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Updated: Jun 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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OOSP: Opportunistic Optimization Scheme for Pod Deployment Enhanced with Multilayered Sensing.

Joo-Young Roh1, Sang-Hoon Choi1, Ki-Woong Park2

  • 1SysCore Lab, Sejong University, Seoul 05006, Republic of Korea.

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Summary
This summary is machine-generated.

Kubernetes scheduling can be inefficient for microservices. A new adaptive pod placement technique optimizes performance by considering service dependencies, reducing response times by 11.5% and improving throughput by 10.04%.

Keywords:
container orchestrationinspectionkubernetesmicroservice architecturesystem optimization

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Area of Science:

  • Cloud Computing
  • Distributed Systems
  • Container Orchestration

Background:

  • Kubernetes is the standard for container orchestration but its default scheduler has limitations.
  • The default scheduler's focus on CPU/memory leads to suboptimal performance and resource waste in complex microservice architectures.
  • Increased inter-service communication latency negatively impacts overall system performance.

Purpose of the Study:

  • To propose an adaptive pod placement optimization technique for Kubernetes.
  • To address the performance and resource efficiency limitations of the default Kubernetes scheduler.
  • To improve application performance and resource utilization in cloud-native environments.

Main Methods:

  • Developed an adaptive pod placement optimization technique using multi-tier inspection.
  • Collected and analyzed multi-tier data, focusing on pod coupling and dependencies.
  • Configured a Kubernetes cluster in a virtualized environment for experimental validation.

Main Results:

  • The proposed method significantly outperformed the default Kubernetes scheduler.
  • Achieved up to an 11.5% reduction in average response time.
  • Increased requests processed per second by up to 10.04%.

Conclusions:

  • The adaptive pod placement technique effectively minimizes inter-pod communication delay and enhances system-wide resource utilization.
  • The method offers a more sophisticated and adaptive scheduling approach compared to traditional methods.
  • This research contributes to optimizing cloud-native environments, with potential for broader application across diverse workloads and cloud platforms.