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A computational resources scheduling algorithm in edge cloud computing: from the energy efficiency of users’ perspective

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Abstract

Nowadays, research in the field of task offloading not only pays attention to the computing performance of computing nodes, but also pays attention to the impact of energy consumption on computing performance. The current multi-core frequency conversion processors mainly use dynamic voltage frequency scaling (DVFS) technology to reduce processor frequency and energy consumption. The most representative is ARM’s big.LITTLE which is a heterogeneous processor architecture, with large cores having strong performance and small cores being more energy efficient. In this article, we aim to implement energy-saving task offloading based on the big.LITTLE processor. We regard multi-core and multi-edge clouds as multiple computing nodes with different computing capabilities and express the task offloading problem as a computing resource scheduling problem. We modeled the calculation time and energy consumption through a non-cooperative game and proved that the non-cooperative game is an accurate potential game. The theory proves that non-cooperative games have at least one pure strategy Nash equilibrium point (NEP). We designed an edge cloud multi-user computing resource scheduling algorithm based on big.LITTLE processor (MUCRS). The simulation experiment results show that the MUCRS algorithm can obtain a suitable task offloading scheme, and the time and energy cost are lower than several similar algorithms.

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Data Availability Statement

Generate random numbers to check validity; see Sect. 6.1 for details. Therefore, no supporting datasets are available.

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Acknowledgements

The authors thank the editors and anonymous reviewers for their valuable comments and suggestions.

Funding

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants No. 61976243 and No. 61602155, in part by the Scientific and Technological Innovation Team of Colleges and Universities in Henan Province under Grant No. 20IRTSTHN018, and in part by the basic research projects in the University of Henan Province under Grant No. 19zx010.

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Authors and Affiliations

  1. School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China

    Jiaqi Zhang, Ruijuan Zheng, Xuhui Zhao, Junlong Zhu, Junwei Xu & Qingtao Wu

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  1. Jiaqi Zhang
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  2. Ruijuan Zheng
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  3. Xuhui Zhao
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  4. Junlong Zhu
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  5. Junwei Xu
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  6. Qingtao Wu
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Contributions

JZ contributed to writing, theoretical analysis, review and editing, and funding acquisition. RZ helped in methodology, supervision, project administration and funding acquisition. XZ provided resources, validation and software. JX contributed to validation and software. QW helped in conceptualization and review.

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Correspondence to Ruijuan Zheng.

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Zhang, J., Zheng, R., Zhao, X. et al. A computational resources scheduling algorithm in edge cloud computing: from the energy efficiency of users’ perspective. J Supercomput 78, 9355–9376 (2022). https://doi.org/10.1007/s11227-021-04146-z

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