International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Efficient Task Offloading Decision Based on Task Size Prediction Model and Genetic Algorithm

  • Quan T. Ngo (Department of Electrical and Computer Engineering, University of Ulsan) ;
  • Dat Van Anh Duong (Department of Electrical and Computer Engineering, University of Ulsan) ;
  • Seokhoon Yoon (Department of Electrical and Computer Engineering, University of Ulsan)
  • Received : 2024.05.01
  • Accepted : 2024.05.11
  • Published : 2024.08.31
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Abstract

Mobile edge computing (MEC) plays a crucial role in improving the performance of resource-constrained mobile devices by offloading computation-intensive tasks to nearby edge servers. However, existing methods often neglect the critical consideration of future task requirements when making offloading decisions. In this paper, we propose an innovative approach that addresses this limitation. Our method leverages recurrent neural networks (RNNs) to predict task sizes for future time slots. Incorporating this predictive capability enables more informed offloading decisions that account for upcoming computational demands. We employ genetic algorithms (GAs) to fine-tune fitness functions for current and future time slots to optimize offloading decisions. Our objective is twofold: minimizing total processing time and reducing energy consumption. By considering future task requirements, our approach achieves more efficient resource utilization. We validate our method using a real-world dataset from Google-cluster. Experimental results demonstrate that our proposed approach outperforms baseline methods, highlighting its effectiveness in MEC systems.

Keywords

Acknowledgement

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant 2021R1I1A3051364.

References

  1. Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, "A survey on mobile edge computing: The communication perspective," IEEE Communications Surveys and Tutorials., vol. 19, no. 4, pp. 2322-2358, 4th Quart., 2017. DOI: doi.org/10.1109/COMST.2017.2745201
  2. M. T. Beck, M. Werner, S. Feld, and T. Schimper, "Mobile edge computing: A taxonomy," in Proc. 6th International Conference on Advances in Future Internet, pp. 48-54, 2014.
  3. X. Lyu, H. Tian, P. Zhang, and C. Sengul, "Multi-user joint task offloading and resources optimization in proximate clouds," IEEE Transactions on Vehicular Technology, vol. 66, no. 4, pp. 3435-3447, Apr. 2017. DOI: doi.org/10.1109/TVT.2016.2593486
  4. T. X. Tran and D. Pompili, "Joint task offloading and resource allocation for multi-server mobile-edge computing networks," IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 856-868, Jan. 2019. DOI: doi.org/10.1109/TVT.2018.2881191
  5. L. Tan, Z. Kuang, L. Zhao, and A. Liu, "Energy-Efficient Joint Task Offloading and Resource Allocation in OFDMA-Based Collaborative Edge Computing," IEEE Transactions on Wireless Communications, vol. 21, no. 3, pp. 1960-1972, Mar. 2022. DOI: doi.org/10.1109/TWC.2021.3108641
  6. T. W. Jung, J. Y. Lee and K. D. Jung, "Traffic-based reinforcement learning with neural network algorithm in fog computing environment," International journal of internet, broadcasting and communication: IJIBC, vol. 12, no. 1, pp.144-150,
  7. H. Yuan, G. Tang, X. Li, D. Guo, L. Luo, and X. Luo, "Online dispatching and fair scheduling of edge computing tasks: A learning-based approach," IEEE Internet of Things Journal, 8(19), pp.14985-14998, 2021. DOI: doi.org/10.1109/JIOT.2021.3073034
  8. E. Dahlman, S. Parkvall, and J. Skold, "4G: LTE/LTE-Advanced for Mobile Broadband," New York, NY, USA: Academic, 2013.
  9. M. Mitchell, "An Introduction to Genetic Algorithms," Cambridge, MA, USA: MIT Press, 1996.
  10. M. Tang and V.W. Wong, "Deep reinforcement learning for task offloading in mobile edge computing systems," IEEE Transactions on Mobile Computing, 21(6), pp.1985-1997, 2020. DOI: doi.org/10.1109/TMC.2020.3036871
  11. J. Chen, H. Xing, Z. Xiao, L. Xu, and T. Tao, "A DRL agent for jointly optimizing computation offloading and resource allocation in MEC," IEEE Internet of Things Journal, 8(24), pp.17508-17524, 2021. DOI: doi.org/10.1109/JIOT.2021.3081694