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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Fair-efficient allocation mechanism with meta-types resources in cloud computing.

Fengyue Zhang1, Chunlei Shi2

  • 1School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, 467000, China.

Scientific Reports
|November 5, 2025
PubMed
Summary
This summary is machine-generated.

We developed GAF-MT, a novel resource allocation mechanism for cloud computing. This system ensures fairness and boosts resource utilization, even with diverse user needs and high loads.

Keywords:
Cloud computingFairnessMulti-resource allocation

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Area of Science:

  • Cloud Computing
  • Resource Management
  • Algorithmic Optimization

Background:

  • Balancing resource utilization efficiency and user fairness is challenging in cloud systems due to diverse requirements and heterogeneous resources.
  • Existing methods struggle to effectively manage complex resource landscapes and user demands.

Purpose of the Study:

  • To propose GAF-MT, a meta-type-based resource allocation mechanism.
  • To enhance fairness and efficiency in cloud computing resource allocation.
  • To address challenges posed by diverse user requirements and heterogeneous resource types.

Main Methods:

  • Introduced meta-types for structured resource grouping and modeling user-specific requirements.
  • Developed a scheduling algorithm to find feasible solutions.
  • Implemented the GAF-MT mechanism using GUROBI.

Main Results:

  • GAF-MT effectively ensures fairness among users.
  • Significantly improved overall resource utilization.
  • Maintained high system performance under high user loads.
  • Reduced resource fragmentation.

Conclusions:

  • GAF-MT provides a fair and efficient solution for cloud resource allocation.
  • The meta-type approach effectively handles diverse user needs and heterogeneous resources.
  • GAF-MT demonstrates superior performance and fairness in various experimental environments.