Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Task Scheduling and Resource Management Strategy for Edge Cloud Computing Using Improved Genetic Algorithm

  • Xiuye Yin (School of Computer Science and Technology, Zhoukou Normal University) ;
  • Liyong Chen (School of Network Engineering, Zhoukou Normal University)
  • Received : 2022.01.21
  • Accepted : 2022.06.03
  • Published : 2023.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

Keywords

Acknowledgement

This work is supported by the National Natural Science Foundation of China (No. 61402350, 61103143, U1404620, and U1404622), the Key Scientific and Technological Project of Henan Province (No. 182102310034, 172102310124, and 212102210400), the Key Research Projects of Henan Provincial Department of Education (No. 20A520046).

References

  1. D. Madeo, S. Mazumdar, C. Mocenni, and R. Zingone, "Evolutionary game for task mapping in resource constrained heterogeneous environments," Future Generation Computer Systems, vol. 108, pp. 762-776, 2020. https://doi.org/10.1016/j.future.2020.03.026
  2. E. H. Lee and S. Lee, "Task offloading algorithm for mobile edge computing," Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 2, pp. 310-313, 2021. https://doi.org/10.7840/kics.2021.46.2.310
  3. A. R. Arunarani, D. Manjula, and V. Sugumaran, "Task scheduling techniques in cloud computing: a literature survey," Future Generation Computer Systems, vol. 91, pp. 407-415, 2019. https://doi.org/10.1016/j.future.2018. 09.014
  4. P. P. Hung, M. G. R. Alam, H. Nguyen, T. Quan, and E. N. Huh, "A dynamic scheduling method for collaborated cloud with thick clients," International Arab Journal of Information Technology, vol. 16, no. 4, pp. 633-643, 2019.
  5. G. Lou and Z. Cai, "A cloud computing oriented neural network for resource demands and management scheduling," International Journal of Network Security, vol. 21, no. 3, pp. 477-482, 2019. https://doi.org/10.6633/IJNS.201905_21(3).14
  6. X. Huang, C. Li, H. Chen, and D. An, "Task scheduling in cloud computing using particle swarm optimization with time varying inertia weight strategies," Cluster Computing, vol. 23, pp. 1137-1147, 2020. https://doi.org/10.1007/s10586-019-02983-5
  7. Y. Li and C. Jiang, "Distributed task offloading strategy to low load base stations in mobile edge computing environment," Computer Communications, vol. 164, pp. 240-248, 2020. https://doi.org/10.1016/j.comcom.2020.10.021
  8. S. Luo, X. Chen, Z. Zhou, X. Chen, and W. Wu, "Incentive-aware micro computing cluster formation for cooperative fog computing," IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2643-2657, 2020. https://doi.org/10.1109/TWC.2020.2967371
  9. S. Josilo and G. Dan, "Decentralized algorithm for randomized task allocation in fog computing systems," IEEE/ACM Transactions on Networking, vol. 27, no. 1, pp. 85-97, 2019. https://doi.org/10.1109/TNET.2018.2880874
  10. G. Sakarkar, N. Purohit, N. S. Gour, S. B. Meshram, "A review of computational task offloading approaches in mobile computing," International Journal of Scientific Research in Science, Engineering and Technology, vol. 6, no. 2, pp. 381-387, 2019. https://doi.org/10.32628/IJSRSET
  11. W. Li, S. Cao, K. Hu, J. Cao, and R. Buyya, "Blockchain-enhanced fair task scheduling for cloud-fog-edge coordination environments: model and algorithm," Security and Communication Networks, vol. 2021, article no. 5563312, 2021. https://doi.org/10.1155/2021/5563312
  12. X. Xu, Q. Liu, Y. Luo, K. Peng, X. Zhang, S. Meng, and L. Qi, "A computation offloading method over big data for IoT-enabled cloud-edge computing," Future Generation Computer Systems, vol. 95, pp. 522-533, 2019. https://doi.org/10.1016/j.future.2018.12.055
  13. Z. Zhou, H. Liao, B. Gu, S. Mumtaz, and J. Rodriguez, "Resource sharing and task offloading in IoT fog computing: a contract-learning approach," IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 3, pp. 227-240, 2020. https://doi.org/10.1109/TETCI.2019.2902869
  14. J. Liu, S. Wang, J. Wang, C. Liu, and Y. Yan, "A task oriented computation offloading algorithm for intelligent vehicle network with mobile edge computing," IEEE Access, vol. 7, pp. 180491-180502, 2019. https://doi.org/10.1109/ACCESS.2019.2958883