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A Fair and Efficient Resource Allocation Algorithm for Cloud Rendering Jobs
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AI
DOI：10.1109/TSC.2025.3570847.png)
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Abstract
En 中文
Service level agreements (SLAs) formulated by cloud rendering service providers and users are varied, as users may have diverse performance requirements for their own jobs. This leads to a complex issue that cloud resources need to be allocated to rendering jobs in an appropriate and effective manner to satisfy users’ diverse SLAs. To address this issue, in this article, we propose a novel fair and efficient resource allocation algorithm, which aims to maximize the execution efficiency of rendering service applications while satisfying users’ diverse SLAs. First, to satisfy users’ diverse SLAs, we propose a rigorous definition of weighted acceleration ratio fairness, whose guiding principle is that the execution speed of a rendering job should be proportional to its weight determined by users’ SLAs. Then, under the guidance of the proposed principle of acceleration ratio fairness, we formulate a new algorithm to fairly allocate resources to rendering jobs. Lastly, to improve execution efficiency and coordinate efficiency and fairness, we propose a fair and efficient resource allocation algorithm with relaxing fairness in resource competitive and non-competitive situations separately for rendering service applications. With extensive experiments that involve real rendering application workloads, we validate the effectiveness of our algorithms in improving execution efficiency and satisfying users’ diverse SLAs.
Keywords:
Cloud
execution efficiency
fairness
rendering service application
resource allocation
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