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Updated: Aug 5, 2025

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K8sSim: A Simulation Tool for Kubernetes Schedulers and Its Applications in Scheduling Algorithm Optimization.

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  • 1School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.

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|March 29, 2023
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Summary
This summary is machine-generated.

Selecting the best Kubernetes (K8s) scheduling algorithm for AI workloads is time-consuming. K8sSim, a novel Kubernetes simulator, accurately and rapidly evaluates different scheduling algorithms, accelerating results by over 38x.

Keywords:
KubernetesKubernetes simulatorreal cluster tracesscheduling algorithms

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Area of Science:

  • Cloud computing
  • Distributed systems
  • Resource management

Background:

  • Kubernetes (K8s) is a dominant cloud resource manager.
  • Selecting optimal scheduling algorithms for AI workloads is challenging due to long evaluation times.
  • Existing methods lack efficient simulation capabilities for various scheduling algorithms.

Purpose of the Study:

  • To design and implement K8sSim, a Kubernetes simulator.
  • To accurately evaluate the performance of different scheduling algorithms for generic and AI workloads.
  • To accelerate the performance evaluation process for Kubernetes and Volcano scheduling algorithms.

Main Methods:

  • Developed K8sSim, a Kubernetes simulator incorporating Kubernetes and Volcano scheduling algorithms.
  • Integrated typical scheduling algorithms like GANG_MRP, GANG_LRP, and GANG_BRA.
  • Utilized real cluster traces from Alibaba for evaluation.

Main Results:

  • K8sSim accurately simulates scheduling processes, achieving similar CloseRate to real clusters.
  • The simulator accelerates scheduling time by an average of 38.56x.
  • Performance impacts of different scheduling algorithms can be quickly evaluated.

Conclusions:

  • K8sSim provides an accurate and efficient solution for evaluating Kubernetes scheduling algorithms.
  • The simulator addresses the urgent need for rapid performance assessment of scheduling strategies.
  • K8sSim enables faster selection of optimal algorithms for cloud workloads, especially AI.