Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Particle Swarm Optimization in Gated Recurrent Unit Neural Network for Efficient Workload and Resource Management

효율적인 워크로드 및 리소스 관리를 위한 게이트 순환 신경망 입자군집 최적화

  • Ullah, Farman (Division of Computer Science and Engineering, Jeonbuk National University) ;
  • Jadhav, Shivani (Division of Computer Science and Engineering, Jeonbuk National University) ;
  • Yoon, Su-Kyung (Division of Computer Science and Engineering, Jeonbuk National University) ;
  • Nah, Jeong Eun (University College, Yonsei University)
  • Received : 2022.08.29
  • Accepted : 2022.09.21
  • Published : 2022.09.30
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Abstract

The fourth industrial revolution, internet of things, and the expansion of online web services have increased an exponential growth and deployment in the number of cloud data centers (CDC). The cloud is emerging as new paradigm for delivering the Internet-based computing services. Due to the dynamic and non-linear workload and availability of the resources is a critical problem for efficient workload and resource management. In this paper, we propose the particle swarm optimization (PSO) based gated recurrent unit (GRU) neural network for efficient prediction the future value of the CPU and memory usage in the cloud data centers. We investigate the hyper-parameters of the GRU for better model to effectively predict the cloud resources. We use the Google Cluster traces to evaluate the aforementioned PSO-GRU prediction. The experimental shows the effectiveness of the proposed algorithm.

Keywords

References

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