ArticleScientific reports2025
Fair-efficient allocation mechanism with meta-types resources in cloud computing.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Fair and efficient resource allocation is a fundamental goal of cloud computing systems. However, diverse user requirements and heterogeneous resource types make it difficult to balance utilization efficiency and user-perceived fairness. To address this challenge, we propose a meta-type-based resource allocation mechanism, GAF-MT, which is based on the principle of asset fairness. GAF-MT introduces meta-types to model structured resource groupings and supports user-specific requirements while reducing fragmentation. We design a scheduling algorithm to find feasible solutions and implement GAF-MT based on GUROBI. Extensive experiments in small-scale and large-scale user environments show that GAF-MT not only ensures fairness, but also significantly improves resource utilization and maintains high performance even under high user loads.
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