in Networks and Systems, vol. 570, pp. 292–300. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-20029-8
Chen, L.P., Wu, I.C., Liang, G.Z.: “Enhancing parallel game-tree searches by using idle resources of a high performance render farm. In: 2015 Conference on Technologies and Applications of Artificial Intelligence (2015). https://doi.org/10.1109/TAAI.2015.7407120
Sheharyar, A., Bouhali, O.: A framework for creating a distributed rendering environment on the compute clusters. Int. J. Adv. Comput. Sci. Appl. 4(6), 117–123 (2013)
Chen, L.P., Wu, I.C., Liang, G.Z.: Enhancing parallel game-tree searches by using idle resources of a high performance render farm. In: 2015 Conference on Technologies and Applications of Artificial Intelligence, pp. 461–466 (2015)
Yao, J., Pan, Z., Zhang, H.: A distributed render farm system for animation production. In: International Conference on Entertainment Computing, vol. 29, pp. 264–269 (2009)
AWS Thinkbox, “Job Scheduling” (2020). https://docs.thinkboxsoftware.com/products/deadline/10.1/1User%20Manual/manual/job-scheduling.html
Schwarzkopf, M., Konwinski, A., Abd-El-Malek, M., Wilkes, J.: Omega: flexible, scalable schedulers for large compute clusters. In: SIGOPS European Conference on Computer Systems, pp. 351–364 (2013)
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Chen, LP., Yei, LF., Chiao, HT. (2025). A Job Scheduling Method for Ensuring Long-Term Workload Fairness in Cloud Platforms. In: Barolli, L. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-76452-3_29
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DOI: https://doi.org/10.1007/978-3-031-76452-3_29
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