Enhancing Priority-Based Adaptive Resource Allocation for High-Performance Computing Platforms

High-performance computing platforms accelerate rendering application execution by efficiently distributing workloads across clusters of computing hosts. Priority-based scheduling offers a simple and effective mechanism for computing resource allocation, often aligned with pay-per-use models. Traditional priority calculation methods often overlook inter-user parameters, such as competing user priorities and system scales. This paper presents an enhanced adaptive resource allocation strategy that introduces two normalisation techniques: priority scaling and weight sharing. By balancing fairness and responsiveness, the proposed method allows short jobs to complete earlier and avoids queue congestion, resulting in a more efficient and user-friendly environment for rendering workloads with diverse job types and priority levels. Experimental results show that this adaptive approach significantly reduces waiting times with marginal impact on the completion time of high-priority tasks.