Parameter Estimation-Aided Edge Server Selection Mechanism for Edge Task Offloading

Hai Xue, Di Zhang*, Celimuge Wu, Yusheng Ji, Saiqin Long, Cheng Wang, Takuro Sato

*この研究の対応する著者

研究成果: Article査読

抄録

As the extension of cloud computing, edge computing has witnessed remarkable progress in the last few years. In edge computing, task offloading from edge device to edge server is becoming a promising technique that substantially enhances the quality of service for edge devices. Nevertheless, appropriate edge server selection for task offloading remains a challenge if multiple edge servers are selectable nearby. In this article, we propose a parameter estimation-aided edge server selection scheme for task offloading (PEESS) with multiple potential edge servers. The proposed scheme jointly leverages Baum-Welch algorithm and forward algorithm of hidden Markov model. That is, PEESS leverages Baum-Welch algorithm to estimate the parameters of potential edge servers based on their time-sequential incoming task records. Subsequently, the forward algorithm is utilized for the probability calculation of the observations based on the estimated parameters. Eventually, the probability calculation results are sorted in descending order, and the edge device selects the target edge server based on the sequence of sending requests to the available edge servers from optimal to suboptimal. Extensive simulation results demonstrate that PEESS outperforms other conventional and state-of-the-art edge server selection schemes in terms of time consumption and energy consumption.

本文言語English
ページ(範囲)2506-2519
ページ数14
ジャーナルIEEE Transactions on Vehicular Technology
73
2
DOI
出版ステータスPublished - 2024 2月 1

ASJC Scopus subject areas

  • 航空宇宙工学
  • 電子工学および電気工学
  • コンピュータ ネットワークおよび通信
  • 自動車工学

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